Designs doi: 10.3390/designs10030054

Authors: Antoni Lara Albert Cruz Sylvia Andrea Cruz Magnus Carl Fredrik Eriksson Antonio Confalonieri Andreu Sanz

The environmental impact of battery systems is strongly influenced by early design decisions related to materials, structural architecture and assembly strategies. While extensive research addresses battery performance and recycling processes, fewer studies focus on how ecodesign principles can be systematically translated into concrete design solutions at the product level. This article presents an ecodesign strategy applied to the development of a battery enclosure from an industrial design perspective. The proposed approach combines the use of aluminium with high recycled content, a modular enclosure based on extruded profiles adaptable to different battery sizes, a single-material architecture enabled by welded joints, and reversible fastened connections to support assembly, disassembly and repairability. The article discusses how ecodesign criteria such as material efficiency, circularity, modularity and design for assembly and disassembly (DfA/DfD) can be embedded into a coherent battery enclosure concept, while also addressing the main limitations and trade-offs of the proposed strategy.

Designs doi: 10.3390/designs10030053

Authors: Antreas Kantaros Alexandra Tsatsou Zoe Kanetaki Theodore Ganetsos Constantinos Stergiou Michail Papoutsidakis Evangelos Pallis

This work examines the contribution of additive manufacturing as an enabling technology in the design and development of smart and sustainable construction systems, with particular emphasis on nature-based solutions. While the existing literature has devoted considerable attention to the material properties of additive manufacturing, much less emphasis has been placed on its role in design processes, prototyping, and decision-making in construction and urban systems. To address this gap, this study presents a comprehensive bibliometric analysis of the intersection between smart city frameworks and 3D printing technologies, utilizing a dataset of 103 peer-reviewed publications retrieved from the Scopus database. Using keyword co-occurrence analysis and network mapping through VOSviewer, this study identifies dominant thematic structures, core research hubs, and evolving trends within the field. Complementing this bibliometric analysis with qualitative synthesis, it also reveals a significant convergence of digital design, smart cities, and sustainability strategies. This work further highlights the contribution of additive manufacturing to design processes through rapid prototyping, customization, and the exploration of design alternatives. Rather than framing additive manufacturing as a replacement for conventional design practices, this study positions it as a complementary design capability that can enhance the design process, while also acknowledging important challenges related to scaling, regulation, and integration into construction workflows. This review concludes by outlining future research directions for strengthening the design-oriented integration of additive manufacturing within smart construction systems.

Designs doi: 10.3390/designs10030052

Authors: Aziz Ahmed

The construction industry currently operates at a critical threshold [...]

Designs doi: 10.3390/designs10030051

Authors: Susan Murillo Alexander Aguila Téllez

This paper presents a structured planning framework for the coordinated integration of photovoltaic (PV) systems and capacitor banks (CBs) in radial distribution networks to improve steady-state voltage regulation and reduce active-power losses. The proposed methodology combines deterministic power-flow assessment, index-based candidate screening, and constrained joint placement and sizing using the Grey Wolf Optimizer (GWO) with an embedded CAPEX proxy. Compared with PV-only integration, the coordinated PV–CB strategy provides a more effective improvement in steady-state electrical performance, particularly in terms of slack-bus power factor and voltage regulation. In addition, relative to fixed coordinated PV–CB scenarios, the GWO-based formulation yields more balanced technical–economic solutions by improving power factor and voltage conditions while avoiding unnecessary overdimensioning of installed capacity. On the IEEE 15-bus system, the optimized configuration achieves a 45.9% reduction in active-power losses, improves the slack-bus power factor to 0.947, and reduces the average voltage deviation to 2.57%, with convergence reached in approximately 16 iterations. On the IEEE 34-bus system, the optimized solution yields a 49.8% loss reduction, increases the slack-bus power factor to 0.955, and reduces the average voltage deviation to 2.39%, with convergence reached in approximately 133 iterations. Using an energy price of 8.14 ctUSD/kWh, the corresponding annual loss–cost savings are approximately 19,975 USD and 78,475 USD for the IEEE 15- and 34-bus systems, respectively. The results demonstrate that the proposed GWO-based coordinated planning approach can achieve electrically effective and economically feasible solutions through the combined provision of local active-power injection and reactive-power compensation in radial distribution networks under steady-state operating conditions.

Designs doi: 10.3390/designs10030050

Authors: Christina Kostopoulou Vasileios D. Sagias Paraskevi Zacharia Antreas Kantaros Constantinos Stergiou

Sustainability is an increasingly important objective in design and engineering, yet the environmental implications of advanced computational design methods remain insufficiently quantified. This study examines the contribution of topology optimization to sustainable product development when applied exclusively to a product’s internal structure, while preserving external geometry, mechanical performance, and design intent. The furniture sector was selected as a representative case due to its significant environmental footprint and the strong role of aesthetic requirements within the design methodology. A gate-to-gate Life Cycle Assessment was performed to compare a conventionally designed stool with an internally optimized counterpart, both developed under the same design constraints and manufactured via Fused Deposition Modeling using Carbon Fiber-reinforced PETG (CF-PETG). The results indicate that computational strategies can reduce material waste by 57.8% to 90% compared to traditional subtractive methods. However, these benefits may be partially offset by increased energy demand during additive manufacturing due to geometric complexity. An additional comparative assessment involving generative design demonstrates that alternative computational strategies can achieve more balanced trade-offs between material efficiency and manufacturing energy, supporting sustainability while respecting design methodology constraints.

Designs doi: 10.3390/designs10030049

Authors: Davut Akdaş

It is commonly noted in the literature that reducing mass and moment of inertia lowers the requirements for powerful electromechanical hardware and improves the overall energy efficiency of legged robots. For this reason, the humanoid robot RB2, the second of its kind at Balikesir University, has been developed iteratively. The motivation for this research is to design a lightweight, low-power humanoid robot to gain physical insight into the viability of using Delrin and 3D-printed ABS parts in its support structure and to enhance the robot’s efficiency in terms of weight and, as a result, power requirements. The number of degrees of freedom and the order of the joint motions of the planes are optimised to reduce moments of inertia and increase the range of motion of the robot’s legs. Additionally, the mechanical structure incorporates design features to facilitate assembly and maintenance. The newer robot’s weight is reduced to 25% of our first humanoid robot’s, while maintaining the same joint range of motion.

Designs doi: 10.3390/designs10030048

Authors: Xumiao Lin Joana Correia Miguel Marques Ana Foles José Silva Teresa Batista Carmen Luisa Vásquez Stanescu Lucas Marinho Fernando Barros

The global transition toward a low-carbon economy has accelerated the adoption of renewable energy sources. This paper presents the development of a model-based electronic Decision Support System for renewable energy planning, incorporating energy storage and carbon footprint assessment. The tool assists stakeholders in the preliminary evaluation of local wind and solar resources. To validate the model’s credibility, a comparative analysis was conducted, using the Port of Sines, Portugal, as an industrial case study. Solar energy estimations were benchmarked against PVSyst, while wind energy simulations were compared with an INEGI technical study. Results indicate consistency in solar estimates, with maximum deviations of 14% for fixed installations and 13% for vertical barriers, primarily due to terrain orography that was not yet integrated into the algorithm. Regarding wind energy, deviations reached 19% to 25%, largely resulting from the use of aggregated mean values in the reference data and generic turbine models. Overall, this work contributes to energy engineering by formalizing a validated workflow that facilitates early-stage sizing and strategic investment decisions under conditions of data scarcity. The tool proves effective for rapid screening of promising investment options while maintaining a balance between computational complexity and practical usability.

Designs doi: 10.3390/designs10030047

Authors: Mahmoud Dhimish Peter Behrensdorff Poulsen

This review paper presents a system-level engineering design perspective on end-of-life (EoL) photovoltaic (PV) recycling, addressing a critical gap in the literature that is predominantly focused on material and process-level analyses. A unified framework is developed to evaluate mechanical, thermal, chemical, and emerging laser-based technologies through the lenses of system architecture, process control, and infrastructure integration. The study introduces design-oriented concepts, including optimal processing windows, modular system configurations, and multi-layer control frameworks, to support decision-making in scalable PV recycling systems. Particular emphasis is placed on laser-based recycling (e.g., femtosecond laser technology), which enables non-thermal, high-precision, and interface-selective material separation, representing a paradigm shift towards intelligent and adaptive recycling infrastructures. The paper also highlights the transition from conventional bulk PV processing to precision-controlled, artificial intelligence (AI)-enabled systems, and outlines future research and industrial pathways required to realize sustainable, high-efficiency PV recycling within a circular economy.

Designs doi: 10.3390/designs10030046

Authors: Arthur Erdman John Titus Mahmud Suhaimi Ibrahim Sean Mather

In a complete kinematic synthesis process, a designer must select a planar linkage topology that is well suited to their problem situation. This involves weighing a set of competing priorities. For example, is it better to choose a simple topology like a four-bar mechanism that will be cheaper to produce, or a complex topology like an eight-bar mechanism that can produce intricate motions but will also be more expensive and more difficult to synthesize? The process of selecting the topology is broadly known as type synthesis, or sometimes structure synthesis, and has been studied in the past. However, past works on planar linkage type synthesis have overemphasized isomorphism detection, identifying the complete set of unique topologies up to a certain number of links, while the central problem of choosing the ideal topology has often been overlooked. In this work, a general procedure for forming basic kinematic chains (BKCs), a simplified topological representation, is presented. Then, a set of rules and design principles is provided that can help a designer narrow the infinite possible BKC options down to a manageable set. A few practical examples are provided to demonstrate the concepts and show that the procedure is effective. A literature review is also provided that examines past works, as well as introducing alternative approaches, such as simultaneous algorithmic methods and spatial methods.

Designs doi: 10.3390/designs10030045

Authors: Roberta Cromi Francesca Berti Matteo Gavazzoni Luigi La Barbera Dalila Di Palma Sara Maggioni Jacopo Menini Massimo Franceschini Stefano Foletti Tomaso Villa

Bone resorption secondary to stress shielding is a leading cause of hip implant failure, primarily due to the stiffness mismatch between the femur and the prosthesis. Although anatomical stem designs generally provide improved load transfer, Dorr type C femurs often require straight stems to ensure adequate primary stability. This work presents a systematic approach to designing a straight, additively manufactured porous titanium hip stem aimed at minimizing stress shielding. The lattice architecture is customized to replicate the mechanical properties of bone based on patient-specific femoral CT scans. The performance of the resulting porous implant is numerically assessed under simplified physiological gait loading conditions. The implant behavior is evaluated through a homogenization strategy to model the lattice structure, significantly reducing the computational effort and making the methodology easily replicable. Compared to its full counterpart, the porous design achieves a significant reduction in predicted bone loss, suggesting that the proposed framework is a promising proof of concept for patient-specific implants. While further experimental validation and larger cohort studies are required, these findings highlight the potential of mechanically tunable porous structures to mitigate the stress shielding phenomenon in anatomical conditions such as Dorr type C femurs, which require straight stems.

Designs doi: 10.3390/designs10020044

Authors: Yan Ma Zehui Huang Hongbin Tang Jianjiao Deng Jingchun Wang Shibin Wang Zhiguo Zhang Zhenjiang Wu

Due to high specific energy absorption, composite/metal hybrid multi-cell thin-walled tubes hold significant potential in the field of automotive passive safety. However, the material coupling effect enhancing SEA often elevated the initial peak crushing force, reducing crushing force efficiency and compromising occupant protection. To balance SEA and CFE, trigger holes were introduced as an induced deformation mechanism for hybrid tubes to reduce IPCF while preserving SEA, with the optimized perforated configuration yielding higher CFE than the non-perforated counterpart. A high-fidelity finite element model of the hybrid tube was developed and experimentally validated, and the influences of induced structural parameters on SEA and CFE were investigated. Given the strong nonlinear coupling between trigger parameters and crashworthiness, a multilayer perceptron surrogate model was constructed using 200 optimal Latin hypercube sampling samples (20 for validation). A Q-learning enhanced particle swarm optimization (QL-PSO) algorithm was adopted for optimization, with reinforcement learning dynamically adjusting PSO parameters to balance global exploration and local exploitation. Finite element simulations validated that the proposed method achieved a favorable SEA-CFE trade-off, with SEA and CFE improved by 12.02% and 16.39% respectively, outperforming reported configurations. Compared with standard PSO, QL-PSO exhibited superior search efficiency and inverse mapping accuracy, with 22% higher optimization efficiency and full compliance with inverse design performance targets. This study provided valuable guidance for the design of thin-walled energy-absorbing structures in multi-material vehicle bodies.

Designs doi: 10.3390/designs10020043

Authors: Mahmoud Donia Emad Elbeltagi Ahmed Elhakeem Hossam Wefki

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry.

Designs doi: 10.3390/designs10020042

Authors: Gianfranco Parlangeli

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, such as unexpected inertial stress and strain on the mechanical structure, passenger discomfort, and unsafe and unpredictable deviation of the real trajectory with respect to the reference planned one. Oppositely, smooth and feasible trajectories are often desired in real-time navigation for nonholonomic mobile robots where the surrounding environment can have a dynamic and complex shape with obstacles. In this paper, we propose a novel technique for the generation of smooth, collision-free, and near time-optimal paths for nonholonomic mobile robots. The proposed method exploits the features of a set of tunable bump functions, with the goal of pursuing smooth reference curves with tunable features (such as curvature, or jerk) yet seeking a reasonable length minimality, thus combining the advantages of the two most adopted techniques, namely Bezier interpolation and Dubins curves. After a thorough description of the analytical methods, the paper is primarily concerned with the design and tuning methods of the path-planning algorithm. Both a graphical method and numerical investigations and examples are performed to fully exploit the algorithm potentialities and to show the efficiency of the proposed strategy.

Designs doi: 10.3390/designs10020041

Authors: Tao Yang Hanning Chen

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing operations are fixed, while only the rescheduling window composed of waiting operations and urgent-order operations is re-optimized. On this basis, two rescheduling strategies, namely complete rescheduling and deferred rescheduling, are designed and compared. Second, to improve the solution capability in complex dynamic environments, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) with a three-layer encoding scheme is proposed. The algorithm incorporates hybrid initialization, tabu-guided crossover, simulated annealing mutation, and critical-path-based variable neighborhood search. Experimental results show that the proposed method performs well in energy consumption optimization and tool wear control, while effectively improving the diversity and distribution quality of the Pareto solution set. Further analysis indicates that deferred rescheduling generally outperforms complete rescheduling, while the original-orders-first and urgents-first strategies exhibit different strengths in convergence, solution quality, and objective optimization. The proposed study provides an effective modeling and optimization framework for multi-objective green rescheduling problems and offers theoretical support for production scheduling decisions that need to balance production efficiency, energy saving, and tool-related cost control in practical manufacturing systems.

