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Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 12354

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Special Issue Editors

Dr. Mirosław Szczepanik

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Guest Editor

Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Interests: technical sciences, especially in the discipline of mechanics; development and application of computer methods, especially artificial intelligence methods, in application to technical systems; application of optimization methods mainly in the design of components for means of transport; design of rail vehicles, cars and aircraft

Dr. Arkadiusz Poteralski

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Guest Editor

Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Interests: develop the concept and methodology of the optimization for selected mechanical structures;optimization algorithms; artificial immune systems;evolutionary algorithmsprocedure for simultaneously optimization of shape, topology and distribution of different materials for the spatial structure

Special Issue Information

Dear Colleagues,

Mechanical structures, depending on their purpose, should meet many design assumptions. As a result, they should meet safety requirements related to their geometry, strength, and deformability. Additionally, mechanical constructions should often be ergonomic, lightweight, cheap to produce, and feasible with the availability of known production methods. They should therefore be optimal when meeting various criteria. In order to obtain optimal solutions, we use various systems and tools in the field of computational mechanics. We invite you to submit articles on modern methods for optimal design and their applications in mechanical engineering.

Topics:

Computational mechanics in solid-, fluid-, and biomechanics to achieve optimal design with the applications of the following:

Computational intelligence;
Artificial intelligence methods;
Sensitivity and reliability analysis;
Inverse problems and optimization;
Soft computing;
Advanced Finite Element Method, Finite Volume Method, and Boundary Element Method;
Discrete Element Method;
Meshless and related methods;
Numerical approaches to initial and boundary value problems;
Parallel computing;
Exascale computing;
Multiscale computing;
Other methods applied in computational mechanics.

Dr. Arkadiusz Poteralski
Dr. Mirosław Szczepanik
Guest Editors

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Keywords

computational mechanics
solid mechanics
fluid mechanics
biomechanics
optimization
optimal design
computational intelligence
artificial intelligence methods
sensitivity and reliability analysis
inverse problems
soft computing
finite element method
finite volume method
boundary element method
discrete element method
meshless and related methods
parallel computing
exascale computing
multiscale computing

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Published Papers (12 papers)

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21 pages, 2743 KB

Open AccessArticle

Optimization via Genetic Algorithm of the Sandwich Composite Structure for the Racing Car Monocoque

by Kamil Dolata and Mirosław Szczepanik

Appl. Sci. 2025, 15(23), 12436; https://doi.org/10.3390/app152312436 - 24 Nov 2025

Viewed by 211

Abstract

The aim of the study was to carry out optimization via genetic algorithm in order to select the best configuration of the sandwich composite structure from which the racing car monocoque is built. The tools used were the static structural analysis by means of the finite element method and multi-objective genetic algorithm implemented in the Ansys Workbench 2024 R1 software. The optimization was carried out to determine the optimal number of layers and orientation of fibers in the sandwich structure. To evaluate the efficiency of the composite structure proposed for the racing car monocoque, two key indicators were employed: Inverse Reserve Factor (IRF) and Total Deformation Load Multiplier (TDLM). The objective function was designed to minimize the overall weight while maintaining the required strength and stiffness of the structure. The results of the conducted analyses demonstrate that the applied optimization via the genetic algorithm method delivers the desired outcomes, meeting the specified design criteria and enhancing the mechanical performance of the monocoque. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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27 pages, 7649 KB

Open AccessArticle

A Concept of Equivalent Load Scheme for Easy Prediction of Structural Topology When Load Position Changes Randomly

by Bogdan Bochenek and Katarzyna Tajs-Zielińska

Appl. Sci. 2025, 15(22), 12294; https://doi.org/10.3390/app152212294 - 19 Nov 2025

Viewed by 245

Abstract

The contemporary optimal design methodologies must be aligned with actual operating conditions of the structures, like, for example, load uncertainty—a situation which often occurs in engineering problems. This paper focuses on the topology optimization of structures under loads uncertainty, a situation which often occurs in engineering problems. It is worth underlining that random load changes can significantly affect generated topologies, therefore predicting them is an important design task. In this paper, a numerical approach suited to cope with this task is proposed. It is based on the idea that while minimizing structure compliance, random load changes can be mimicked by the deterministic problem of multiple load cases. This very useful approach, however, requires hundreds of load cases to consider. To reduce the number of load cases to a few, a new concept, the Equivalent Load Scheme—ELS, is proposed. This idea, being very simple, does not require specialized software to predict the structural topology of minimal compliance for uncertain point of load application. The implementation of this idea has been tested on numerical examples, including an engineering one. The results confirmed that the presented ELS concept can be regarded as a useful alternative to the existing techniques, significantly simplifying the design process. Taking into account the effectiveness, ease of implementation, and versatility, the proposed idea stands for an original contribution to structural topology optimization, suited for the case of loads exposed to random changes, in particular, when the position of the load changes randomly. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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20 pages, 5340 KB

