Mathematical and Computer Modelling of Technical and Engineering Systems

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1911

Editors


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Department of Industrial Automation, Ternopil Ivan Pul’uj National Technical University, Ternopil, Ukraine
Interests: mathematical modelling; optimization of complex systems; computational intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Medical Informatics, I. Horbachevsky Ternopil National Medical University, 46002 Ternopil, Ukraine
Interests: mathematical modelling; machine learning; artificial intelligence; cyber–physical systems; medical and biological processes; lattice differential equations; stability studies; computer modeling

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Guest Editor
Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: mathematical modeling; optimization of complex systems; combinatorial optimization; packing and covering problems; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, modelling has become an indispensable tool for understanding, predicting, and optimizing the behaviour of complex systems across science, engineering, and society. The rapid growth of computational power, coupled with advances in data-driven techniques such as machine learning and artificial intelligence, has opened up new frontiers for integrating theory, simulation, and real-world applications. These developments are transforming how researchers approach challenges ranging from industrial design and environmental sustainability to healthcare, transportation, and digital infrastructure.

This Special Issue will bring together cutting-edge research that highlights both methodological innovations and practical applications of modelling. We welcome contributions that explore novel mathematical frameworks, hybrid approaches combining physics-based and data-driven models, and interdisciplinary studies that bridge theory with practice. Particular emphasis will be placed on works that demonstrate how modelling can provide actionable insights, improve decision-making, and enhance system resilience in uncertain and dynamic environments.

By fostering collaboration among scholars worldwide, this Special Issue will create a cohesive collection of high-quality research articles and reviews that reflect the state of the art in modelling. Our goal is not only to showcase technical advances but also to stimulate dialogue across disciplines, encouraging the development of new ideas and future research directions. We invite contributions from both established experts and emerging researchers, and we strongly encourage collaborative submissions that span multiple domains of application.

Special Issue is open to a broad spectrum of topics, including but not limited to the following:

  • Frontiers in Data-Driven and Computational Modelling of Complex Systems;
  • Innovations in Computational and AI-Enhanced Modelling for Complex Systems;
  • Hybrid Approaches in Data-Driven and Physics-Based Modelling of Complex Systems;
  • Next-Generation Computational Modelling: From Algorithms to Applications.

Through this Special Issue, we will provide a platform for advancing the science and practice of modelling, while building a global community of researchers dedicated to pushing the boundaries of what modelling can achieve.

Prof. Dr. Pavlo Maruschak
Prof. Dr. Andrii Sverstiuk
Prof. Dr. Sergiy Yakovlev
Dr. Dmytro Chumachenko
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Modelling 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 1200 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

  • complex systems
  • simulation modelling
  • network theory
  • computational modelling
  • data-driven methods
  • machine learning and AI in modelling
  • hybrid modelling approaches
  • complex systems analysis
  • simulation and optimization
  • digital twin technologies
  • interdisciplinary applications
  • system resilience and uncertainty
  • predictive analytics
  • sustainability and recirculation
  • healthcare and computer-aided decision support systems

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Published Papers (3 papers)