Designs doi: 10.3390/designs10020040

Authors: Luis Mayo-Alvarez Jorge Córdova-Maraví Diego García-Gómez Iván Paredes-Julca

Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a limited systematic understanding of how emerging technologies support engineering design practices and influence the implementation and performance of Lean Construction in diverse civil engineering scenarios. This study presents a systematic literature review of 70 peer-reviewed articles published between 2019 and 2025, following the PRISMA 2020 guidelines. The selected studies were examined using a structured classification framework consisting of three analytical categories: Technologies and Tools, Construction Methods and Sustainability, and Production Philosophies and Management. From an engineering design perspective, this framework allows the identification of technological trends, design-support tools, and management strategies that influence the planning, modeling, and optimization of construction processes. The results show that digital technologies, such as Building Information Modeling (BIM), automation systems, Artificial Intelligence, and Industry 4.0 tools, play a significant role in supporting engineering design activities by improving project visualization, coordination, and decision-making during the design and planning stages. These technologies contribute to more integrated design processes aligned with Lean Construction principles. At the same time, the analysis reveals that the adoption of Lean Construction technologies varies depending on project characteristics, levels of digital maturity, and regional industry conditions. The main barriers identified in the literature include interoperability limitations, insufficient workforce training, and organizational resistance to technological change. Overall, the review provides a structured synthesis of recent research trends and highlights the technological and managerial factors that influence the successful integration of Lean Construction with engineering design practices in civil engineering. The findings contribute to bridging the gap between technological innovation, design methodologies, and Lean Construction implementation, offering insights for both researchers and practitioners seeking to improve efficiency, sustainability, and design performance in construction projects.

Designs doi: 10.3390/designs10020039

Authors: Aiken H. Ortega-Heredia Oscar E. Coronado-Hernández Vicente S. Fuertes-Miquel

Filling operations in pressurised pipeline systems can trap air pockets, generating hazardous transient overpressures that threaten structural integrity and operational reliability. Evaluating these events using conventional hydraulic models can be computationally intensive, limiting design-space exploration of operational scenarios. This study presents a simulation-driven design-screening framework based on Monte Carlo simulation to evaluate and predict peak absolute pressures during pipeline start-up and filling operations. A total of 2000 transient scenarios were generated for a representative 1100 m pipeline system by varying key geometric and operational parameters, including diameter, friction factor, column lengths, slopes, and reservoir elevation. Twenty-eight machine learning regression models were trained to develop a physics-informed surrogate model capable of rapidly predicting pressure peaks within the defined parameter domain. The trilayered neural network achieved the highest predictive accuracy, with robust validation (RMSE = 10.95 m, R2 = 0.99) and test performance (RMSE = 9.78 m, R2 = 0.99). Screening results showed that nominal pressure thresholds of 61.18 m and 407.89 m were exceeded in 97.53% and 4.89% of the retained peak-forming scenarios (n = 1746), respectively. The proposed framework provides an efficient and reproducible surrogate-based design-screening approach for transient overpressure risk within the evaluated hydraulic domain.

Designs doi: 10.3390/designs10020038

Authors: Antreas Kantaros George Sakellaropoulos Theodore Ganetsos Nikolaos Laskaris

Digital fabrication technologies increasingly enable designers and researchers to reinterpret historical design languages through contemporary production methods. Within this context, desktop 3D printing offers an accessible yet constrained medium for translating stylistically rich interior design objects into tangible form. This study examines how distinct twentieth-century interior design movements—Art Deco, Bauhaus, and Mid-century Modern—are mediated through desktop additive manufacturing, focusing on the preservation of formal identity rather than manufacturing performance. Representative interior objects were digitally reconstructed from archival and reference material and fabricated under standardized desktop 3D printing conditions. The investigation adopts a style-oriented evaluation framework that examines silhouette continuity, characteristic geometric features, ornamental legibility, and structural–stylistic coherence. To support comparative interpretation, a Style Preservation Index (SPI) is introduced as a structured design evaluation tool that makes stylistic assessment explicit and repeatable without reducing it to purely geometric metrics. The results demonstrate that stylistic legibility is preserved to differing degrees depending on the formal vocabulary of each design movement, with minimal and geometrically rational styles exhibiting higher compatibility with layer-based fabrication than ornamentally dense or materially expressive designs. Rather than framing these differences as technological limitations, the study interprets them as insights into how design languages interact with fabrication constraints. By positioning desktop additive manufacturing as a medium of design translation rather than replication, this work contributes a reproducible framework for design research, heritage interpretation, and education, offering a structured approach for exploring how historical styles can be re-engaged through contemporary digital fabrication.

Designs doi: 10.3390/designs10020037

Authors: Zhenfeng Han Deming She Jun Liu

Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated deep learning framework is proposed that employs a unidirectional, multi-layer LSTM network for end-to-end prediction of structural responses directly from ground motions. The proposed model features two innovations. First, its multi-output capability enables simultaneous prediction of complete response time histories and peak values for key engineering demand parameters—bending moment, curvature, and pile cap displacement. Second, the network incorporates sliding time windows and residual connections to capture complex nonlinear soil–structure interaction. These predictions are integrated into a probabilistic seismic demand model to generate fragility curves. The framework is validated using a high-fidelity OpenSees model of a real bridge PGF subjected to 1000 ground motions. Results demonstrate the model’s excellent predictive accuracy: for peak bending moment, the mean predicted-to-actual ratio ranges from 0.97 to 1.03, with standard deviation below 0.12; the derived fragility curves show excellent agreement with benchmarks, achieving an average R2 of 0.985 across four damage states. More importantly, the framework reduces the time for a complete fragility assessment (200 incremental dynamic analyses) from approximately 12 h to about 1 s—a 40,000× speed-up—making data-driven rapid and large-scale seismic risk assessment a reality. The proposed framework provides engineers with a practical design tool for rapidly evaluating alternative foundation configurations and informing seismic design decisions, thereby integrating advanced data-driven methods directly into the engineering design workflow.

Designs doi: 10.3390/designs10020036

Authors: Margarida Graça Nuno Martins Miguel Terroso

This study is part of the FAIST research project—Agile, Intelligent, Sustainable and Technological Factory, coordinated by the Footwear Technology Centre of Portugal (CTCP), which aims to develop an innovative production process through the creation of a sustainable footwear model fully adapted to the user’s foot anatomy and personalized according to individual aesthetic preferences. Within this context, the need emerged to design an online platform with an interface capable of effectively addressing user needs throughout all stages of the personalization process, from the foot scanning to the aesthetic personalization of the model, while ensuring an efficient, intuitive, and pleasant navigation experience. Thus, this work aims to demonstrate how the design process of a footwear personalization platform, across its different phases, can contribute to the revitalization of the Portuguese footwear industry, as well as to describe its effectiveness, with the goal of being potentially adapted and implemented in similar contexts. The adopted methodology was based on the principles of Design Thinking, an approach centered on user needs. The development of the platform involved the creation of personas, the definition of the information architecture, the development of wireframes and workflows, and the execution of usability tests using the System Usability Scale (SUS). The results demonstrate a high success rate, validating the proposed solution with users and confirming the suitability of the applied methodologies.

Designs doi: 10.3390/designs10020035

Authors: Alexandra Leonova Matteo Russo Cuauhtemoc Morales-Cruz Marco Ceccarelli

This paper presents the mechanical design and experimental evaluation of the biped unit of LARMbot V.3—a compact low-cost humanoid robot for educational and research purposes. The biped unit features a modular architecture with a parallel leg mechanism for bipedal locomotion. The mechanical configuration of the unit is introduced, highlighting improvements on previous versions in terms of compactness and operating efficiency. A functional prototype is developed and described with detailed specifications of its actuation and transmission systems. To evaluate the performance of the proposed design, experimental tests were conducted both in-air and on-ground, demonstrating the robot’s ability to perform repeatable walking cycles. The results confirm the feasibility of the design and its potential as a platform for further developments in humanoid locomotion.

Designs doi: 10.3390/designs10020034

Authors: Askar Abdykadyrov Amandyk Tuleshov Nurzhigit Smailov Zhandos Dosbayev Sunggat Marxuly Yerlan Sarsenbayev Beket Muratbekuly Nurlan Kystaubayev

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·106 V/m, the ion concentration stabilizes in the range of 1013–1015 m−3, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators.

Designs doi: 10.3390/designs10020033

Authors: Asad Shafique Georgii Kolev Oleg Bayazitov Varvara Sheptunova Ekaterina Kopets

This paper presents the design and multi-domain evaluation of three chaos-based excitation strategies for brushless DC (BLDC) motor drives implemented using Chua circuit-generated deterministic chaotic signals injected at three distinct control points: the PWM duty cycle, the commutation sequence, and the current feedback loop. A systematic design methodology is established for each injection architecture, including signal normalization, amplitude parameterization, and injection point characterization, evaluated across the electromagnetic, thermal, mechanical, and acoustic domains through MATLAB (R2024a) simulation and physical test stand validation. PWM injection produces controlled spectral dispersion with 5–7% speed reduction and a 10–15 dB SNR decrease, making it the recommended design choice for acoustic signature masking in stealth UAV applications. Commutation injection achieves severe system destabilization with speed reduction exceeding 56% and SNR losses greater than 30 dB, establishing it as a design tool for accelerated stress testing and fault emulation. Current feedback injection delivers a balanced excitation profile with 12–20% efficiency loss and 16–30% SNR reduction, making it suitable as a design method for online parameter identification and adaptive control development. This study establishes the first multi-domain comparative design framework for application-specific selection of chaos excitation strategies in BLDC drives, supported by nonparametric statistical validation and experimental acoustic confirmation, providing drive engineers with quantitative selection criteria across four physical domains.

Designs doi: 10.3390/designs10020032

Authors: Oğuz Kağan Keleş Mustafa Bağrıyanık

Electrical power systems have taken on a significant role in aviation, becoming critical to solution plans driven by environmental concerns. Therefore, concepts focusing on energy efficiency and increased dependence on electrical power have gained great popularity. As electrical energy begins to replace traditional hydraulic, mechanical, and pneumatic systems in conventional aircraft, improvements in system design have become inevitable. Optimization studies are conducted to achieve weight reduction, a crucial design parameter for aircraft electrical power systems. A noteworthy target for these efforts is power cables, given their substantial contribution to the overall weight of the system. Reducing the weight of cables between distribution units and loads is related to the Load Allocation Problem (LAP). The solution to the LAP, which involves determining which loads should be powered by which distribution units, results in a significant decrease in cable weight. In this study, a method named Electrical Power System Planning Strategy (E2P2S) was developed to solve the LAP for aircraft electrical power systems, aiming for weight reduction under certain constraints. The developed method was tested using CPLEX 22.1.0 software, and a case study was conducted using the F-16 platform as a reference. The results demonstrate that the impact of weight on aircraft electrical power systems is substantially affected by the optimization, highlighting the importance of this work for future aircraft concepts that will increasingly rely on electrical energy.

Designs doi: 10.3390/designs10020031

Authors: Xiaoyu Huang Jiazhe Tang Elizabeth Rendon-Morales Rodrigo Aviles-Espinosa

In this paper we present the design of a tolerance analysis-based closed-loop system and a compensation framework applied to high-precision linear Delta robots. It considers the modelling of static and dynamic errors propagation arising from the structural tolerances and the end-effector’s positioning. This approach is combined with a closed-loop control system implemented using high-resolution optical encoders. The model is applied to the ROMI robot, a high-precision experimental Delta robot designed for microsurgical applications. Our simulation results reveal a theoretical home position error (the centre of the robot’s platform) of 1.9 mm, which is effectively compensated through kinematic calibration and a tolerance analysis-based closed-loop system. The proposed framework is evaluated experimentally through proof-of-concept experiments mimicking a microsurgical resection task conducted on a human peripheral nerve sample. The results from executing micrometre scale parallelogram and circular trajectories showed error reduction rates of 92.3% and 51.2% respectively, after five trajectory iterations. These findings confirm that manufacturing-induced errors can be consistently compensated using the proposed methodology, thus eliminating the need for ultra-high-precision machined components. This work establishes a practical and scalable pathway for designing more affordable high-precision robotic systems suitable for microsurgical and other high-precision applications.

Designs doi: 10.3390/designs10020030

Authors: Waseem Ahmed Qingjin Peng

Augmented Reality (AR) and Large Language Models (LLMs) have made significant advances across many fields, opening new possibilities, particularly in complex machine operations. In complex operations, non-expert users often struggle to perform high-precision tasks and require constant supervision to execute tasks correctly. This paper proposes a novel AR-MLLM-based training system that integrates AR, multimodal large language models (MLLMs), and prompt engineering to interpret real-time machine feedback and user activity. It converts extensive technical text into structured, step-by-step commands. The system uses a prompt structure developed through an iterative design method and refined across multiple machine operation scenarios, enabling ChatGPT to generate task-specific contextual digital overlays directly on the physical machines. A case study with participants was conducted to assess the effectiveness and usability of the AR-MLLM system in Coordinate Measuring Machine (CMM) operation training. The experimental results demonstrate high accuracy in task recognition and feature measurement activity. The data further show reduced time and user workload during task execution with the proposed AR-MLLM system. The proposed system not only provides real-time guidance and enhances efficiency in CMM operation training but also demonstrates the potential of the AR-MLLM design framework for broader industrial applications.

