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

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 807

Special Issue Editors


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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 technical systems; application of optimization methods mainly in the design of components for means of transport; design of rail vehicles, cars, and aircraft
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Interests: concept and methodology of the optimization of selected mechanical structures; optimization algorithms; artificial immune systems; evolutionary algorithms procedure for simultaneous optimization of shape, topology, and distribution of different materials for the spatial structure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mechanical structures, depending on their intended purpose, must satisfy numerous design requirements. 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 using available known production methods. Consequently, such structures should be optimized with respect to 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 included are as follows:

Computational mechanics in solid-, fluid-, and biomechanics to achieve optimal design with the applications involving 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. Mirosław Szczepanik
Dr. Arkadiusz Poteralski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (1 paper)

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Research

24 pages, 3104 KB  
Article
Virtual Sensors Based on Finite Element Method: Balancing Accuracy, Runtime and Offline Effort
by Andreas Kormann, Tobias Rosnitschek, Stephan Tremmel and Frank Rieg
Appl. Sci. 2026, 16(4), 2049; https://doi.org/10.3390/app16042049 - 19 Feb 2026
Viewed by 571
Abstract
Access to internal fields such as stress, temperature, and fatigue indicators is essential for condition monitoring, yet direct sensing is often impractical. Finite element method (FEM)-based virtual sensors address this gap by combining sparse measurements with physics-based models. This work compares two virtual [...] Read more.
Access to internal fields such as stress, temperature, and fatigue indicators is essential for condition monitoring, yet direct sensing is often impractical. Finite element method (FEM)-based virtual sensors address this gap by combining sparse measurements with physics-based models. This work compares two virtual sensor workflows. The live FEM approach executes a model on demand and provides high-fidelity estimates at the cost of multi-second runtimes. The lookup database approach shifts computation offline by precomputing responses and answering online queries by fast interpolation. We introduce a quantitative cost model that links measured runtime scaling, offline construction effort, and online latency to deployment choices. The cost model is evaluated through timing studies, accuracy assessments, and an empirical break-even analysis relating offline effort to the expected number of online queries. Two case studies illustrate the method, a nonlinear tension-bar benchmark and a steady-state thermal model of a CPU die. Live FEM runtime follows a power law with α1.2 for the tensile case and an effective α0.66 for the CPU case due to dominant overheads. The resulting rules translate accuracy targets and latency budgets into workflow-selection criteria that support integration into digital-twin and monitoring pipelines. Full article
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