Open AccessArticle

Two-Stage Hybrid Optimization of Topology and Infill Density in Polymer Extrusion Additive Manufacturing for Lightweight High-Integrity Structures

by Kedarnath Rane, Andrew Bjonnes, Dickon Walker and Sampan Seth

Appl. Sci. 2025, 15(22), 12258; https://doi.org/10.3390/app152212258 - 18 Nov 2025

Viewed by 460

Abstract

Material Extrusion (MEX) additive manufacturing offers a versatile platform for producing lightweight, structurally optimized components. This study investigates the simultaneous optimization of topology and infill density using three polymer composite materials, PPA-CF, PAHT-CF, and ABS, selected for their mechanical performance, cost efficiency, and printability. Cylindrical specimens were fabricated with nine mass retention levels (100% to 33%) by systematically varying topology and infill parameters. Compression testing was conducted to assess stiffness, deformation behavior, and structural integrity under simulated operational loads. Results show that combining topology optimization with variable infill density can significantly reduce material usage and manufacturing time while maintaining mechanical reliability across all three materials. PAHT-CF demonstrated the highest strength-to-weight performance, while ABS offered cost-effective alternatives for less demanding applications. The study establishes clear relationships between design strategies and material behavior, enabling the production of net-shape satellite support structures with fewer design iterations and improved throughput. These findings support the adoption of resource-efficient manufacturing practices and provide a framework for sustainable, low- to mid-volume production in high-value manufacturing industries. Overall, the integration of design and material optimization advances the potential of additive manufacturing for scalable, cost-effective, and environmentally conscious aerospace solutions. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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37 pages, 14347 KB

Open AccessArticle

Application of FEM Analyses and Neural Networks Approach in Multi-Stage Optimisation of Notched Steel Structures Subjected to Fatigue Loadings

by Paweł J. Romanowicz, Bogdan Szybiński, Marek Barski, Adam Stawiarski and Mateusz Pałac

Appl. Sci. 2025, 15(20), 11194; https://doi.org/10.3390/app152011194 - 19 Oct 2025

Viewed by 472

Abstract

The stress concentration, which appears in loaded structural elements with voids, holes or undercuts, is the main source of premature fatigue failure. So, an increase in fatigue life can be achieved by reducing stress concentrations around the notches. Different techniques can be used to reduce the stress concentration. One of them is the application of additional stress relief undercuts or holes, while a second one relies on the application of overlays glued in the vicinity of notches. The proposed study is focused on the optimisation of notched specimens using a multi-stage optimisation process, including the use of artificial neural networks (ANNs). On this basis, the comparison of the effectiveness of various modern finite element optimisation tools is made. Here, special attention is paid to samples with elliptical holes and the application of the ANN technique in determining the optimal solution for the configuration of stress relief holes. The proposed study is illustrated by the example of a steel specimen with an elliptical opening. Specimens without stress relief holes and with an optimal configuration of stress relief holes are subjected to fatigue tests to confirm the effectiveness of the proposed approach. The performed study revealed that the cutting of additional circular stress relief holes reduces the stress concentration around the elliptical opening by about 12% and leads to an increase in fatigue life by about 79% for the applied material. Moreover, the comparison of the possibilities of the reduction in SCF by the application of stress relief holes, composite overlays and the simultaneous application of composite overlays and stress relief holes for the investigated notched samples is performed. Following the numerical results, it is observed that the use of composite overlays additionally decreases the stress concentration factor in relation to specimens with stress relief holes by an additional 6%. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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31 pages, 6593 KB

Open AccessArticle

Domain-Oriented Hierarchical Topology Optimisation—An Approach for Heterogeneous Materials

by João Dias-de-Oliveira, Joaquim Pinho-da-Cruz and Filipe Teixeira-Dias

Appl. Sci. 2025, 15(18), 10201; https://doi.org/10.3390/app151810201 - 18 Sep 2025

