Search Results (272)

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

The prediction of tribological behavior in engineering systems based on output data streams is often considered empirical, as it is difficult to fully describe the underlying processes using closed-form mathematical models. For instance, accurately predicting the coefficient of friction (COF) across the three major lubrication regimes using a single model remains elusive. Machine learning (ML) offers a powerful data-driven approach for modeling complex tribological processes by learning nonlinear relationships between operating conditions and performance metrics. This work develops a high-accuracy deep learning model for predicting the COF in lubricated tribosystems across boundary, mixed, and hydrodynamic lubrication regimes. Extensive experimental data were collected using a ball-on-disk tribometer, which generated a large and diverse dataset spanning multiple materials, lubricants, temperatures, loads, and sliding speeds and covered the full Stribeck curve. These data were then used to train and optimize a neural network capable of accurately reproducing frictional transitions across lubrication regimes. The breadth of the dataset not only allows for the full Stribeck behavior to be captured, but also enables generalization across distinct tribosystems. The resulting model demonstrates strong predictive performance and is deployed using a graphical user interface as a practical tool for estimating COF in ball-on-disk lubricated sliding contacts. Full article

Dynamic stress data are essential for evaluating structural fatigue life. To address the challenges of complex test data formats, low data reading efficiency, and insufficient visualization, this study systematically analyzes the .raw and .sie file formats from IMC and HBM data acquisition systems and proposes a unified parsing approach. A lightweight .dac format is designed, featuring a “single-channel–single-file” storage strategy that enables rapid, independent retrieval of specific channels and seamless cross-platform sharing, effectively eliminating the inefficiency of the .sie format caused by multi-channel coupling. Based on Python v3.11, an automated format conversion tool and a PyQt5-based visualization platform are developed, integrating graphical plotting, interactive operations, and fatigue strength evaluation functions. The platform supports stress feature extraction, rainflow counting, Goodman correction, and full life-cycle fatigue damage assessment based on the Palmgren–Miner rule. Experimental results demonstrate that the proposed system accurately reproduces both time- and frequency-domain features, with equivalent stress deviations within 2% of nCode results, and achieves a 7–8× improvement in file loading speed compared with the original format. Furthermore, multi-channel scalability tests confirm a linear increase in conversion time (R² > 0.98) and stable throughput across datasets up to 10.20 GB, demonstrating strong performance consistency for large-scale engineering data. The proposed approach establishes a reliable data foundation and efficient analytical tool for fatigue life assessment of structures under complex operating conditions. Full article

Several commercial seismometers now support CSTP, the real-time communication protocol used in the China Earthquake Early Warning System, but there is still no simple, flexible, and low-cost solution to archive CSTP streams or integrate them into existing data processing systems. In this study, we design and implement SharpCEEWPServer, a lightweight, out-of-the-box graphical server that integrates client management, real-time data reception, visualization, and archiving, and can, via RingServer, convert CSTP real-time streams into widely supported SeedLink streams. Hardware compatibility is evaluated using four commercial CSTP-capable instruments, a forwarding chain is built to assess forwarding functionality and reliability, and concurrency performance is tested using simulated networks with different station counts. The stability under network impairment scenarios and the performance of the forwarding system were also analyzed. The results show that the server can reliably receive and forward real-time data streams, and that laptop-class hardware is sufficient to withstand the load imposed by an M7.0 earthquake scenario when receiving real-time streams from 1000 three-component seismometers. However, the current version’s latency performance can only meet the needs of non-early warning networks. Overall, the proposed server significantly lowers the deployment and usage threshold for new CSTP-capable instruments and provides an efficient, low-cost integration solution for temporary networks in earthquake emergency response and seismic arrays. Full article

In the elastic–plastic analysis of structures, the deformation problem of cantilever beams is a classical problem, in which it is usually assumed that the material constituting the beam has an identical elastic modulus and identical yield strength when it is tensioned and compressed. These characteristics are manifested graphically as the symmetry of tension and compression. In this work, we will give up the general assumption and consider that the material has the property of tension–compression asymmetry, that is, the material presents different moduli in tension and compression and different yield strengths in tension and compression. First, the elastic–plastic response of the cantilever beam with a concentrated force acting at the fixed end in the loading stage is theoretically analyzed. When the plastic hinge appears at the fixed end, the maximum deflection at the free end is derived, and in the unloading stage the residual deflection at the free end is also given. At the same time, the theoretical solution obtained is validated by the numerical simulation. The results indicate that when considering the tension–compression asymmetry of materials, the plastic zone length from the fixed end no longer keeps the classical value of 1/3 and will become bigger; the tension–compression asymmetry will enlarge the displacement during the elastic–plastic response; and the ultimate deflection in loading and the residual deflection in unloading are both greater than the counterparts in the classical problem. The research results provide a theoretical reference for the fine analysis and optimal design of cantilever beams. Full article