Designs doi: 10.3390/designs10020029

Authors: Seoyeon Kim Yoonjung Jang Heejin Kim Junhyung Kim Sungbeen Lee HyunJune Yim Dokshin Lim

Infertility treatment often requires patients to self-administer hormonal injections, creating significant physical, logistical, and psychological burdens. While medical technologies have improved pharmacological efficacy and safety, design aspects addressing usability, portability, and emotional distress remain underexplored. This study presents Blloom, a compact self-injection device that integrates ergonomic, thermal, and emotional considerations designed through an interdisciplinary design-thinking framework. This study identified critical user needs related to self-injection anxiety, medication refrigeration, and treatment-related stigma through in-depth, multi-method qualitative design research. The resulting prototype is characterized by one-handed operation, concealed needle delivery, and built-in passive cooling (2–8 °C for up to 8 h). Formative evaluations with patients and clinicians confirmed its improved usability, emotional comfort, and contextual compatibility. At this prototypical stage, medication- and container-specific compatibility, as well as long-term reliability, require further bench testing and clinical validation. Process analysis further revealed how designer–engineer collaboration evolved from empathic exploration to implementation-driven convergence. The findings demonstrate how human-centered design can mitigate the multidimensional burdens of infertility treatment and provide a replicable framework for interdisciplinary innovation in self-managed healthcare devices.

Designs doi: 10.3390/designs10020028

Authors: Hongting He Zunyue Liu Fei Xiao

With the growing demand for personalized design, residential layout generation has become a key research area in architecture and artificial intelligence. This paper presents a novel method for generating personalized layouts using an improved Conditional Variational Autoencoder (HCVEA). This method introduces conditional variables to control key features such as room count, functional zoning, and spatial distribution, while enhancing the latent space structure to improve both diversity and controllability. By incorporating user preferences as conditional inputs and integrating reinforcement learning with the autoencoder-based architecture, the layout generation process is optimized for more accurate and efficient results. A novel Connectionist Temporal Classification Attention (CTC-Attention) decoder is introduced to improve contextual semantic understanding. The Asynchronous Advantage Actor–Critic (A3C) reinforcement learning algorithm is employed to enhance training efficiency. Experimental results averaged over three independent runs show that HCVEA achieves an FZMR of 90.7% and an RAR of 92.3% with low standard deviation, outperforming baseline models. It also maintains a constraint compliance rate of 88.6% and adaptability to different room configurations.

Designs doi: 10.3390/designs10020027

Authors: Huaxing Zhai Shuxun Li Yu Zhang Wei Li Lingxia Yang

As a critical component of the hydrogen supply system for fuel cells in hydrogen-powered unmanned aerial vehicles (UAVs), the dynamic performance of the two-stage hydrogen pressure-reducing valve (PRV) directly influences the stability and safety of the fuel cell system. To address the insufficient output pressure control accuracy of existing hydrogen PRVs under a 70 MPa inlet pressure, this study designs a compact, fast-response, and high-precision two-stage hydrogen PRV. The flow coefficients of the valve orifices at each stage are obtained through Computational Fluid Dynamics (CFD) simulations, based on which a multi-physics coupled system dynamics model of the two-stage hydrogen PRV is derived. Using this multi-physics coupled dynamics model, a dynamic characteristic simulation model is established in MATLAB/Simulink. Numerical simulations performed with this model reveal the influence of different structural parameters on the dynamic characteristics of the first-stage and second-stage PRVs. The results provide theoretical and methodological references for the structural design and efficient optimization of two-stage hydrogen PRVs under high-pressure differential conditions, offering important guidance for improving the safety and stability of fuel cell hydrogen supply systems.

Designs doi: 10.3390/designs10020026

Authors: Yuxin Song Hanning Chen

In recent years, multi-UAV systems have demonstrated broad applications in both security and civilian domains, where cooperative encirclement has emerged as a key research focus. However, existing work predominantly addresses single-target scenarios with homogeneous UAVs using passive tracking strategies, which are inadequate for handling highly maneuverable targets. To overcome these limitations, this paper proposes an active interception decision framework integrating LSTM networks with an off-policy independent actor–critic framework employing a PPO-style clipped surrogate objective, referred to as LIPPO. It aims to address the complex problem of heterogeneous UAV swarms encircling multiple continuously learning targets. The framework employs an LSTM module for real-time trajectory prediction and uses the predicted future positions as interception points, shifting the paradigm from passive tracking to proactive interception. At the decision level, LIPPO adopts a hybrid architecture where each UAV acts as an independent learner, while a shared experience pool enables efficient knowledge transfer across the swarm. Comprehensive simulations demonstrate LIPPO’s superiority. In complex scenarios, it achieves an encirclement success rate up to 10 percentage points higher than non-predictive baselines and reduces energy consumption by nearly 28% compared to centralized training multi-agent reinforcement learning algorithms. These results confirm that LIPPO’s active interception is both effective and efficient.

Designs doi: 10.3390/designs10020025

Authors: Li-Jia Peng Zhuo Yang Tian-Le Jin Xu-Yang Cao

At present, global climate change is intensifying and extreme natural disasters are frequent [...]

Designs doi: 10.3390/designs10020024

Authors: Muhannad Ahmed Obeidi

Advanced manufacturing technologies are increasingly shaping how engineering systems are designed, optimised, and realised [...]

Designs doi: 10.3390/designs10010023

Authors: Liquan Yang Kun Zhao Xiaojun Wang Qingqing Lü Xuandong Wu Gaowei Tian Qun Li Guangxi Li

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m3/s and 0.6 m3/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems.

Designs doi: 10.3390/designs10010022

Authors: Elkin I. Gutierrez-Velasquez Hector Parra-Peñuela Jairo Cortes-Lizarazo

The Support System with Vertical Retractable Mechanism (SSVRS) is an advancement in telescopic technology that replaces continuous threaded or fluid-dependent interfaces with an internal stepped mechanism based on geometric mechanical interference. This coaxial design uses an integrated pin that engages with discrete grooves, enabling rapid height adjustments and positioning speeds that are significantly faster than those of traditional mechanisms. Unlike friction-based systems that are prone to slipping under dynamic loads, the SSVRS provides millimeter-level precision and exceptional stability, even in vibrational environments. The SSVRS’s versatility stems from its parametric modular design, which scales from lightweight domestic fixtures to heavy-duty industrial machinery by customizing material selection—ranging from high-strength steel to glass fiber-reinforced nylon—and slot configuration. Specifically, vertical slot arrangements facilitate rapid movement, and spiral geometries allow for high-precision alignment. Furthermore, the SSVRS optimizes long-term operational efficiency and sustainability through low maintenance requirements, minimal moving parts, and the use of recyclable materials. By combining high-speed positioning, robust structural integrity, and adaptive modularity, the SSVRS provides a high-performance, concrete alternative to current mainstream linear modules and traditional support structures.

Designs doi: 10.3390/designs10010021

Authors: Qiang Liu Xi Zheng Qiuhan Zhang Yongjian Bian Zuqing Yu

The hanging net shielding system, employing a suspended cage-type enclosed structure to restrict the high-voltage transmission wire, has seen increasingly widespread application in transmission line crossing construction. However, the lack of a comprehensive dynamic analysis methodology has limited the standardization of its design and usage. In this investigation, a systematical dynamic modeling and analysis procedure of the hanging net shielding system is proposed based on the absolute nodal coordinate formulation (ANCF). The carrier cable, slings and transmission wire are discretized by the ANCF cable element. The spatial flexible beam–beam contact model and the assumption of a single contact area are adopted to perform the contact searching between the transmission wire and the horizontal pulley. The system dynamics analysis equation is assembled and solved by generalized alpha method. A full-scale model is simulated for the transmission wire impact condition and the variation history of the tension in carrier cable and the sling cable are given. The peak value of the tension in carrier cable could be 110 kN, while the largest tension in sling cable is 9 kN. Results could help to ensure construction safety, shorten the design cycle of the protection system and reduce the development cost at the same time.

Designs doi: 10.3390/designs10010020

Authors: Xunan Ding Juheng Wang Chenchen Han Xiao Chen Jingang Wang

Accurate and reliable current measurement is a key prerequisite for ensuring the safe operation of power systems. Conventional through-core and wound current transformers require power outage for installation or modification of line structures, which are plagued by high installation difficulty and cost, and fail to meet the digital development needs of smart grids. To address the demand for non-intrusive installation of current transformers, this paper proposes a non-intrusive current transformer with PCB coils in reverse-series connection. First, a magnetic coupling current calculation model is established to design a reverse-series double-layer coil structure, and a mathematical model of the equivalent circuit for the sensing and measurement system is constructed. The influence of circuit parameters on the output response is analyzed, yielding an optimization method for the system operating state and completing the hardware circuit design. Subsequently, a simulation model of the reverse-series double-layer coil is built to calculate and analyze the amplitude-frequency characteristics, steady-state and transient performance, as well as anti-interference capability of the transformer. The results demonstrate that the designed transformer, combined with an active integrating circuit, achieves an upper cutoff frequency of 13,169 Hz and a lower cutoff frequency approaching 0 Hz, which satisfies the requirements of wide-frequency measurement while ensuring high sensitivity and anti-interference capability. Finally, a current-sensing experiment platform is built for comparative verification with conventional invasive current transformers. Experimental results show that after correction with a proportional coefficient of 1.317, the fitting squared error is only 0.0038. The linearity remains excellent under different conditions with a wide dynamic measurement range, and the phase error is less than 15°. Within the range of 2–120% of the rated current, the ratio error is less than 0.9%, indicating high measurement accuracy. This study provides a new high-precision and convenient method for current measurement in smart grids.

Designs doi: 10.3390/designs10010019

Authors: Nikitas Gerolimos Vasileios Alevizos Emmanouela Sfyroera Johannis Tsoumas Georgios Priniotakis George A. Papakostas

This comprehensive review investigates how biomimetic mechanisms inform engineered systems that adapt to the user and environment during use, marking a shift from aesthetic imitation to functional compliance. By synthesizing a curated evidence base of 52 key studies, this work identifies four investigation domains: (i) biomorphic structures, (ii) compliant material systems, (iii) computational modelling via AI and digital twins, and (iv) integrated ergonomic-sustainability evaluations. Our analysis reveals a technical continuum dominated by Passive Compliance (59.6%), while identifying significant translational bottlenecks in closed-loop adaptive verification. To address these gaps, the study introduces a functional taxonomy and the Nautilus Model as a maturity framework for iterative, knowledge-preserving design. Furthermore, a set of benchmark tasks (e.g., 100 Hz adaptation, 500,000-cycle durability) is established to support the validation of future co-evolutionary, eco-centric products. This synthesis establishes a new research agenda that integrates biological self-organization with rigorous ergonomic verification.

Designs doi: 10.3390/designs10010018

Authors: Jiacheng Li Xishuo Zhu Han Tian Junsheng Zhang Hao Li Haoting Liu Junyuan Li

Chain breakage in dual-chain scraper conveyors poses significant risks to the safe and efficient operation of coal mines. To address the challenges of harsh underground environments and the lack of effective synchronization monitoring, this paper presents the design and implementation of an intelligent monitoring system for conveyor integrity. The system integrates non-contact Hall-effect sensors with a custom-designed intrinsically safe data acquisition unit. A systematic algorithmic framework is designed, comprising an adaptive threshold and plateau seeking (ATPS) module and an adaptive clustering-based identification (ACCI) module, to enable high-accuracy automatic identification of chain elements. Furthermore, a novel synchronization evaluation design based on event correlation and statistical features is introduced to quantify inter-chain timing deviations. This leads to the construction of a Chain Synchronization Index (CSI) for desynchronization anomaly detection. Field experiments conducted under representative operating conditions, including normal operation and controlled single-chain disconnection scenarios, demonstrate that the proposed design achieves a chain element recognition accuracy of 98.2%. Under normal conditions, the CSI remains consistently high, while breakage faults are sensitively detected. The proposed system provides a practical engineering solution for synchronization-aware condition monitoring and anomaly warning of scraper conveyor chains in underground coal mines.

Designs doi: 10.3390/designs10010017

Authors: Vincenzo La Battaglia Livia Del Pinto Stefano Marini Alessandro Giorgetti Gabriele Arcidiacono

This work presents the development of a measuring instrument capable of assessing the possible presence of critical permanent deformations on the connecting rod in hybrid cars equipped with gasoline-powered internal combustion engines. The permanent deformation can be due to incorrect fueling and cause a progressive engine failure through the breaking of one or more connecting rods. The measuring tool developed is a non-invasive, low-cost system and permits the detection of the incipient damage without dismantling the engine, thus assuring a time-saving approach. The instrument is composed of a mechanical system and an electronic interface that permits easy use during measuring operations and the possibility to store the data collected. An experimental campaign was implemented to validate the measurement system’s capability to detect this type of damage and to determine a threshold beyond which it is necessary to proceed with the replacement of connecting rods. The results show the optimal ability to differentiate between usual technological variability of the piston stroke and the range that can be connected to the anomaly studied. The system is also able to permit the measurement of a whole engine in less than 20 min.

Designs doi: 10.3390/designs10010016

Authors: Oumaima Mikram Abdelmajid Abouloifa Ibtissam Lachkar Chaouqi Aouadi Juan Wang

The widespread adoption of nonlinear loads in industry has introduced significant power quality issues in electric power distribution grids. The integration of these nonlinear loads has led to the proliferation of serious power quality problems such as the generation of harmonics and reactive power that negatively impact the quality and stability of the electrical grid. In addition to eliminating current harmonics, a shunt active power filter (APF) can also provide reactive power compensation. By dynamically adjusting the reactive power injection, these APFs can improve the power factor of the system and maintain the desired voltage regulation. The proposed control leverages the differential flatness property of the SAPF system, allowing for exact linearization and simplified tracking control without requiring complex modulation techniques. In this paper, a flatness-based control scheme is proposed for a three-phase three-level Neutral Point Clamped (NPC) APF. The main objectives of this work are twofold. The first objective is to mitigate current harmonics and compensate the reactive power drawn by nonlinear loads. The second objective focuses on maintaining a stable DC-link capacitor voltage of the active power filter (APF). To meet these requirements, a cascaded control structure is used, where the external loop regulates the DC-link voltage, while the inner loop is responsible for harmonic current compensation. The effectiveness of the proposed control strategy is validated through simulation results obtained using the MATLAB/Simulink R2024a environment.

