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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: closed (20 September 2025) | Viewed by 3531

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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

E-Mail Website
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 (7 papers)

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Research

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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 74

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 159

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 342

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 438

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 468

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

Viewed by 478

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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Other

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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

Viewed by 1015

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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