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Research

26 pages, 5313 KB  
Article
Mathematical Modeling and Comparative Evaluation of PI and PID Speed Controllers for Electric Vehicle Traction Systems
by Oleg Lyashuk, Dmytro Mironov, Pavlo Maruschak, Volodymyr Dzyura and Viktor Shevchuk
Modelling 2026, 7(3), 100; https://doi.org/10.3390/modelling7030100 - 20 May 2026
Viewed by 360
Abstract
Although PI and PID controllers are mature control laws, their effect on energy-related variables is rarely isolated in a complete electric vehicle traction model when the plant, controller tuning basis and driving conditions are kept unchanged. A full-system MATLAB/Simulink model was developed, comprising [...] Read more.
Although PI and PID controllers are mature control laws, their effect on energy-related variables is rarely isolated in a complete electric vehicle traction model when the plant, controller tuning basis and driving conditions are kept unchanged. A full-system MATLAB/Simulink model was developed, comprising a DC motor with PWM H-bridge, reduction gear, vehicle dynamics and a lithium-ion battery with SOC monitoring. Fixed-gain PI and PID configurations were compared under FTP75, with US06 added as a dynamic-cycle assessment. Speed tracking was evaluated using RMSE, MAE, IAE and ITAE, while energy behavior was assessed through SOC depletion, battery voltage, current and braking-command signals. Under FTP75, both controllers achieved nearly identical tracking accuracy, with an overall RMSE of 0.1525 km/h across the active intervals. Despite this kinematic equivalence, PID reduced SOC depletion by 0.980 percentage points over 4.963 km and produced a less intense but more distributed braking command. The additional 600 s US06 simulation did not confirm a general PID advantage: both controllers reached the same maximum speed and showed practically identical tracking accuracy, while PID did not reduce SOC depletion. The results show that the derivative channel changes the control-command pattern, but it does not automatically improve kinematic or energy performance under fixed-gain tuning. Full article
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25 pages, 6665 KB  
Article
Automated Water Hammer Analysis for Fracture Parameter Inversion Using High-Frequency Shut-In Pressure Signals During Hydraulic Fracturing
by Mao Zhu and Hanyi Wang
Modelling 2026, 7(3), 87; https://doi.org/10.3390/modelling7030087 - 30 Apr 2026
Viewed by 460
Abstract
Hydraulic fracture geometry is of great importance for evaluating stimulation effectiveness and supporting the efficient development of unconventional oil and gas reservoirs, and it can be estimated from field shut-in water hammer signals. However, field signals are commonly characterized by strong noise, pronounced [...] Read more.
Hydraulic fracture geometry is of great importance for evaluating stimulation effectiveness and supporting the efficient development of unconventional oil and gas reservoirs, and it can be estimated from field shut-in water hammer signals. However, field signals are commonly characterized by strong noise, pronounced non-stationarity, strong dependence on manual extraction of effective response segments, and limited automation in inversion analysis. To address these issues, this study develops an integrated automated interpretation framework for shut-in water hammer analysis, which combines an adaptive shape-preserving Kalman filter for non-stationary signal denoising, an automatic response segment identification method, and a particle swarm optimization-based inversion strategy for fracture geometry estimation. The framework is validated using field high-frequency pressure data from hydraulically fractured wells. The results show that the proposed denoising method improves the signal-to-noise ratio from 11.99 dB to 25.05 dB while preserving key transient features. The response segments can be extracted efficiently, with runtimes of 0.84–1.22 s and onset errors within 0–5 s. For a representative fracturing stage, the relative errors of the inverted fracture half-length and fracture height are 6.21% and 3.04%, respectively. The proposed framework provides a low-cost and field-applicable tool for fracture evaluation and engineering decision-making. Full article
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16 pages, 8985 KB  
Article
Practical Significance of Reliability-Based Structural Design: Application to Electro-Mechanical Components
by Domen Šeruga, Lovro Novak, Marko Nagode and Jernej Klemenc
Modelling 2026, 7(2), 47; https://doi.org/10.3390/modelling7020047 - 27 Feb 2026
Viewed by 428
Abstract
The study reports on the essential level of details in simulations during the development of structural components if reliability-based design is used to ensure their quality and operational safety. A general method, which is initially introduced, is then applied to an indicator spring [...] Read more.
The study reports on the essential level of details in simulations during the development of structural components if reliability-based design is used to ensure their quality and operational safety. A general method, which is initially introduced, is then applied to an indicator spring of a fuse element during assembly and operation stages. First, it is proven that design of simulations based on orthogonal arrays which includes variations of form, material properties and operating conditions within expected scatter limits provides a comparable determination of the scale parameter for the two-parameter Weibull distribution as the experimental observations of the same process. The shape parameter of the distribution tends to be underestimated by the simulations resulting in a higher scatter of the expected properties than experimentally measured. Next, it is shown that the maximum likelihood method to determine representative parameters of the scatter of assembly and operation stages provides a better match with experimental data than the median rank regression. Finally, a high reliability of the indication has been calculated for the fuse element if both the scatter of the assembly and the operation conditions were considered. Full article
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