Designs doi: 10.3390/designs10010015

Authors: Paweł Turek Jarosław Tymczyszyn Paweł Habrat Jacek Misiura

Reverse engineering (RE) is essential in the automotive and aerospace industries for reconstructing high-precision components, such as exhaust valves, when design documentation is unavailable. However, different measurement methods introduce varied errors that can affect engine performance and safety. This study presents a comparative analysis of contact and optical measurement systems—specifically the CMM Accura II (ZEISS Group, Oberkochen, Germany), Mahr MarSurf XC 20 (Esslingen am Neckar, Germany), GOM Scan 1 (ZEISS/GOM, Braunschweig/Oberkochen, Germany) and MCA-II with an MMD×100 laser head (Nikon Metrology, Leuven, Belgium)—to assess their accuracy in reconstructing exhaust valve geometry. The research procedure involved measuring global surface deviations and critical functional parameters, including stem diameter, straightness, and seat angle. The results indicate that tactile methods (CMM and Mahr) provide significantly higher accuracy and lower dispersion than optical methods. The Mahr system was the most effective for stem precision, while the CMM was the only system to pass the seat angle tolerance requirement unambiguously. In contrast, the MCA-II laser system failed to meet the required precision–mechanical tolerances. The findings suggest that an optimal industrial strategy should adopt a hybrid methodology: utilizing rapid optical scanning (GOM) for general geometry and high-precision tactile systems (CMM, Mahr) for critical functional features. This approach can reduce total inspection time by 30–40% while ensuring technical safety and preventing catastrophic engine failures.

Designs doi: 10.3390/designs10010014

Authors: Youliang He

Inverse pole figure mapping is a common orientation visualization method used in electron backscatter diffraction (EBSD) software to display crystal orientations. Although this technique has been routinely used in commercial EBSD software, the coloring algorithm employed to map the orientation and construct the color key (standard stereographic triangle) has not been reported in the literature. This paper presents a simple algorithm to color the standard stereographic triangles of the 11 Laue groups by mapping the Maxwell color triangle to the curved standard stereographic triangles using nonlinear shape functions commonly employed in finite element methods. Detailed procedures are given to illustrate how the mapping is performed and how it is used to construct inverse pole figure maps from Euler angles. Color coding of the seven different standard stereographic triangles is demonstrated using a computer program written in C++. It is shown that the simple color-coding algorithm presented in this paper can be conveniently utilized to display orientation data in inverse pole figure maps, which is a critical part of designing customized EBSD software. It also provides a method to adjust the color center within the curved triangles to more uniformly distribute the color, which is not available in commercial EBSD software. The algorithm can also be used to design orientation representation software for other applications, e.g., crystal plasticity simulations, where representation of orientation data is also a routine task.

Designs doi: 10.3390/designs10010013

Authors: Aravinthan Arumugam Alokesh Pramanik Amit Rai Dixit Animesh Kumar Basak

Abrasive water jet machining (AWJM) is a non-traditional machining process that is increasingly employed for shaping hard-to-machine materials, particularly titanium (Ti)-based alloys such as Ti-6Al-4V. Owing to its non-thermal nature, AWJM enables effective material removal while minimising metallurgical damage and preserving subsurface integrity. The process performance is governed by several interacting parameters, including jet pressure, abrasive type and flow rate, nozzle traverse speed, stand-off distance, jet incident angle, and nozzle design. These parameters collectively influence key output responses such as the material removal rate (MRR), surface roughness, kerf geometry, and subsurface quality. The existing studies consistently report that the jet pressure and abrasive flow rate are directly proportional to MRR, whereas the nozzle traverse speed and stand-off distance exhibit inverse relationships. Nozzle geometry plays a critical role in jet acceleration and abrasive entrainment through the Venturi effect, thereby affecting the cutting efficiency and surface finish. Optimisation studies based on the design of the experiments identify jet pressure and traverse speed as the most significant parameters controlling the surface quality in the AWJM of titanium alloys. Recent research demonstrates the effectiveness of artificial neural networks (ANNs) for process modelling and optimisation of AWJM of Ti-6Al-4V, achieving high predictive accuracy with limited experimental data. This review highlights research gaps in artificial intelligence-based fatigue behaviour prediction, computational fluid dynamics analysis of nozzle wear mechanisms and jet behaviour, and the development of hybrid AWJM systems for enhanced machining performance.

Designs doi: 10.3390/designs10010012

Authors: Florina-Ambrozia Coteț Sára Ferenci Elena Simina Lakatos Loránd Szabó

Hydrogen-integrated decentralized energy systems (DIESs) promise communities higher renewable penetration, greater resilience, and sector coupling across electricity, heat, and mobility. AI supports forecasting, dispatch optimization, multi-asset coordination, and planning, yet designing AI-driven hydrogen communities is challenging because it spans physical infrastructure, cyber-control, and governance. This review (2020–2025) synthesizes design strategies for AI-enabled hydrogen DIESs, distilling architectural patterns, electricity–hydrogen co-optimization, uncertainty-aware operation, and digital-twin planning. It summarizes AI benefits (flexibility, efficiency, reduced curtailment) and recurring risks (forecast-optimization cascades, objective mismatch, data drift, safety and constraint breaches, digital-twin credibility gaps, cybersecurity and privacy issues, and weak reproducibility) and proposes a pragmatic roadmap prioritizing safety-aware control, standardized metrics, transparent assumptions, and community-appropriate governance.

Designs doi: 10.3390/designs10010011

Authors: Gene Hou Jonathan DeGroff

Design optimization is a computational tool that can enable a designer to investigate the effectiveness of a design concept in an organized format. However, this design process requires the design variables, constraints, and objective function to be properly defined and expressed in mathematical forms. Post-optimality analysis thus becomes a necessary step to investigate different variations in the problem formulation and parameters to ensure that optimization produces a stable and trustworthy outcome. One efficient way to achieve this aim is to compute the local derivative of the optimized objective function with respect to the optimization problem parameters, such as bounds on the constraints and the material properties in the state equation. This method is referred to as post-optimality sensitivity analysis. In this study, we derived the post-optimal sensitivity equation to explicitly include the derivatives of state variables with respect to problem parameters and to broaden its applications to minimax and goal attainment design optimization problems.

Designs doi: 10.3390/designs10010010

Authors: Daniel Milling Marzieh Kadivar Aziz Ahmed

As structural engineers face increasing pressure to minimize the embodied carbon of building components, selecting appropriate materials is critical for sustainable design. Thiemission ts study evaluates the life cycle performance of engineered bamboo beams to determine their viability as a low-carbon alternative to traditional timber in structural framing applications. Utilizing OpenLCA software and the Ecoinvent database, a cradle-to-grave analysis was conducted to inform material selection for the Australian construction context. A parametric design study compared two specific bamboo species, Moso and Asper, against traditional Laminated Veneer Lumber (LVL) to identify the optimal material for minimizing environmental impact. The assessment revealed that Asper bamboo beams represent a superior design choice; a 30.74 kg strand-woven functional unit (FU) achieved net-negative emissions of −13.30 kg CO2e under 2025 conditions. This offers a significant design advantage over traditional LVL options, which are net-positive emitters, and outperforms Moso bamboo, which yielded higher net emissions (+24.60 kg CO2e) due to lower sequestration rates. Furthermore, dynamic analysis demonstrated the temporal efficiency of this material in the structural life cycle: in the time required for a single Radiata Pine rotation, Asper bamboo completes five growth cycles, storing a net 103.25 kg of CO2e per functional unit. Confirmed by a sensitivity analysis for robustness, these findings provide quantitative design criteria supporting the integration of Asper bamboo into sustainable building standards and structural specifications.

Designs doi: 10.3390/designs10010009

Authors: Jubayer Ahmed Sajid Seeyama Hossain Ivan Grgić Mirko Karakašić

Decarbonisation of the aviation sector is essential for achieving global-climate targets, with hydrogen propulsion emerging as a viable alternative to battery–electric systems for vertical flight. Unlike previous studies focusing on clean-sheet eVTOL concepts or fixed-wing platforms, this work provides a comprehensive retrofit evaluation of a two-seat light helicopter (Cabri G2/Robinson R22 class) to a hydrogen–electric hybrid powertrain built around a Toyota TFCM2-B PEM fuel cell (85 kW net), a 30 kg lithium-ion buffer battery, and 700 bar Type-IV hydrogen storage totalling 5 kg, aligned with the Vertical Flight Society (VFS) mission profile. The mass breakdown, mission energy equations, and segment-wise hydrogen use for a 100 km sortie are documented using a single main rotor with a radius of R = 3.39 m, with power-by-segment calculations taken from the team’s final proposal. Screening-level simulations are used solely for architectural assessment; no experimental validation is performed. Mission analysis indicates a 100 km operational range with only 3.06 kg of hydrogen consumption (39% fuel reserve). The main contribution is a quantified demonstration of a practical retrofit pathway for light rotorcraft, showing approximately 1.8–2.2 times greater range (100 km vs. 45–55 km battery-only baseline, including respective safety reserves). The Hawk demonstrates a 28% reduction in total propulsion system mass (199 kg including PEMFC stack and balance-of-plant 109 kg, H2 storage 20 kg, battery 30 kg, and motor with gearbox 40 kg) compared to a battery-only configuration (254.5 kg battery pack, plus equivalent 40 kg motor and gearbox), representing approximately 32% system-level mass savings when thermal-management subsystems (15 kg) are included for both configurations.

Designs doi: 10.3390/designs10010008

Authors: Anael Vizcarra Gustavo Quiroz Jose Cornejo

User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas ergonomically informed interfaces can substantially improve task performance. This systematic literature review analyzes 20 peer-reviewed studies published between 2020 and 2024 to examine how visual ergonomics embedded in UI/UX design contributes to error reduction across industrial and professional contexts. The reviewed studies report measurable improvements when ergonomic principles are applied, including reductions in operational errors ranging from approximately 30% to 70%, improvements in task completion time between 20% and 60%, and increased user accuracy and satisfaction in safety-critical and high-workload environments. The findings indicate that visual hierarchy, modular layouts, adaptive components, and real-time feedback are consistently associated with improved performance outcomes. Moreover, task complexity, user expertise, and working conditions were identified as moderating factors influencing ergonomic demands. Overall, the review demonstrates that visual ergonomics should be treated not merely as a usability enhancement but as a strategic design approach for minimizing human error and supporting reliable human–machine interaction in complex digital environments.

Designs doi: 10.3390/designs10010007

Authors: Michael Natale Cunzolo Aziz Ahmed

This paper addresses critical issues related to the structural design of a micro-factory housing a mobile 3D printing system for plastic recycling. Rather than a simple comparison, it quantifies the “modification penalty”, the structural and economic cost of retrofitting a repurposed ISO shipping container (ISCC) versus deploying a purpose-built cold-formed steel (CFS) volumetric structure. Finite Element Analysis of a standard 20-foot shipping container revealed a serviceability failure in its roof under standard imposed loads. Concurrently, an initial analysis of an equivalent CFS structure also indicated non-compliance, with significant floor and roof deflections. Both platforms were subsequently redesigned with structural reinforcements to achieve full compliance with Australian Standards. The comparative evaluation moves beyond static analysis to incorporate critical performance metrics. While the CFS structure proved to be 575 kg lighter with a lifespan 300–400% longer, the modified ISCC was 47% cheaper in initial capital outlay ($7161 vs. $13,549). However, when considering the totality of performance factors, specifically the ISCC’s inherent vulnerability to resonance (8–18 Hz), which overlaps with transport frequencies, and the logistical burden of losing CSC certification upon modification, the CFS platform is conclusively identified as the superior engineering solution. Its design flexibility, predictable performance, and amenability to purpose-built optimization make it a more reliable and operationally secure platform for this specialized application.

Designs doi: 10.3390/designs10010006

Authors: Madiha Sattar Usman Masud Abdul Razzaq Farooqi Faraz Akram Zeashan Khan

With the increasing integration of renewable energy sources into the grid, power quality at the point of common coupling (PCC)—particularly harmonic distortion introduced by power electronic converters—has become a critical concern. This paper presents a rigorous design and evaluation of a three-phase, fifteen-level cascaded H-bridge multilevel inverter (CHB MLI) with an LCL filter, selected for its superior harmonic attenuation, compact size, and cost-effectiveness compared to conventional passive filters. The proposed system employs Phase-Shifted Pulse Width Modulation (PS PWM) for balanced operation and low output distortion. A systematic, reproducible methodology is used to design the LCL filter, which is then tested across a wide range of switching frequencies (1–5 kHz) and grid impedance ratios (X/R = 2–9) in MATLAB/Simulink R2025a. Comprehensive simulations confirm that the filter effectively reduces both voltage and current total harmonic distortion (THD) to levels well below the 5% limit specified by IEEE 519, with optimal performance (0.53% current THD, 0.69% voltage THD) achieved at 3 kHz and X/R ≈ 5.6. The filter demonstrates robust performance regardless of grid conditions, making it a practical and scalable solution for modern renewable energy integration. These results, further supported by parametric validation and clear design guidelines, provide actionable insights for academic research and industrial deployment.

Designs doi: 10.3390/designs10010005

Authors: Wladyslaw Koc

This study discusses the issue of designing reverse curves, i.e. a geometric system consisting of two circular arcs (usually with different radii), directed in opposite directions and directly connected to each other. The design is performed in an appropriate local Cartesian coordinate system. The origin of this system is located at the point of intersection of adjacent main directions of the route. Unlike other geometric situations, reverse curves have three main directions, which significantly complicate the design process. The initial values of the radii of the reverse arcs must correspond to the existing system of main directions. The introduction of transition curves causes these radii to decrease; their values are determined iteratively. A set of formulas for creating a geometric system of reverse curves is presented. These formulas were used in the calculation example. A graph of the horizontal curvature of the track axis and a method for determining the possible train speed, both without the use of cant on an arc and with the use of cant, are shown. The presented procedure is universal and can be applied to other geometric situations involving the design of reverse curves. It is also necessary to emphasize the practical usefulness of the discussed method not only in the design process, but also to pay attention to the cognitive value of the article.

Designs doi: 10.3390/designs10010004

Authors: Mert Ozkaya Alper Turunc Yusuf Talha Togrul

Smart charging management systems for electric vehicles (SCMSs) enable the effective management of electric vehicle (EV) charging processes using smart technologies. Numerous SCMS technologies have been available for different stakeholders, e.g., EV drivers, charging station managers, and car manufacturers. Despite the ever-increasing interest in SCMSs, the literature lacks in reusable, standardised architecture design that reduces the effort for the development of quality SCMSs. In this paper, we propose a reference architecture (RA) for SCMSs. Our RA design is based on our comprehensive domain analysis that encompasses the analysis of the existing literature and commercial technologies which have been supported by our survey on EV drivers. In our RA, we provide four different viewpoints. The context viewpoint classifies the potential stakeholders and their roles and responsibilities. The module viewpoint defines the software implementation units and their modules that can be used for implementing any SCMSs. The component and connector viewpoint defines the executing parts of any SCMSs and their organisations into layers. The allocation viewpoint defines how the executable components can be mapped into the physical devices. We validated our RA design via prototyping and surveying to measure the RA’s applicability in real-world scenarios and usability for stakeholders.