Service design often involves using diverse business and process modelling notations to represent strategic and operational aspects of services. Although complementary, no modelling environment currently enables integrated use of these notations. This paper addresses this gap by proposing a model-driven solution that supports multiple modelling notations within a unified environment. The research is guided by the following question: To what extent can a modelling environment that integrates multiple business and process modelling notations benefit service designers? To answer it, the study adopts Design Science Research (DSR) methodology and develops a prototype integrating several graphical Domain-Specific Languages (DSLs), along with mechanisms for model transformation, traceability, and validation. The prototype was evaluated through a two-phase process: (1) a laboratory case study applying the double diamond model of service design to a real-world scenario, and (2) an empirical study involving nine service design professionals who assessed the usability of the tool, efficiency, and completeness of generated models. Results show that integrating heterogeneous modelling notations through Model-Driven Engineering (MDE) can reduce modelling effort by up to 36.4% and generate models with up to 97.7% completeness, demonstrating not only technical benefits but also contributions to the well-being of designers by reducing cognitive load, fostering consistency, and improving communication among the stakeholders involved in the designing process. Full article

The agility and scalability of modern industrial control systems critically depend on seamlessly integrating of heterogeneous field devices. However, this integration is fundamentally hindered at the communication level by the diversity of proprietary industrial protocols, which creates data silos and impedes the implementation of advanced control strategies. To overcome this communication barrier, this paper presents GMGbox, a graphical modeling-based protocol adaptation engine. GMGbox encapsulates protocol parsing and data conversion logic into reusable graphical components, effectively bridging the communication gap between diverse industrial devices and control applications. These components are orchestrated by a graphical modeling program engine that enables codeless protocol configuration and supports dynamic loading of protocol dictionary templates to integrate protocol variants, thereby ensuring high extensibility. Experimental results demonstrate that GMGbox can concurrently and reliably parse multiple heterogeneous industrial communication protocols, such as Mitsubishi MELSEC-QNA, Siemens S7-TCP, and Modbus-TCP. Furthermore, it allows engineers to visually adjust protocol algorithms and parameters online, significantly reducing development complexity and iteration time. The proposed engine provides a flexible and efficient data communication backbone for building reconfigurable industrial control systems. Full article

High-resolution LiDAR-derived three-dimensional (3D) digital outcrop models are crucial for detailed geological analysis. However, their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing significant visualization challenges. Although Level of Detail (LOD) techniques are well-established in Geographic Information Systems (GISs) and computer graphics, they still require customized design to address the unique characteristics of geological outcrops. This paper presents an automated method for constructing and visualizing LOD models specifically tailored to high-resolution LiDAR outcrops. The workflow begins with segmenting the single-body model based on texture coverage, followed by building an adaptive LOD tile pyramid for each segment using a pseudo-quadtree approach. The proposed LOD construction method incorporates several innovative components: segmentation based on texture coverage, an adaptive LOD tile pyramid using a pseudo-quadtree, and a feature-preserving mesh simplification algorithm that includes vertex sharpness constraint and boundary freezing strategy to maintain critical geological features. For visualization, a dynamic multi-scale loading and rendering mechanism is implemented using an LOD index with the OpenSceneGraph (OSG) engine. The results demonstrate that the proposed method effectively addresses the bottleneck of rendering massive outcrop models. The models loading time and average memory usage were reduced by more than 90%, while the average display frame rate reached around 60 FPS. It enables smooth, interactive visualization and provides a robust foundation for multi-scale geological interpretation. Full article

In immersive digital devices, high environmental complexity can lead to rendering delays and loss of interactive details, resulting in a fragmented experience. This paper proposes a lightweight NeRF (Neural Radiance Fields) modeling and multimodal perception fusion method. First, a sparse hash code is constructed based on Instant-NGP (Instant Neural Graphics Primitives) to accelerate scene radiance field generation. Second, parameter distillation and channel pruning are used to reduce the model’s size and reduce computational overheads. Next, multimodal data from a depth camera and an IMU (Inertial Measurement Unit) is fused, and Kalman filtering is used to improve pose tracking accuracy. Finally, the optimized NeRF model is integrated into the Unity engine, utilizing custom shaders and asynchronous rendering to achieve low-latency viewpoint responsiveness. Experiments show that the file size of this method in high-complexity scenes is only 79.5 MB ± 5.3 MB, and the first loading time is only 2.9 s ± 0.4 s, effectively reducing rendering latency. The SSIM is 0.951 ± 0.016 at 1.5 m/s, and the GME is 7.68 ± 0.15 at 1.5 m/s. It can stably restore texture details and edge sharpness under dynamic viewing angles. In scenarios that support 3–5 people interacting simultaneously, the average interaction response delay is only 16.3 ms, and the average jitter error is controlled at 0.12°, significantly improving spatial interaction performance. In conclusion, this study provides effective technical solutions for high-quality immersive interaction in complex public scenarios. Future work will explore the framework’s adaptability in larger-scale dynamic environments and further optimize the network synchronization mechanism for multi-user concurrency. Full article