Viewed by 548

Abstract

In structural topology optimisation, intermediate densities are typically interpreted as local distributions of heterogeneous materials, bridging the gap between a solid and a void through optimised arrangements of cellular or composite microstructures. These multiscale configurations, governed by interactions between micro- and macroscales, are commonly addressed via hierarchical approaches. However, such methods often suffer from high computational cost and limited practical applicability. This work proposes an alternative strategy that reformulates the hierarchical problem by replacing pointwise microscale variations with a subdomain-based formulation. Each subdomain is associated with a periodic microstructure, reducing the number of local problems and significantly decreasing computational demands. A multiscale topology optimisation framework is developed using Asymptotic Expansion Homogenisation, enabling effective macrostructural properties and supporting inverse homogenisation for microscale design. The proposed method is implemented in a user-developed code and validated through several benchmark problems. The results show that the subdomain approach yields discrete and manufacturable microstructures that better reflect real-world composite applications, while also achieving substantial improvements in computational efficiency. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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16 pages, 3123 KB

Open AccessArticle

Numerical Modeling of Tissue Irradiation in Cylindrical Coordinates Using the Fuzzy Finite Pointset Method

by Anna Korczak

Appl. Sci. 2025, 15(18), 9923; https://doi.org/10.3390/app15189923 - 10 Sep 2025

Viewed by 476

Abstract

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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20 pages, 3275 KB

Open AccessArticle

Lifting-Line Predictions for Optimal Dihedral Distributions in Ground Effect

by Amanda K. Olsen, Zachary S. Montgomery and Douglas F. Hunsaker

Appl. Sci. 2025, 15(17), 9558; https://doi.org/10.3390/app15179558 - 30 Aug 2025

Viewed by 784

Abstract

When a flying wing comes within close proximity to the ground, a phenomenon called ground effect occurs where the lift is increased and the induced drag is decreased. This research seeks to determine the optimal dihedral distribution predicted by lifting-line theory that minimizes induced drag in ground effect. Despite some limitations, using lifting-line theory for this study allows for quick results across a large range of design variables, which would be infeasible for high-fidelity methods. The SLSQP optimization method is used along with a numerical lifting-line code to find the dihedral distribution that minimizes induced drag. Results are presented showing how the wing height, taper ratio, lift coefficient, and aspect ratio impact the induced drag and optimal dihedral distributions. For a given geometry, lifting-line theory predicts that there is a certain height above ground where the optimal solutions for a wing below this height result in bell-shaped wings with large section dihedral angles corresponding to a significant induced-drag reduction. For example, a wing with

R_{A} = 8

and height of

h / b = 0.25

can benefit from a reduction in induced drag of nearly 50% by employing an optimal dihedral distribution compared to a wing with no dihedral distribution. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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15 pages, 4840 KB

Open AccessArticle

Wake Turbulence Induced by Local Blade Oscillation in a Linear Cascade

by Vitalii Yanovych, Volodymyr Tsymbalyuk, Daniel Duda and Václav Uruba

Appl. Sci. 2025, 15(17), 9263; https://doi.org/10.3390/app15179263 - 22 Aug 2025

Viewed by 645

Abstract

This paper investigates the oscillatory effect of a single blade on the turbulence wake downstream of a low-pressure turbine cascade. Experimental investigations were conducted at a chord-based Reynolds number of

2.3 \times 10^{5}

with an excitation frequency of 73 Hz. The experimental [...] Read more.

2.3 \times 10^{5}

with an excitation frequency of 73 Hz. The experimental campaign encompassed two incidence angles (−3° and +6°) and three blade motion conditions: stationary, bending, and torsional vibrations. Turbulence characteristics were analyzed using hot-wire anemometry. The results indicate that the bending mode notably alters the wake topology, causing a 5% decline in streamwise velocity deficit compared to other modes. Additionally, the bending motion promotes the formation of large-scale coherent vortices within the wake, increasing the integral length scale by 7.5 times. In contrast, Kolmogorov’s microscale stays mostly unaffected by blade oscillations. However, increasing the incidence angle causes the smallest eddies in the inter-blade region to grow three times larger. Moreover, the data indicate that at −3°, bending-mode results in an approximate 13% reduction in the turbulence energy dissipation rate compared to the stationary configuration. Furthermore, the study emphasizes the spectral features of turbulent flow and provides a detailed assessment of the Taylor microscale under different experimental conditions. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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20 pages, 7605 KB

Open AccessArticle

Evaluating the Efficiency of Nature-Inspired Algorithms for Finite Element Optimization in the ANSYS Environment

by Antonino Cirello, Tommaso Ingrassia, Antonio Mancuso, Giuseppe Marannano, Agostino Igor Mirulla and Vito Ricotta