Designs doi: 10.3390/designs10010003

Authors: Rocco Furferi Andrea Profili Monica Carfagni Lapo Governi

Liquid Crystal Elastomers combine the elasticity of polymer networks with the anisotropic ordering of liquid crystals, thus enabling reversible shape modifications and stimulus responsive actuation. Unfortunately, manual LCE fabrication remains limited by operator-dependent variability, which can lead to inconsistent film thickness and manufacturing times inadequate for a mass production. This work presents a low-cost, automated manufacturing framework that redesigns the mechanical assembly steps of the traditional one-step LCE fabrication process. The design includes rubbing, slide alignment, spacer placement, and infiltration cell assembly to ensure consistent film quality and scalability. A customized Cartesian robot, built by adapting a modified X–Y core 3D printer, integrates specially designed manipulator systems, redesigned magnetic slide holders, automated rubbing tools, and supporting fixtures to assemble infiltration devices in an automated way. Validation tests demonstrate reproducible infiltration, improved mesogen alignment confirmed via polarized optical microscopy, and high geometric repeatability, although glass-slide thickness variability remains a significant contributor to deviations in final film thickness. By enabling parallelizable low-cost production, the designed hardware demonstrates its effectiveness in devising the scalable manufacturing of LCE films suited for advanced therapeutic and engineering applications.

Designs doi: 10.3390/designs10010002

Authors: Abdin Abdin Nicola Bianchi

The aim of this research is to present both a sensorless control and a torque derating algorithm in the overload region of a permanent magnet motor for e-bikes. First, the theoretical backgrounds and the field-oriented control are presented. Then, a sensorless control is designed based on the back-emf estimation with a second-order generalized integral flux observer for the permanent magnet motor. The second-order generalized integral flux observer is an adaptive filter which can eliminate the DC offset and strongly attenuate the harmonics of the estimated rotor flux. The algorithms have been simulated and then validated by means of tests on a permanent magnet motor for e-bikes.

Designs doi: 10.3390/designs10010001

Authors: Putra Aji Pangestu Christian Harito Elioenai Sitepu Safarudin Gazali Herawan Syauqi Abdurrahman Abrori Cokisela Christian Lumban Tobing

Cerebral palsy (CP) often causes mobility limitations that require assistive devices such as Ankle Foot Orthoses (AFOs) to enhance functional stability. This study aims to develop an optimized 3D-printed AFO design that improves comfort, structural durability, and production efficiency for children with CP. The research applies a Design of Experiment approach using the Taguchi method to optimize 3D printing parameters, supported by tensile testing to identify the best material configuration. Design alternatives were prioritized using the Analytical Hierarchy Process, while Finite Element Analysis was conducted to evaluate mechanical performance under physiological loading. The selected PETG configuration (33% infill density and 0.15 mm layer thickness) demonstrated improved tensile strength and flexibility, contributing to enhanced structural behavior. A prototype was produced and validated using the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) questionnaire. Results showed higher overall user satisfaction for the optimized 3D-printed AFO compared to conventional devices, particularly in safety, comfort, and durability. The integration of optimized material parameters, systematic design evaluation, and user-centered assessment provides an effective pathway toward improving AFO performance and supporting the mobility and quality of life of children with cerebral palsy.

Designs doi: 10.3390/designs9060146

Authors: Muhammad Hijaaj Tahir Catherine E. Jones Robert Ian Whitfield

Multifunctional aerostructures that carry mechanical loadings while conducting electrical currents offer a promising approach to reduce the weight of Electrical Power Systems (EPS) of aircraft. However, Carbon Fibre-Reinforced Polymer (CFRP), when used for aerostructures, presents challenges in achieving multi-functionality due to anisotropic mechanical, electrical, and thermal properties. These properties are interdependent on both laminate-level design factors (fibre/resin choice, fibre volume fraction, stacking sequence, and electrode configuration) and system-level EPS constraints (allowable voltage drop, current, and installation geometry). State-of-the-art material design and selection methods lack a coupled mechanical–electro–thermal design and selection approach to overcome these challenges of a complex design space to enable identification of multifunctional CFRP (MF-CFRP) solutions. This paper presents the first methodology for the design and selection of MF-CFRP with combined electrical, structural, and thermal properties. The methodology integrates requirement capture, laminate layup determination, electro-thermal assessment, option ranking, and manufacturing route selection. The methodology couples laminate-level design factors with system-level EPS constraints and includes iterative loops to refine either the CFRP design or the EPS parameters when no solution initially exists. The methodology is demonstrated to enable the design of a CFRP component to conduct the electrical current as part of the 28 VDC network in an aircraft. This case study demonstrates the value of the methodology to identify knowledge and dataset gaps necessary for MF-CFRP design, alongside enabling the design of MF-CFRP components to enable increased power density of weight-critical EPS. Although the case study focused on a 28 VDC system, the methodology is generalisable to other aircraft electrical architectures since system-level electrical parameters are used within the methodology as adaptable inputs.

Designs doi: 10.3390/designs9060145

Authors: Hari Tunga Neloy Shome Amirmohammad Shafiee Prisha Jonnalagadda Noah Wong Amirmahdi Shafiee Sohan Bobba Karanjit Kooner

Glaucoma is recognized as the second leading cause of blindness globally and a primary cause of irreversible blindness, estimated to affect over 80 million patients worldwide, including 4.5 million in the United States. Though the disease is multifactorial, the primary cause is elevated intraocular pressure (IOP), which damages the optic nerve fibers that connect the eye to the brain, thus interfering with the quality of vision. Current treatments have evolved, which consist of medications, laser therapies, and surgical interventions such as filtering procedures, glaucoma drainage devices (GDDs), and current innovations of minimally invasive glaucoma surgeries (MIGS). This paper aims to discuss the history and evolution of the design and biomaterials employed in GDDs and MIGS. Through a comprehensive review of the literature, we trace the development of these devices from early concepts to modern implants, highlighting advancements in materials science and surgical integration. This historical analysis, ranging from the mid-19th century, reveals a trend towards enhanced biocompatibility, improved efficiency in IOP reduction, and reduced complications. We conclude that the ongoing evolution of GDDs and MIGS underscores a persistent commitment to advancing patient care in glaucoma, paving the way for future device innovations and therapeutic trends to treat glaucoma.

Designs doi: 10.3390/designs9060144

Authors: Luis Miguel Pires Ileida Veiga

This work presents the development and evaluation of a low-cost and low-power IoT system for monitoring slope instabilities, rockfalls, and landslides using LoRa communication. The prototype integrates commercial ESP32-based hardware with an SX1276 transceiver, a triaxial MEMS accelerometer, and a GPS module for real-time tilt and location measurements. A tilt-estimation expression was derived from accelerometer data, enabling adaptation to different terrain inclinations. Laboratory tests were performed to validate the stability and accuracy of the inclination measurement, followed by outdoor LoRa range tests under mixed line-of-sight conditions. A lightweight dashboard was implemented for real-time visualization of GPS position, signal quality, and tilt data. The results show reliable tilt detection, consistent long-range communication, and low power consumption, highlighting the potential of the proposed prototype as a scalable and energy-efficient tool for geotechnical monitoring.

Designs doi: 10.3390/designs9060143

Authors: Dongqiang Li Baodong Jiang Gan Li Chun Zhu

In the field of engineering construction design, slope instability near water bodies remains a significant challenge. This issue is influenced by various factors, including fluid dynamics and external load disturbances. This study focuses on the design and stability evaluation of the slope in the Sejiang deformation area of the Baala Hydropower Station, applying three advanced techniques: PS-InSAR remote sensing for dynamic slope deformation data, FLAC3D stability simulation for numerical analysis of slope stability, and FLOW-3D wave calculation for quantifying secondary wave effects caused by potential landslides. By integrating these technologies, the study provides a multi-dimensional, quantitative evaluation of the secondary disasters triggered by landslides in this region. The findings are as follows: (1) The slope in the deformation zone exhibits a long-term “stable-creep” evolution, characteristic of a “stable-creep landslide” type; (2) Sliding failure primarily occurs along the interface between the bedrock and overburden layer due to shear deformation; (3) When the deformation body, with a volume of 2.1 million cubic meters, slides into the water at a velocity of 24 m/s, the calculated maximum water level height on the opposite bank reaches approximately 2925 m, near the top elevation of the dam, but still within the project’s preset safety threshold. The design methodologies and conclusions drawn from this study offer valuable insights for evaluating and designing the stability of near-water slopes in other hydropower stations.

Designs doi: 10.3390/designs9060142

Authors: Peng Zhang Hongjie Jin Rui Guo Xiaokun Xu Shuaikang Li Qingxiang Meng

The anti-slide pile with multi-row prestress anchor is widely used to prevent the failure of the slope. This paper proposes a multi-row anchor prestress optimization method based on a staged uniform design that combines FLAC3D 9.0 numerical simulations with the minimum bending moment criterion. By determining a global reference prestress and performing successive layered adjustments, the proposed method effectively controls the peak bending moment of the support structure and significantly enhances overall stability. Case studies demonstrate that this method reduces the peak bending moment of piles by approximately 31.88%, leading to a more uniform bending moment distribution, improved safety, and better cost efficiency. These results indicate that the proposed method provides an efficient and reliable approach for optimizing prestress distribution in complex slope support systems.

Designs doi: 10.3390/designs9060141

Authors: Wanling Yang Yasha Ji Shengtao Zhou Ling Ji Yu Lei Minhao Wang

Rice husk ash (RHA) offers an eco-friendly way to improve concrete. Owing to the complex mix design of RHA concrete, accurately predicting its strength remains a challenge. This study addresses this need by compiling a dataset of 291 compressive strength records for RHA concrete. Using seven key input variables (e.g., cement, water, and RHA content), three novel hybrid models were developed by integrating the XGBoost algorithm with advanced metaheuristic optimizers: Northern Goshawk Optimization (NGO), Arctic Puffin Optimization (APO), and Catch Fish Optimization Algorithm (CFOA). These hybrid models were compared against classic Random Forest (RF), and Support Vector Regression (SVR), and unoptimized XGBoost models. The results demonstrated that all hybrid models significantly outperformed the unoptimized classic models. The APO–XGBoost model achieved the highest prediction accuracy on the testing set (RMSE = 3.5462, R2 = 0.9579 on testing set), followed by CFOA–XGBoost and NGO–XGBoost. Cement content was revealed to be the most influential parameter on compressive strength, as determined by a sensitivity analysis, ahead of both water and coarse aggregate content. This research confirms the superiority of metaheuristic-optimized hybrid models for predicting the strength of RHA concrete, providing a reliable data-driven tool to support its mix design and promote its application in sustainable construction.

Designs doi: 10.3390/designs9060140

Authors: Zhuming Bi Ruaa Jamal Rabi Salem Alfakawi Hosni Abu-Mulaweh Donald Mueller

This article provides a Structured Literature Review (SLR) on the uses of Digital Twins (DT-Is) in the development of medical products. The purposes of our SLR are to find out (1) whether existing DT-I technologies are mature enough to be adopted for new medical product development, and (2) if the answer to item (1) is no, what existing works can be utilized in developing DT-Is for designs of bone fixations? It is our finding that numerous works are reported on using DT-Is in healthcare applications such as remote surgeries, remote diagnoses, personalized medicines, and assistive technologies. These applications involve one-to-one correspondence of physical and digital entities but exhibit several limitations in (1) inheriting and transferring knowledge from legacy products to new products and (2) a lack of a systematic approach in creating innovations for new product development. We suggest adopting Digital Triad (DT-II) for medical product development. A background study on using DT-II for the design of bone staples is conducted to illustrate the feasibility of the proposed idea.

Designs doi: 10.3390/designs9060139

Authors: Tony Castillo-Calzadilla

In the current energy transition landscape, characterised by the dual requirements of accelerated decarbonisation and increased resilience to disruptions in the electricity system, the concept of positive energy districts (PEDs) is becoming increasingly relevant [...]

Designs doi: 10.3390/designs9060138

Authors: Taher Maatallah Mussad Al-Zahrani Salman Hilal Abdullah Alsubaie Mohammad Aljohani Murad Alghamdi Faisal Almansour Loay Awad Yassine Slimani Sajid Ali

The transition to low-carbon energy systems necessitates innovative design strategies for decarbonizing hydrogen production, particularly in industrial-scale applications where steam methane reforming (SMR) remains predominant. This study proposes a novel, integrated process design for blue hydrogen production that addresses both energy and environmental sustainability through process intensification and resource valorization. A hybrid system was developed that combines solar thermal energy input with the catalytic potential of industrial waste, specifically, red mud, a byproduct of alumina refining. A solar parabolic dish (SPD) was engineered to contribute 10% of the heat demand, generating superheated steam at 477 °C. This work serves as a proof-of-concept, demonstrating the technical viability of integration at a bench scale. In parallel, red mud was characterized, thermochemically activated, and formulated into a low-cost catalyst for the SMR process. The integrated system includes solar-assisted steam generation, red mud-based catalytic reforming, CO2 capture using methyl diethanolamine (MDEA), and hydrogen purification via pressure swing adsorption (PSA). The full process was modeled and optimized using ASPEN Plus, ASPEN Adsorption, and COMSOL Multiphysics® Under optimal conditions (900 °C, 25 bar, steam-to-carbon ratio of 3), the system produced 1070 kg/h of hydrogen, achieving 95% CO2 capture efficiency and 99.99% hydrogen purity. Techno-economic analysis revealed the red mud-derived catalyst costs 3.89 SAR/g (1.04 USD/g), a 77% cost reduction compared to conventional Ni-based catalysts. The integration of solar thermal energy, while offering modest direct economic savings of approximately 9500 SAR (2530 USD) annually, primarily demonstrates the technical feasibility of renewable heat integration for reducing the carbon intensity of hydrogen production.