Structural Topology Optimisation (STO) plays a critical role in computational engineering, enabling the creation of material-efficient, performance-driven structures. However, dynamic STO workflows, particularly those involving time-varying or seismic excitations, are often inaccessible to architects and engineers due to their reliance on standalone solvers, large-scale data handling, and advanced programming skills. This paper introduces a Computer-Aided Design (CAD)-embedded, time-dependent STO framework built upon a modular, adjoint-based optimisation core integrated into a Visual Programming Language (VPL) interface. Implemented within a parametric CAD environment through a custom C# component, the framework embeds a MATLAB-based solver to support geometry definition, boundary condition control, and dynamic finite element analysis under harmonic and seismic loading. The resulting Graphical User Interface (GUI) lowers technical barriers by enabling users to iteratively configure STO parameters, manage meshing, and visualise real-time results. Case studies on tall building façades under earthquake excitation validate the framework’s ability to minimise displacement at targeted Degrees of Freedom (DOFs), dynamically adapt material distributions, and enhance structural resilience. By bridging high-fidelity computational methods with accessible visual workflows, the proposed system advances the integration of dynamic STO into both architectural and engineering practice. Full article

The increasing need for precise testing in battery and electronic component development has driven the demand for modular and scalable laboratory systems. This paper presents the design and initial implementation of a LabVIEW-based measurement system tailored for ISO/IEC 17025-compliant testing environments. The system’s software architecture is modular and built around a Hardware Abstraction Layer, enabling the integration of various remotely controlled instruments, such as programmable power supplies, electronic loads, and climate chambers. LabVIEW’s object-oriented programming and multi-threaded execution environment allows synchronized control and real-time data acquisition. Test procedures are defined using a JSON-based sequence structure, supporting repeatable testing. A graphical editor provides an intuitive interface for configuring test steps, ensuring ease of use. The system is designed to support future expansion, including high-speed measurement modules and parallel test execution. This solution lays the foundation for a reliable and extensible automated testing platform that aligns with modern industrial and regulatory standards. Full article

In recent years, advancements in technologies related to hydrogen have facilitated the exploitation of this energy carrier in conjunction with renewable energies to meet the energy demands of diverse applications. This paper describes a pilot plant within the framework of a research and development (R&D) project aimed at utilizing hydrogen in both industrial and domestic sectors. To this end, this facility comprises six subsystems. Initially, a photovoltaic (PV) generator consisting of 48 panels is employed to generate electrical current from solar radiation. This PV array powers a proton exchange membrane (PEM) electrolyzer, which is responsible for producing green hydrogen by means of water electrolysis. The produced hydrogen is subsequently stored in a bottling storage system for later use in a PEM fuel cell that reconverts it into electrical energy. Finally, a programmable electronic load is utilized to simulate the electrical consumption patterns of various profiles. These physical devices exchange operational data with an open source supervisory system integrated by a set of Industry 4.0 (I4.0) and Internet of Things (IoT)-framed environments. Initially, Node-RED acts as middleware, handling communications, and collecting and processing data from the pilot plant equipment. Subsequently, this information is stored in MariaDB, a structured relational database, enabling efficient querying and data management. Ultimately, the Grafana environment serves as a monitoring platform, displaying the stored data by means of graphical dashboards. The system deployed with such I4.0/IoT applications places a strong emphasis on the continuous monitoring of the power inverter that serves as the backbone of the pilot plant, both from an energy flow and communication standpoint. This device ensures the synchronization, conversion, and distribution of electrical energy while simultaneously standing as a primary data source for the supervisory system. The results presented in this article describe the design of the system and provide evidence of its successful implementation. Full article

Descriptive geometry plays a fundamental role in developing spatial reasoning and geometric problem-solving skills in engineering education. This study investigates the comparative effectiveness of two instructional methodologies—Monge’s traditional projection system and the CADOP method, which integrates computer-aided design tools with orthographic projection principles. A quasi-experimental design was implemented with 90 undergraduate engineering students randomly assigned to two groups. Both groups followed the same instructional sequence and were evaluated using baseline surveys, rubric-based performance assessments, and post-training reflections. Quantitative analysis included mean comparisons, t-tests, and effect sizes, while inter-rater reliability confirmed scoring consistency. The results showed that CADOP students significantly outperformed those taught with Monge’s method across all criteria—conceptual under-standing, graphical accuracy, procedural consistency, and spatial reasoning—with very large effect sizes. Qualitative data indicated that CADOP enhanced clarity, efficiency, and confidence, while Monge promoted conceptual rigor but higher cognitive effort. The findings confirm that CADOP effectively reduces procedural complexity and cognitive load, supporting deeper spatial comprehension. Integrating CADOP with selected manual practices offers a balanced pedagogical approach for modernizing descriptive geometry instruction in engineering education. Full article

This paper presents a novel analytical framework for the design and understanding of class EF inverters under both optimal and non-optimal load conditions. Unlike conventional approaches that rely heavily on numerical simulations, the proposed method provides a fast, visual, and intuitive tool for analyzing inverter operation. Its effectiveness is demonstrated experimentally on a 15 MHz class EF inverter across three distinct load conditions, showing good agreement with theoretical predictions. To highlight the robustness and broad applicability of the approach, a class ${\Phi }_2$ inverter—a lumped-element analog of the class EF inverter—is also implemented and successfully analyzed. By combining theoretical insight, experimental validation, and generalization to alternative topologies, the proposed framework offers an efficient, accessible, and versatile tool for high-frequency resonant inverter design. Full article