Appl. Sci. 2025, 15(12), 6750; https://doi.org/10.3390/app15126750 - 16 Jun 2025

Viewed by 770

Abstract

Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. This study investigates the feasibility and benefits of embedding three prominent metaheuristic algorithms, the Genetic Algorithm (GA), the Firefly Algorithm (FA), and the Group Search Optimizer (GSO) embedded into the ANSYS Parametric Design Language (APDL). The performance of each optimizer was assessed in three case studies. The first two are spatial truss structures, one comprising 22 bars and the other 25 bars, commonly used in structural optimization research. The third is a planar 15-bar truss in which member sizing and internal topology were simultaneously refined using a Discrete Topology (DT) variable method. For both the FA and the GSO, enhanced ranger-movement strategies were implemented to improve exploration–exploitation balance. Comparative analyses were conducted to assess convergence behavior, solution quality, and computational efficiency across the different metaheuristics. The results underscore the practical advantages of a fully integrated APDL approach, highlighting improvements in execution speed, workflow automation, and overall robustness. This work not only provides a comprehensive performance comparison of GA, FA, and GSO in structural optimization tasks, but it can also be considered a novelty in employing native APDL routines for metaheuristic-based finite element analysis. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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14 pages, 2309 KB

Open AccessArticle

Multiscale and Failure Analysis of Periodic Lattice Structures

by Young Kwon and Matthew Minck

Appl. Sci. 2025, 15(12), 6701; https://doi.org/10.3390/app15126701 - 14 Jun 2025

Cited by 1 | Viewed by 723

Abstract

A full-cycle, multiscale analysis technique was developed for periodic lattice structures with geometric repetition, aiming for more efficient modeling to predict their failure loads. The full-cycle analysis includes both upscaling and downscaling procedures. The objective of the upscaling procedure is to obtain the effective material properties of the lattice structures such that the lattice structures can be analyzed as continuum models. The continuum models are analyzed to determine the structures’ displacements or buckling failure loads. Then, the downscaling process is applied to the continuum models to determine the stresses in actual lattice members, which were applied to the stress and stress gradient based failure criterion to predict failure. Example problems were presented to demonstrate the accuracy and reliability of the proposed multiscale analysis technique. The results from the multiscale analysis were compared to those of the discrete finite element analysis without any homogenization. Furthermore, physical experiments were also conducted to determine the failure loads. Then, multiscale analysis was undertaken in conjunction with the failure criterion, based on both stress and stress gradient conditions, to compare the predicted failure loads to the experimental data. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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53 pages, 2758 KB

Open AccessSystematic Review

Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025)

by Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski, Damian Frej and Grzegorz Wilk-Jakubowski

Appl. Sci. 2025, 15(19), 10816; https://doi.org/10.3390/app151910816 - 8 Oct 2025

Cited by 2 | Viewed by 2660

Abstract

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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36 pages, 2683 KB

Open AccessSystematic Review

Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review

by Ramin Yarmohammadian, Florian Put and Ruben Van Coile

Appl. Sci. 2025, 15(15), 8740; https://doi.org/10.3390/app15158740 - 7 Aug 2025

Cited by 1 | Viewed by 3550

Abstract

Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation—issues that are especially critical in safety-sensitive fields such as fire safety engineering (FSE). To address these concerns, physics-informed surrogate modelling (PISM) integrates physical laws into machine learning models, enhancing their accuracy, robustness, and interpretability. This systematic review synthesises existing applications of PISM in FSE, classifies the strategies used to embed physical knowledge, and outlines key research challenges. A comprehensive search was conducted across Google Scholar, ResearchGate, ScienceDirect, and arXiv up to May 2025, supported by backward and forward snowballing. Studies were screened against predefined criteria, and relevant data were analysed through narrative synthesis. A total of 100 studies were included, covering five core FSE domains: fire dynamics, wildfire behaviour, structural fire engineering, material response, and heat transfer. Four main strategies for embedding physics into machine learning were identified: feature engineering techniques (FETs), loss-constrained techniques (LCTs), architecture-constrained techniques (ACTs), and offline-constrained techniques (OCTs). While LCT and ACT offer strict enforcement of physical laws, hybrid approaches combining multiple strategies often produce better results. A stepwise framework is proposed to guide the development of PISM in FSE, aiming to balance computational efficiency with physical realism. Common challenges include handling nonlinear behaviour, improving data efficiency, quantifying uncertainty, and supporting multi-physics integration. Still, PISM shows strong potential to improve the reliability and transparency of machine learning in fire safety applications. Full article

(This article belongs to the Special Issue Intelligent Systems and Tools for Optimal Design in Mechanical Engineering and Their Practical Applications)

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