Designs doi: 10.3390/designs9060137

Authors: Nikhil Aryan Narendra Gariya Pravin Sankhwar

Soft pneumatic actuators (SPAs) are gaining attention in the field of soft robotics due to their lightweight, highly flexible, and safer interaction while operated under an unstructured environment. They are easy to fabricate, produce high output force, and are relatively very inexpensive compared to other soft actuators. However, accurate prediction of their nonlinear bending behavior is one of the main challenges, which is mainly due to the complex material properties and high deformation patterns. Therefore, this study focused on a hybrid approach that accurately captures the bending behavior of a single-chambered SPAs. This approach integrates physics-based modeling (finite element analysis (FEA) and analytical modeling) with a data-driven (polynomial regression modeling) approach to analyze the bending of single-chambered SPAs. Initially, four different hyperelastic material models (Neo-Hookean, Yeoh, Arruda–Boyce, and Ogden) were tested using FEA to analyze how material selection affects the SPA response. It is found that the Arruda–Boyce model generates the highest bending of 101° at 30 kPa pressure, while the other models consistently underestimated deformation at higher pressures. Further, an enhanced mathematical or analytical model was developed using Euler and Timoshenko beam theory with certain assumptions, such as neutral axis shifting, chamber ballooning, and shear deformation. These assumptions significantly improve the prediction accuracy and generate a bending angle of 99°at 30 kPa, which closely matches FEA bending. Further, a polynomial regression-based machine learning (ML) model was trained using analytical or mathematical bending data for faster output prediction. This data-driven approach achieves very high accuracy in the validation range, with an average absolute percentage deviation of only 0.002%. Additionally, comparison with the analytical results showed a mean absolute error (MAE) of 0.00180°, root mean squared error (RMSE) of 0.00205°, and coefficient of determination (R2) value of 0.999999808. Overall, integrating physics-based modeling with a data-driven approach provides a reliable and scalable method for SPA design. It provides practical information on material selection, analytical correction, and ML modeling, which will reduce the need for time-consuming prototyping. Finally, this hybrid approach can help to accelerate the development of soft robotic grippers, rehabilitation tools, and other bio-inspired actuation systems.

Designs doi: 10.3390/designs9060136

Authors: Ilja Heitlager Bernard Jenniskens A. Georges L. Romme

Companies in asset-intensive industries, such as aviation and railways, face unique digital transformation challenges due to the misalignment between the rapid evolution of digital technologies and decades-long asset lifecycles. Existing innovation frameworks are inadequate for managing this complexity, which in turn creates tensions between innovation requirements and operational reliability demands. This paper therefore investigates how asset-intensive companies can systematically integrate digital technologies, while fully complying with regulatory constraints and safety requirements. We employ a design science approach in a study of Nederlandse Spoorwegen (NS), the Dutch national railway operator, focusing specifically on the implementation of AI-driven CCTV systems within the operations of NS. Drawing on a literature review and participant-observer as well as interview data, we develop six design propositions that address the key digital transformation challenges of asset-intensive companies in the area of market readiness assessment, modular architecture, regulatory compliance, temporal coordination, ecosystem governance, and organizational capability development. Using these design propositions, we develop the Iterative Development & Adoption Model (IDAM) that operationalizes market maturity assessment through market readiness levels to guide make-or-buy transitions across four iterative phases: ideate, assess, realise, and review. This model includes a Development Reference Architecture for emerging technologies and an Integration Reference Architecture for more mature technologies, enabling concurrent sourcing strategies based on technological maturity. IDAM provides actionable guidance for decisions about technology adoption in asset-intensive contexts, thereby offering a systematic approach to innovation management in industries with very long asset lifecycles and huge regulatory constraints.

Designs doi: 10.3390/designs9060135

Authors: Alexandru-Adrian Ancuta Cristian-Alexandru Rentea Daniel-Mihail Iozsa

Electric vehicles have a limited driving range compared to conventional vehicles. This paper aims to present a particular study of the performances of electric vehicles based on real driving conditions on a cycle carried out using the general conditions of European Regulation regarding RDE tests. In this sense, a modern electric SUV was used and experimental tests were conducted using a measuring equipment connected to the vehicle via the OBD2 plug. The experimental results will be analyzed and presented in the following sections, and the main influencing factors on the energy performances (range, state of charge, energy consumption) that may occur in real running conditions will be identified.

Designs doi: 10.3390/designs9060134

Authors: Marco Cuomo Alessandro Naddeo Rosaria Califano

Automotive seat comfort is a critical factor in enhancing driver satisfaction, especially in sports cars, where design must balance comfort features and performance-oriented features like lateral containment and anti-submarining. This study adopts an empirical-analytical approach for assessing and modelling perceived comfort in sports car seats using both objective and subjective data. A total of 64 participants (50 males, 14 females) evaluated two types of sports car seats—a road model (SEAT A) and a racing model (SEAT B)—during 15-min driving simulations using a dynamic simulator equipped with a full-body pressure mat (XSENSOR X3 PRO). Comfort was assessed through a postural comfort questionnaire using 10-point Likert scales. Statistical analysis revealed significant correlations between anthropometry, pressure distribution, and perceived comfort. In light of the correlation analysis, regression models were developed for four anthropometric percentile clusters (0–25th, 25–50th, 50–75th, 75–100th). Models were validated (accuracy > 75%) and one of them (named Model III) achieved accuracies of 95%, 96%, 90%, and 97% for its percentile clusters. The proposed models offer actionable insights for tailoring sports car seats to different user percentiles, enabling more personalized and effective seat designs that enhance both performance and comfort.

Designs doi: 10.3390/designs9060133

Authors: Li Li Tiansheng Chen Haibo Liu Rui Guo Ruiyu He Qingxiang Meng

To realistically simulate the entire slip-surface process from crack initiation to run-out, we couple the simplified Bishop method (LEM) with zero-thickness cohesive elements (CZM): LEM first pinpoints the critical slip circle, then CZM tracks interface opening, progressive damage, and sliding along that exact surface. Benchmarked against ACADS EX11, the framework reproduces the classical factor of safety while delivering the post-failure displacements, energy dissipation, and crack paths that LEM or traditional FEM cannot capture, offering a practical tool for landslide-prone slope design.

Designs doi: 10.3390/designs9060132

Authors: Carmen González-Lluch Raquel Plumed Pedro Company

This paper contributes to advancing the quality of 3D procedural models by proposing practices based on the principle of “how to avoid doing it wrong” derived from analyzing negative knowledge in CAD modeling. The described framework adopts the three levels of quality identified in the literature—usability, reusability, and design intent richness—while focusing specifically on deriving best practices to prevent failures based on negative knowledge related to reusability. The framework is built on the premise that quality issues hindering reusability can be categorized into three interrelated but largely independent types, each affecting the sketches, datums, or features within a procedural model’s tree structure. In this work, the study of sketches is addressed. The negative impact of typical failures, identified through representative case studies that illustrate both design and redesign errors, is analyzed to extract best practices that ensure CAD model reusability. While this study results in a practical guide for avoiding sketch-related errors that compromise the reusability of CAD models, the main contribution lies in demonstrating a framework that transforms negative knowledge into effective best practices.

Designs doi: 10.3390/designs9060131

Authors: Mohammad Javad Shahriyari Hossein Khaleghi Andrea Magrini Ernesto Benini

The effect of a novel blade treatment on the performance characteristics of NASA Rotor 37 is investigated numerically in this study. The treatment includes making special holes in the blade and near the tip section. The impact of the treatment on the end-wall flow structure is evaluated and discussed. Furthermore, the influence of the streamwise location and the angle of the holes is investigated. The results reveal that a significant stability enhancement can be achieved by the appropriate design of the hole location and configuration, at the expense of a small degradation in the peak efficiency and pressure ratio. It is shown that the position of the holes should be downstream of the passage shock wave to maximize the operating range of the rotor. In this situation, the shock is sucked back by the hole, which reduces its angle and postpones stall inception. Maximum stability improvement (about 30%) has been obtained for a hole angle equivalent to 75 degrees and a 60% chord location.

Designs doi: 10.3390/designs9060130

Authors: Sri Suresh Mavuri Surender Reddy Salkuti

This study introduces a design-oriented framework for an intelligent Energy Management System (EMS) in a Multi-Microgrid (MMG) environment to achieve efficient, reliable, and sustainable power operation. The proposed EMS is systematically designed to coordinate three interconnected microgrids with the main grid, optimizing Distributed Energy Resource (DER) utilization under uncertain weather, load, and market conditions. A novel Prairie Dog Optimization (PDO) algorithm is developed as a key algorithmic design innovation to enhance decision-making in day-ahead scheduling and load management. Through an optimization-based design approach, the EMS minimizes Energy Generation Cost (EGC) and Probability of Power Supply Deficit (PPSD). Simulation studies on a modified 33-bus system validate the design’s effectiveness, showing that PDO reduces operational cost by 5% and carbon emissions by 20% compared to Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). A better system performance is indicated by the optimal EGC of 0.1567 $/kWh and PPSD of 0.155%. Comprehensively, the PDO-based EMS is an important addition to the design engineering field by offering scalable, adaptive, and sustainable energy system design to the design of resilient and zero-emission MMG architectures to be used in the future in smart grids.

Designs doi: 10.3390/designs9060129

Authors: Rafaela Orenga Panizza Farzad Jalaei Mazdak Nik-Bakht

The construction industry is a major consumer of raw materials and a significant contributor to global waste. In Canada, the construction, renovation, and demolition (CRD) sector diverts only 16% of its waste from landfills, underscoring the urgent need for circular economy (CE) practices. This study develops a generalizable and reproducible framework for archetype identification to support CE strategies, with a focus on Montréal, Canada’s second-largest city. We define a new set of exterior shell archetypes for low-rise residential buildings and demonstrate their application in a neighborhood-scale case study. These archetypes enable systematic estimation of material and component stocks, as well as end-of-life recovery flows, across a representative sample of buildings in the Mercier–Hochelaga–Maisonneuve district. Results show that prioritizing reuse can nearly double material recovery compared to conventional sorting and recycling. More broadly, this framework advances engineering design for circular systems by integrating component-level data into reuse strategy assessment and providing a scalable approach for urban circularity.

Designs doi: 10.3390/designs9060128

Authors: Yonggang Yue Su Xu Yongqiang Fan Xiaoyun Tian Xunyu Liu Xiaobao Hu Jingang Wang

To address the problems of low inversion accuracy and poor noise resistance in pulsed eddy current (PEC) grounding grid depth detection, this study proposes a novel inversion model (IE-CBiLSTM). This model integrates the Informer Encoder with the CNN-BiLSTM for the first time to detect the depth of the PEC grounding grid and conducts experimental verification based on an independently designed pulsed eddy current detection device and a dedicated coil sensor. The model design employs a two-dimensional convolutional neural network (CNN) to extract local spatial features, combines a bidirectional long short-term memory network (Bi-LSTM) to model temporal dependencies, and introduces a multi-head attention mechanism along with the Informer structure to enhance the expression of key features. In terms of data construction, the design integrates both forward simulation data and measured data to improve the model’s generalization capability. Experimental validation includes self-burial experiments and field tests at a substation. In the self-burial test, the IE-CBiLSTM inversion results show high consistency with actual burial depths under various conditions (1.0 m, 1.2 m, and 1.5 m), significantly outperforming other optimization algorithms, achieving a coefficient of determination (R2) of 0.861, along with root mean square error (ERMS) and mean relative error (EMR) values of 17.54 Ω·m and 0.061 Ω·m, respectively. In the field test, the inversion results also closely match the design depths from engineering drawings, with an R2 of 0.933, ERMS of 11.30 Ω·m, and EMR of 0.046 Ω·m. These results are significantly better than those obtained using traditional Occam and LSTM methods. At the same time, based on the inversion results, a three-dimensional inversion map of the grounding grid and a buried depth profile were drawn, and the spatial direction and buried depth distribution of the underground flat steel were clearly displayed, proving the visualization ability of the model and its engineering practicality under complex working conditions. This method provides an efficient and reliable inversion strategy for deep PEC nondestructive testing of grounding grid laying.

Designs doi: 10.3390/designs9060127

Authors: Moses Karakouzian Maple Crow William Van Vlerin Patrick Whitton Mehrdad Karami

This study presents an initial feasibility concept paper for a proposed crosstie system, an innovative railroad crosstie reinforcement system designed to reduce the stresses transmitted to the underlying ballast. While not developed for a specific industry client, the proposed crosstie system lays the groundwork for patent application and potential commercialization, offering a novel alternative to conventional railroad construction. Finite Element Analysis demonstrated that this system can reduce effective stress on the ballast by up to 24%, effectively making train loads appear lighter to the substructure. The design of the proposed system focuses on mitigating the excessive stresses transmitted from crossties to the ballast layer in heavy axle load (HAL) freight rail operations. The goal was to create a reinforcement mechanism that is modular, compatible with existing track infrastructure, and capable of reducing maintenance costs by distributing loads more effectively across the ballast and subgrade. The findings indicate that this system is not only the most cost-effective and sustainable solution but also holds promise for reducing fixed stock investment, minimizing downtime for track maintenance, and enabling expanded rail network connectivity. These results support continued research and investment in the system’s development and deployment.

Designs doi: 10.3390/designs9060126

Authors: Sergei Kotov Marco Ceccarelli

A new version of the L-CADEL elbow joint assisting device is presented as version v5. The design is revised based on the experience of previous versions and on the requirements that consider the application for physical exercise for the elderly people at home. A laboratory prototype has been created with lightweight, portable and easy-to-use functionality that is confirmed by lab test results. A web interface was developed to manage the device as well as to acquire and elaborate data. Results of lab tests are discussed to validate the design feasibility and to characterize the operation performance for future clinical assessments.

Designs doi: 10.3390/designs9060125

Authors: Beatriz Amante Anna Sánchez Ana Puig-Pey Nil Lin Farré

Electric vehicles (EVs) are emerging as cost-effective and eco-friendly alternatives to gasoline cars, but widespread adoption still faces hurdles, notably the scarcity of public fast-charging stations. This paper proposes an optimal method to locate and size a fast-charging station in Barcelona, integrating solar photovoltaics (PV) and a battery energy storage system (BESS). The goal is to reduce range anxiety, cut investment costs, and minimize environmental impact. We introduce a modular, scalable station design compatible with second-life batteries and PV panels. Our methodology is twofold: first, determining the optimal charging infrastructure configuration; second, calculating financial viability via net present value (NPV) and internal rate of return (IRR). Results indicate that PV and BESS installation represents the largest cost component, yet energy independence enables rapid capital recovery, with payback in around four years. Selling surplus energy can generate an additional ~4% profit. NPV and IRR values confirm feasibility for scenarios using PV, BESS, or both. Particularly in the highway deployment scenario, combining PV and BESS yields a 72% reduction in greenhouse gas emissions. Overall, our study demonstrates that integrating renewable generation and storage into fast-charging infrastructure in Barcelona is both economically viable and environmentally beneficial.

Designs doi: 10.3390/designs9060124

Authors: Abdelmajid Larhlida Abdelali Mana Aicha Fathi Badr Ouhammou Zouhair Sadoune Abdelmajid Jamil

This thorough study looks at the use of machine learning (ML) techniques to forecast energy usage in buildings, with an emphasis on mosques. As energy use has a greater impact on both the environment and the economy, it is becoming increasingly important to optimize energy usage in buildings, especially for religious organizations such as mosques. The study goes into a variety of ML methods and models, including neural networks, regression models, decision trees, and clustering algorithms, each customized to a distinct difficulty in energy management. The paper evaluates the efficacy of several ML techniques, noting their merits, shortcomings, and potential applications. Additionally, it investigates the impact of climate, mosque design, occupancy patterns, and geographical variables on energy use. To achieve accurate energy consumption projections, rigorous data collecting, pre-processing, and model validation procedures are required. The paper also discusses important data sources and methodologies for mosque-specific energy analysis. Furthermore, the study emphasizes the practical benefits of applying ML in energy prediction, such as cost savings, increased environmental sustainability, and better resource allocation. This study’s ramifications extend beyond mosques, providing useful insights into energy management in buildings in general. By summarizing the current state of ML applications in mosque energy prediction, this study is an important resource for researchers, decision-makers, and energy management practitioners, paving the way for future advancements and the adoption of more sustainable energy practices in religious institutions.

Designs doi: 10.3390/designs9060123

Authors: Simon Schneider Thomas Zelger Raphael Drexel Manfred Schindler Paul Krainer José Baptista

In recent years, Positive Energy Districts (PEDs) have been interpreted in many—and often conflicting—ways. We recast PEDs as a vehicle for verifiable climate neutrality and present a declaration-ready assessment that integrates (i) a cumulative, science-based GHG budget per m2 gross floor area (GFA), (ii) full life-cycle accounting, and (iii) time-resolved conversion factors that include everyday motorized individual mobility and quantify flexibility. Two KPIs anchor the framework: the cumulative GHG LCA balance (2025–2075) against a maximum compliant budget of 320 kgCO2e·m−2GFA and the annual primary energy balance used to declare PED status with or without mobility. We follow EN 15978 and apply time-resolved emission factors that decline to zero by 2050. Its applicability is demonstrated on six Austrian districts spanning new builds and renovations, diverse energy systems, densities, and mobility contexts. The baseline scenarios show heterogeneous outcomes—only two out of six meet both the cumulative GHG budget and the positive primary energy balance—but design iterations indicate that all six districts can reach the targets with realistic, ambitious packages (e.g., high energy efficiency and flexibility, local renewables, ecological building materials, BESS/V2G, and mobility electrification). Hourly emission factors and flexibility signals can lower import-weighted emission intensity versus monthly or annual factors by up to 15% and reveal seasonal import–export asymmetries. Built on transparent, auditable rules and open tooling, this framework both diagnoses performance gaps and maps credible pathways to compliance—steering PED design away from project-specific targets toward verifiable climate neutrality. It now serves as the basis for the national labeling/declaration scheme klimaaktiv “Climate-Neutral Positive Energy Districts”.

Designs doi: 10.3390/designs9050122

Authors: Salma Nabli Gilde Vanel Tchane Djogdom Martin J.-D. Otis

To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated.

Designs doi: 10.3390/designs9050121

Authors: Fusaomi Nagata Tomoaki Morimoto Keigo Watanabe Maki K. Habib

Recently, deep learning models such as convolutional neural networks (CNNs), convolutional autoencoders (CAEs), CNN-based support vector machines (SVMs), YOLO, fully convolutional networks (FCNs), fully convolutional data descriptions (FCDDs) and so on have been applied to defect detections and anomaly detections of various kinds of industrial products, materials and systems. In those models, downsampled images, including target features, are used for training and testing. On the other hand, although various types of anomaly detection systems based on time series data such as sounds and vibrations are also applied to manufacturing processes, complicated conversions to the frequency domain are basically needed in conventional approaches. This paper addresses an important industrial problem for detecting anomalies in machine tools at low cost using audio data. Intelligent anomaly diagnosis systems for computer numerical control (CNC) machine tools are considered and proposed, in which raw time-series data without the need of conversion to the frequency domain can be directly used for training and testing. As for the NN models for comparison, conventional shallow NN, RNN and 1D CNN are designed and trained using the nine kinds of mechanical sounds. Classification results of test sound block (SB) data by the three models are shown. Then, an autoencoder (AE) is designed and considered for the identifier by training it using only normal SB data of a machine tool. One of the technical needs in dealing with time-series data such as SB data by NNs is how to clearly visualize and understand anomalous regions in concurrence with identification. Finally, we propose the SB data-based FCDD model to meet this need. Basic performance of the SB data-based FCDD model is evaluated in terms of anomaly detection and concurrent visualization of understanding.

Designs doi: 10.3390/designs9050120

Authors: Ulyana Konopada Giulia Pascoletti Mauro Corrado Elisabetta Maria Zanetti

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems.

Designs doi: 10.3390/designs9050119

Authors: Giampiero Donnici Giulio Galiè Leonardo Frizziero

The increasing digitization of design processes has profoundly transformed the role of sketching in industrial design, integrating it with advanced technologies such as artificial intelligence (AI). This paper presents an innovative methodology for automotive design that combines the intuitive power of sketching, both traditional and digital, with the structured approach of Stylistic Design Engineering (SDE) and the capabilities of generative AI. The study investigates how AI can enhance and accelerate key phases of the design process, including ideation, style analysis, and development, by generating design variations and optimizing the transition from initial concepts to re-fined digital models. Through case studies integrating manual sketching, digital tools, and AI, this research demonstrates how this approach not only pre-serves the designer’s creativity but also improves efficiency and precision. The core contribution of this work lies in the development of a circular and iterative framework that balances creative exploration with methodological rigor, enabling significant reductions in time and cost while fostering innovation. The results underscore the potential of this integrated approach to drive a paradigm shift in automotive design and broader industrial design practices. By bridging creative ideation and systematic development, this methodology offers valuable applications not only in aesthetic design but also in engineering design contexts, where sketching can aid in defining and optimizing functional solutions from the earliest stages.

Designs doi: 10.3390/designs9050118

Authors: Nicholas Boys Smith Nikos A. Salingaros

Large language models (LLMs) judge three pairs of architectural design proposals which have been independently surveyed by opinion polls: department store buildings, sports stadia, and viaducts. A tailored prompt instructs the LLM to use specific emotional and geometrical criteria for separate evaluations of image pairs. Those independent evaluations agree with each other. In addition, a streamlined evaluation using a single descriptor “friendliness” yields the same results while offering a rapid screening measure. In all cases, the LLM consistently selects the more human-centric design, and the results align closely with independently conducted public opinion poll surveys. This agreement is significant in improving designs based upon human-centered principles. AI helps to illustrate the correlational effect: living geometry → positive-valence emotions → public preference. The AI-based model therefore provides empirical evidence for a deep biological link between geometric structure and human emotion that warrants further investigation. The convergence of AI judgments, neuroscience, and public sentiment highlights the diagnostic power of criteria-driven evaluations. With intelligent prompt engineering, LLM technology offers objective, reproducible architectural assessments capable of supporting design approval and policy decisions. A low-cost tool for pre-occupancy evaluation unifies scientific evidence with public preference and can inform urban planning to promote a more human-centered built environment.

Designs doi: 10.3390/designs9050117

Authors: Anna A. Kamenskikh Anastasia P. Bogdanova Yuriy O. Nosov Yulia S. Kuznetsova

In this study, the behavior of the spherical bearing component of the L-100 bridge part (AlfaTech LLC, Perm, Russia) is considered within the framework of a finite element model. The influence of the pattern of the coupling of the antifriction interlayer with the lower steel plate on the operation of the part is examined in terms of ideal contact, full adhesion, and frictional contact. The thickness of the antifriction interlayer varied from 4 to 12 mm. The dependencies of the contact parameters and the stress–strain state on the thickness were determined. Structurally modified polytetrafluoroethylene (PTFE) without AR-200 fillers was considered the material of the antifriction interlayer. The gradual refinement of the behavioral model of the antifriction material to account for structural and relaxation transitions was carried based on a wide range of experimental studies. The elastic–plastic and primary viscoelastic models of material behavior were constructed based on a series of homogeneous deformed-state experiments. The viscoelastic model of material behavior was refined using data from dynamic mechanical analysis over a wide temperature range [−40; +80] °C. In the first approximation, a model of the deformation theory of plasticity with linear elastic volumetric compressibility was identified. As a second approximation, a viscoelasticity model for the Maxwell body was constructed using Prony series. It was established that the viscoelastic model of the material allows for obtaining data on the behavior of the part with an error of no more than 15%. The numerical analog of the construction in an axisymmetric formulation can be used for the predictive analysis of the behavior of the bearing, including when changing the geometric configuration. Recommendations for the numerical modeling of the behavior of antifriction layer materials and the coupling pattern of the bearing elements are given in this work. A spherical bearing with an antifriction interlayer made of Arflon series material is considered for the first time.

Designs doi: 10.3390/designs9050116

Authors: Kyriaki Aidinli Prodromos Minaoglou Panagiotis Kyratsis Nikolaos Efkolidis

Furniture is an integral part of daily life. Its comfort and usability are key factors that define its success. In recent years, there has been increasing demand for applications that drive businesses toward Industry 4.0. These applications aim to improve productivity through greater automation in both 3D modeling and fabrication processes. This research aims to develop a Computer Aided Design (CAD) platform that automates the design and manufacturing of furniture. The platform is based on visual programming using Grasshopper 3D™ and provides a solid foundation for processing different geometric shapes. These shapes can be customized according to the user’s preferences. The platform’s innovation lies in its ability to process complex geometries with a fully automated algorithm. Once the initial parameters are set, the algorithm generates the results. The input data includes an initial geometry, which can be highly complex. Additionally, a set of construction parameters is introduced, leading to multiple alternative design solutions based on the same initial geometry. The designer and user can select their final choice, and all resulting design and manufacturing outcomes are automatically generated. These outcomes include 3D part models, 3D assembly files, Bill of Materials, G-code for CNC machining, and nesting capabilities for improved material efficiency. The platform ensures high-quality performance. The results of the study show that the platform successfully works with different geometries. Moreover, the study is significant as the Industry 4.0 transformation moves toward more automated design processes.

Designs doi: 10.3390/designs9050115

Authors: Taher Maatallah

This study presents the development and validation of a high-efficiency optical interface designed for ultra-high-concentration photovoltaic (UHCPV) systems, with a focus on enabling clean and sustainable solar energy conversion. A Fresnel lens serves as the primary optical concentrator in a novel system architecture that integrates advanced optical design with system-level thermal management. The proposed modeling framework combines detailed 3D ray tracing with coupled thermal simulations to accurately predict key performance metrics, including optical concentration ratios, thermal loads, and component temperature distributions. Validation against theoretical and experimental benchmarks demonstrates high predictive accuracies within 1% for optical efficiency and 2.18% for thermal performance. The results identify critical thermal thresholds for long-term operational stability, such as limiting mirror temperatures to below 52 °C and photovoltaic cell temperatures to below 130 °C. The model achieves up to 89.08% optical efficiency, with concentration ratios ranging from 240 to 600 suns and corresponding focal spot temperatures between 37.2 °C and 61.7 °C. Experimental benchmarking confirmed reliable performance, with the measured results closely matching the simulations. These findings highlight the originality of the coupled optical–thermal approach and its applicability to concentrated photovoltaic design and deployment. This integrated design and analysis approach supports the development of scalable, clean photovoltaic technologies and provides actionable insights for real-world deployment of UHCPV systems with minimal environmental impact.

Designs doi: 10.3390/designs9050114

Authors: Zhiliang Sun Wei Wang Hanghang Liu

By critically reviewing pseudo-static methods, it is demonstrated that approximating the earth pressure on a short heel’s vertical face (V-plane) using the Rankine solution for long-heel walls induces a negligible error. A finite element analysis is deployed to validate the pseudo-static results, with dynamic simulations incorporating 1–5 Hz sinusoidal seismic excitations to probe the resonance effects. The key results show that disregarding the impact of layered backfill placement on the initial stress states leads to non-conservative estimates of active earth pressure. Furthermore, the point of application of earth pressure rises significantly during strong shaking, and although the transient safety factors against sliding and overturning may fall below 1.0 during seismic events, the residual deformation analysis suggests that this does not necessarily lead to collapse. A significant amplification of bending moments and greater reductions in post-earthquake safety factors occur when the input frequency approaches the natural frequency of a wall. Finally, the paper proposes resonance prevention strategies for the seismic design of cantilever retaining walls, a methodology incorporating construction effects into the initial stress field modeling, and recommendations for selecting effective safety factors.

Designs doi: 10.3390/designs9050113

Authors: Kristaq Hazizi Suleiman Erateb Arnaldo Delli Carri Joseph Jones Sin Hang Leung Stefania Sam Ronnie Yau

This study addresses a documented gap in detailed, cost-effective, and performance-validated electric vehicle (EV) powertrain solutions. It presents the complete design, construction, and simulation-based validation of a high-efficiency electric powertrain for a Shell Eco-marathon Urban Concept vehicle. Novel contributions of this work include a unique drivetrain architecture: a BLDC motor with a modular two-stage chain drive and a custom lithium-ion battery pack. The design is optimized for compactness and reliability under stringent budget and packaging constraints. A comprehensive Simulink-based vehicle dynamics model was developed for robust validation. This model enabled the estimation of energy consumption, torque profiles, and battery State of Charge under realistic drive cycles. The system demonstrated a remarkably low energy consumption under competition conditions, signifying high efficiency with <50 Wh/km consumption and full compliance with technical regulations. Furthermore, the hardware is thoroughly documented with detailed build instructions, CAD models, and a full bill of materials. This promotes reproducibility. This research offers a validated, low-cost, and replicable electric powertrain. It provides a transferable framework for future Shell Eco-marathon teams and advances lightweight, cost-effective solutions for real-world low-speed electric mobility applications, such as micro-EVs and urban delivery vehicles.

Designs doi: 10.3390/designs9050112

Authors: Antreas Kantaros Theodore Ganetsos Evangelos Pallis Michail Papoutsidakis

In the published paper [...]

Designs doi: 10.3390/designs9050111

Authors: Dimitrios Siakas Kerstin Siakas Georgios Lampropoulos

The aim of this study is to examine existing positive energy district (PED) initiatives by using an explanatory research approach for gaining insight, identifying patterns, clarifying underlying processes, exploring cause-and-effect relationships, and explaining phenomena in a greater depth. Specifically, studies from the existing literature that have explored multiple PEDs were analyzed. Current challenges, barriers, and obstacles, as well as success factors, good practices, and policy guidelines are thoroughly investigated, evaluated, categorized and compared to unveil lessons learnt from diverse existing international projects for turning urban areas into self-sustainable and greener urban neighborhoods. The proposed framework aims to reveal the required processes for successful PED creation and operation. It provides an overview of the current state of the art and enhances comprehension and know-how about the processes needed for the successful adoption and integration of PEDs based on lessons learnt from global challenges and success stories.

Designs doi: 10.3390/designs9050110

Authors: Fahim Ullah Oluwole Olatunji Siddra Qayyum Rameesha Tanveer

The global aging population, particularly those aged 60 and above, is increasingly vulnerable to communicable diseases. Building ventilation (BV) plays a key role in residential aged-care (RAC) facilities, where COVID-19 has had a significant impact. This study systematically reviews the published literature to examine the influence of BV systems (BVSs) on airborne disease (COVID-19) transmission in RACs and recommends strategies to protect vulnerable residents. Using the PRISMA framework, articles published in the last decade were sourced from Scopus, Web of Science, and PubMed. Bibliometric analyses revealed key research clusters on risk factors, transmission, facilities and services, and gender-based and retrospective studies. Australia, the USA, Africa, and the UK have made the most scholarly contributions to this field. Three main research areas emerged: BVS functionality, ventilation’s role in COVID-19 transmission, and spatial building design for effective airflow. Findings reveal that inadequate ventilation and poor indoor air quality are major contributors to disease spread, further influenced by ventilation rate, airflow, temperature, humidity, and air distribution. A hybrid ventilation design that integrates natural and mechanical systems with technologies such as HEPA filters, UVGI, and HVAC is recommended in the current study. In addition, building form and layout should incorporate spatial, engineering, administrative, and hierarchical controls in line with sustainable ventilation design guidelines. This study adds to the growing body of knowledge on the roles of ventilation and design in infection control. It offers practical recommendations for architects, RAC managers, government agencies, and policymakers involved in designing and managing RACs to reduce the risk of communicable disease transmission.

Designs doi: 10.3390/designs9050109

Authors: Paweł Turek

Additive Manufacturing (AM) techniques are rapidly emerging as leading technologies for the creation of complex models [...]

Designs doi: 10.3390/designs9050108

Authors: Lorenzo De Santanna Riccardo Malacrida Gianpiero Mastinu Massimiliano Gobbi

This study investigates the application of Multi-Objective Reinforcement Learning–Dominance-Based (MORL–DB) method to the optimal design of complex mechanical systems. The MORL–DB method employs a Deep Deterministic Policy Gradient (DDPG) agent to identify the optimal solutions of the multi-objective problem. By adopting the k-optimality metric, which introduces an optimality ranking within the Pareto-optimal set of solutions, a final design solution can be chosen more easily, especially when considering a large number of objective functions. The method is successfully applied to the elasto-kinematic optimisation of a double wishbone suspension system, featuring a multi-body model in ADAMS Car. This complex design task includes 30 design variables and 14 objective functions. The MORL–DB method is compared with two other approaches: the Moving Spheres (MS) method, specifically developed for spatial design tasks, and the genetic algorithm with k-optimality-based sorting (KEMOGA). Comparative results show that the MORL–DB method achieves solutions of higher optimality while requiring significantly fewer objective function evaluations. The results demonstrate that the MORL–DB method is a promising and sample-efficient alternative for multi-objective optimisation, particularly in problems involving high-dimensional design spaces and expensive objective function evaluations.

Designs doi: 10.3390/designs9050107

Authors: Jeremy Sarpong Khalil Khanafer Mohammad Sheikh

This study investigates the mechanical performance of two prosthetic forelimb designs for dogs—one with a solid structure and the other with a perforated structure—using Finite Element Analysis (FEA). Both models were analyzed under static loading conditions representing approximately 60% of a dog’s body weight, the typical load borne by the forelimbs. The prosthetics were modeled with ABS plastic, a widely used 3D printing material, and evaluated for Von Mises stress, total deformation, elastic strain, and factor of safety. The analysis showed that both models remained within the elastic limit of the material, indicating that no permanent deformation would occur under the applied loads. The Solid Model demonstrated a significantly higher factor of safety (14) and lower deformation, confirming its structural strength but also highlighting excessive rigidity, increased material use, and higher cost. In contrast, the Perforated Model exhibited slightly higher localized stresses and a lower factor of safety (3.01), yet it still met essential safety requirements while providing greater compliance, flexibility, and material efficiency. These attributes are desirable for comfort, adaptability, and practicality in veterinary applications. Although its long-term durability requires further evaluation, the Perforated Model strikes a more effective balance between safety, comfort, and sustainability. Based on these findings, the perforated design is considered the more suitable option for canine prosthetic development. Future work will extend the analysis to dynamic loading scenarios, such as walking and running, to better simulate real-world performance.

Designs doi: 10.3390/designs9050106

Authors: Xinnian Wang Sina Rastegarzadeh Yayue Pan Jida Huang

Numerous studies have examined various geometric designs in cellular structures, yet the role of cross-sectional geometry remains underexplored. Cross-sections significantly influence the effective material properties of architected materials, where stress concentrations at junctions can reduce structural strength. This study investigates how different cross-sections affect energy absorption efficiency in both bending- and stretching-dominated cellular structures. Five classes of lattice structures, each designed with four distinct cross-sections, were fabricated using a custom stereolithography printer. Mechanical performance—specifically energy absorption and energy absorption efficiency—was evaluated through physical simulation and experimental testing. The results show that selecting optimal cross-sections can enhance yield stress by an average of 35% for cubic, 39% for BCC, 22% for BCCZ, and 41% for FCC structures. These findings demonstrate the critical impact of cross-sectional geometry on mechanical behavior. Both experimental and finite element analysis-based homogenization approaches were employed to validate results. The study proposes cross-section design guidelines aimed at optimizing strength-to-weight ratios, offering valuable insights for the development of high-performance mechanical metamaterials.

Designs doi: 10.3390/designs9050105

Authors: Almira Zhilkashinova Igor Ocheredko Madi Abilev

In the presented work, the main challenge of small hydropower plants—converting low river flow velocities into high generator rotations—is investigated. It was established that applying the flow acceleration effect during interaction with surfaces makes it possible to increase the power output of a small hydropower plant by up to 25%, which corresponds to the level of an innovative solution. Stationary flow amplifiers and their influence on the dynamic interaction of blades were studied. It was revealed that the use of the amplification effect in paired configurations contributes to achieving a multiplicative effect. The potential of small hydropower plants was analytically evaluated, taking into account their dimensions and gear systems. The study was carried out using the method of computational fluid dynamics (CFD), which enables the modeling of complex hydrodynamic processes. Based on the developed three-dimensional model of the object and its discretization into a computational mesh, boundary conditions were set, and the finite volume method was applied to solve the Navier–Stokes equations. To account for turbulent flows, the k-epsilon turbulence model was employed.

Designs doi: 10.3390/designs9050104

Authors: Giuseppe Loprencipe Laura Moretti Mario Saltaren Daniel

The use of Reclaimed Asphalt Pavement (RAP) in asphalt mixtures offers environmental and economic advantages by reducing reliance on virgin aggregates and minimizing construction waste. However, the aged binder in RAP increases mixture stiffness, which can compromise fatigue resistance. This systematic review evaluates the influence of RAP content on fatigue performance compared to conventional mixtures, with a focus on the Indirect Tensile Test (IDT) as the primary assessment method. Following the parameters of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five studies published between 2014 and 2024 were identified through searches in Web of Science, ScienceDirect, ASCE, and Scopus. Study quality was assessed using the Cochrane Risk of Bias tool. The results indicate that although RAP enhances rutting resistance, higher contents (>30%) often lead to reduced fatigue performance due to binder hardening and reduced mixture flexibility. The incorporation of rejuvenators—such as heavy paraffinic extracts—and modifiers, including high-modulus agents, polymers, and epoxy binders, can partially restore aged binder properties and improve performance. Sustainable innovations, such as lignin-based industrial by-products and warm-mix asphalt technologies, show promise in balancing mechanical performance with reduced environmental impact. Variability in material sources, modification strategies, and test protocols limits direct comparability among studies, underscoring the need for standardized evaluation frameworks. Overall, this review highlights that optimizing RAP content and selecting effective rejuvenation or modification strategies are essential for achieving durable, cost-effective, and environmentally responsible asphalt pavements. Future research should integrate advanced laboratory methods with performance-based design to enable high RAP utilization without compromising fatigue resistance.

Designs doi: 10.3390/designs9050103

Authors: Athanasios Konstantakopoulos Nikolaos Kladovasilakis Georgios E. Stavroulakis

An abdominal aortic aneurysm (AAA) poses a significant risk of arterial wall rupture, which critically endangers the patient’s life. To address this condition, an endovascular aneurysm repair (EVAR) is required, involving the insertion and expansion of a stent-graft within the aorta, to support and isolate the weakened vessel wall. In this context, this article aims to approach the problem from a mechanical perspective and to simulate the expansion and deployment procedure realistically, utilizing the Finite Element Analysis (FEA). The process initiates with the computation evaluation of the aortic structure in order to identify critical regions of stress and strain in an aneurysmatic aortic region. Then, a customized 3D-designed stent graft model was developed for the aorta and positioned properly. Applying all the necessary boundary conditions, a complex nonlinear FEA was conducted until the stent-graft expanded radially, reaching a final diameter 25% larger than the aorta’s vessel wall while withstanding mean stress and strain values close to 400 MPa and 1.5%, respectively. Finally, the mechanical behavior of the stent-graft and its interaction with the internal aortic wall, during the expansion process, was evaluated, and the extracted results were analyzed.

Designs doi: 10.3390/designs9050102

Authors: Antreas Kantaros Theodore Ganetsos Evangelos Pallis Michail Papoutsidakis

The increased importance of sustainability imperatives has required a profound reconsideration of the interaction between materials, manufacturing, and design fields. Biomimetic smart materials such as shape-memory polymers, hydrogels, and electro-active composites represent an opportunity to combine adaptability, responsiveness, and ecological intelligence in systems and products. This work reviews the confluence of such materials with leading-edge manufacturing technologies, notably additive and 4D printing, and how their combining opens the door to the realization of time-responsive, low-waste, and user-adaptive design solutions. Through computational modeling and mathematical simulations, the adaptive performance of these materials can be predicted and optimized, supporting functional integration with high precision. On the basis of case studies in regenerative medicine, architecture, wearables, and sustainable product design, this work formulates the possibility of biomimetic strategies in shifting design paradigms away from static towards dynamic, from fixed products to evolvable systems. Major material categories of stimuli-responsive materials are systematically reviewed, existing 4D printing workflows are outlined, and the way temporal design principles are revolutionizing production, interaction, and lifecycle management is discussed. Quantitative advances such as actuation efficiencies exceeding 85%, printing resolution improvements of up to 50 μm, and lifecycle material savings of over 30% are presented where available, to underscore measurable impact. Challenges such as material scalability, process integration, and design education shortages are critically debated. Ethical and cultural implications such as material autonomy, transparency, and cross-cultural design paradigms are also addressed. By identifying existing limitations and proposing a future-proof framework, this work positions itself within the ongoing discussion on regenerative, interdisciplinary design. Ultimately, it contributes to the advancement of sustainable innovation by equipping researchers and practitioners with a set of adaptable tools grounded in biomimicry, computational intelligence, and temporal design thinking.

Designs doi: 10.3390/designs9050101

Authors: Paweł Turek Piotr Bielarski Alicja Czapla Hubert Futoma Tomasz Hajder Jacek Misiura

Due to the rapid advancements in coordinate measuring systems, data processing software, and additive manufacturing (AM) techniques, it has become possible to create copies of existing models through the reverse engineering (RE) process. However, the lack of precise estimates regarding the accuracy of the RE process—particularly at the measurement, reconstruction, and computer-aided design (CAD) modeling stages—poses significant challenges. Additionally, the assessment of dimensional and geometrical errors during the manufacturing stage using AM techniques limits the practical implementation of product replicas in the industry. This paper provides an estimation of the errors encountered in the RE process and the AM stage of various models. It includes examples of an electrical box, a lampshade for a standing lamp, a cover for a vacuum unit, and a battery cover. The geometry of these models was measured using a GOM Scan 1 (Carl Zeiss AG, Jena, Germany). Following the measurement process, data processing was performed, along with CAD modeling, which involved primitive detection, profile extraction, and auto-surface methods using Siemens NX 2406 software (Siemens Digital Industries, Plano, TX, USA). The models were produced using a Fortus 360-mc 3D printer (Stratasys, Eden Prairie, MN, USA) with ABS-M30 material. After fabrication, the models were scanned using a GOM Scan 1 scanner to identify any manufacturing errors. The research findings indicated that overall, 95% of the points representing reconstruction errors are within the maximum deviation range of ±0.6 mm to ±1 mm. The highest errors in CAD modeling were attributed to the auto-surfacing method, overall, 95% of the points are within the average range of ±0.9 mm. In contrast, the lowest errors occurred with the detect primitives method, averaging ±0.6 mm. Overall, 95% of the points representing the surface of a model made using the additive manufacturing technology fall within the deviation range ±0.2 mm on average. The findings provide crucial insights for designers utilizing RE and AM techniques in creating functional model replicas.