1. Numerical solving of dynamic thermography inverse problem for skin cancer diagnosis based on non-Fourier bioheat modelIvan Dominik Horvat, Jurij Iljaž, 2025, izvirni znanstveni članek Opis: This paper presents numerical solving of the inverse bioheat problem to estimate four skin cancer parameters; diameter, thickness, blood perfusion rate and thermal relaxation time, based on the thermal response on the skin surface obtained by dynamic thermography and numerical skin cancer model, which can greatly enhance dynamic thermography diagnostics. To describe the heat transfer inside biological tissue and thermal behavior during the dynamic thermography process as realistic as possible, the non-Fourier dual-phase-lag bioheat model was used, as well as skin cancer model has been composed of multilayered healthy skin, embedded skin tumor and subcutaneous fat and muscle. Boundary element method has been used to solve a complex non-Fourier bioheat model to simulate dynamic thermography based on the skin cancer model and guessed searched parameters to obtain the thermal response on the skin surface during the cooling and rewarming phase using a cold air jet provocation, which is needed for the solution of the inverse bioheat problem. The inverse problem has been solved by optimization approach using the hybrid Levenberg-Marquardt optimization method, while the measurement data has been generated numerically with known exact tumor parameters and added noise, to evaluate the accuracy and sensitivity of the solution. Inverse problem solution has been tested for two different thermal responses; absolute temperature and temperature difference response, as well as for two different tumor stages; early stage or Clark II and later stage or Clark IV tumor. All important tumor parameters were successfully retrieved, especially the diameter and relaxation time, even for the high level of noise, while the accuracy of obtained parameters is slightly better using absolute temperature response. The results demonstrate the robustness of the method and a promising way for early diagnosis. The findings contribute to improving bioheat modeling in biological tissues, solving inverse bioheat problems and advancing dynamic thermography as a non-invasive tool for early skin cancer diagnosis.
Ključne besede: numerical modeling, dynamic thermography, inverse problem, non-Fourier bioheat transfer, dual-phase-lag model, boundary element method, Levenberg-Marquardt optimization
Objavljeno v DKUM: 09.12.2025; Ogledov: 0; Prenosov: 6
 Celotno besedilo (1,83 MB)
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2. Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networksMitja Garmut, Simon Steentjes, Martin Petrun, 2025, izvirni znanstveni članek Opis: Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%.
Ključne besede: interior permanent magnet synchronous machine, artificial neural network, metamodel, multi-objective optimization, finite element method
Objavljeno v DKUM: 08.08.2025; Ogledov: 0; Prenosov: 22
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3. Integrating Multi-Physics Modeling within Multi-Objective Optimization to Enhance the Performance and Efficiency of Permanent Magnet Synchronous Machines : doktorska disertacijaMitja Garmut, 2025, doktorska disertacija Opis: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency.
The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process.
Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy.
The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods.
Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method.
By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations.
In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Ključne besede: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Objavljeno v DKUM: 15.05.2025; Ogledov: 0; Prenosov: 161
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4. The approach of using a horizontally layered soil model for inhomogeneous soil, by taking into account the deeper layers of the soil, and determining the model’s parameters using evolutionary methodsMarko Jesenik, Mislav Trbušić, 2025, izvirni znanstveni članek Opis: A new approach using a horizontally layered analytical soil model for inhomogeneous soil is presented. The presented approach also considers deeper soil layers, which is not the case when simply dividing the area of interest into smaller subareas. The finite element method model was used to prepare test data because, in such a case, the soil parameters are known. Six lines simulating Wenner’s method were used, and their results were combined appropriately to determine the soil parameters of nine subareas. To determine the soil parameters in the scope of each subarea, different optimization methods were used and compared to each other. The results were analyzed, and Artificial Bee Colony was selected as the most appropriate method among those tested. Additionally, the convergence of the methods was analyzed, and Memory Assistance is presented, with the aim of shortening the calculation time. In this study, three-, four-, five-, and six-layered soil models were tested, and it is concluded that the three-layered model is most appropriate. A finite element method model based on the soil determination results was constructed to verify the results. The results of the Wenner’s method simulation in the cases of the test data and final model were compared to confirm the accuracy of the presented method
Ključne besede: grounding system, soil model, finite element method, differential evolution, artificial bee colony, teaching–learning-based optimization
Objavljeno v DKUM: 21.02.2025; Ogledov: 0; Prenosov: 6
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5. Mathematical model-based optimization of trace metal dosage in anaerobic batch bioreactorsTina Kegl, Balasubramanian Paramasivan, Bikash Chandra Maharaj, 2025, izvirni znanstveni članek Opis: Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model’s reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH4 production by 5.4% and 13.5%, respectively, while H2, H2S, and NH3 decreased by 28.2%, 43.6%, and 42.5%, respectively.
Ključne besede: anaerobic digestion, batch bioreactor, methane production, model parameters calibration, active set optimization method, perturbation of model parameter, gradient based optimization, trace metals
Objavljeno v DKUM: 30.01.2025; Ogledov: 0; Prenosov: 4
Celotno besedilo (4,66 MB) |
6. Workpiece placement optimization for robot machining based on the evaluation of feasible kinematic directional capabilitiesSaša Stradovnik, Aleš Hace, 2024, izvirni znanstveni članek Opis: Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes. In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot’s workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations.
Ključne besede: workpiece placement optimization, robotic surface machining, feasible kinematic directional capabilities, decomposed twist feasibility (DTF) method, manipulability, non-linear optimization
Objavljeno v DKUM: 12.08.2024; Ogledov: 64; Prenosov: 49
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7. Design and optimization of a spherical magnetorheological actuatorJakob Vizjak, Anton Hamler, Marko Jesenik, 2023, izvirni znanstveni članek Opis: Recently, an increasing number of electromagnetic devices have been using smart fluids. These include ferrofluids, electrorheological fluids, and magnetorheological (MR) fluids. In the paper, magnetorheological fluids are considered for use in a spherical actuator for haptic applications. An approach is presented to the design and optimization of such a device, using finite element method modelling linked with differential evolution (DE). Much consideration was given to the construction of the objective function to be minimized. A novel approach to objective function assembly was used, using reference values based on the model design and created with parameters set to the midpoint values of the selected range. It was found to be a useful strategy when the reference values are unknown. There were four parameters to be optimized. Three of them gravitated towards the boundary value, and the fourth (actuator radius) was somewhere in between. The value of the objective function reached a minimum in the range of actuator radius between 42.9880 mm and 45.0831 mm, which is about a 5% difference in regard to the actuator radius. Three passes of optimization were performed with similar results, proving the robustness of the algorithm.
Ključne besede: magnetorheological fluid, finite element method, FEM, optimization, differntial evolution, DE, actuator
Objavljeno v DKUM: 22.05.2024; Ogledov: 173; Prenosov: 26
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8. Innovative approaches to wear reduction in horizontal powder screw conveyors : a design of experiments-guided numerical studyMarko Motaln, Tone Lerher, 2024, izvirni znanstveni članek Opis: Numerical simulations play a vital role in the modern engineering industry, especially when faced with interconnected challenges such as particle interactions and the structural integrity of conveyor systems. This article focuses on the handling of materials and emphasizes the importance of using parametric numerical analysis to improve efficiency, reduce wear, and enhance the structural integrity of horizontal screw conveyors. Through the utilization of the Design of Experiments, we systematically investigated critical parameters such as screw pitch, clearance, wear, rotational velocity, and additional structural factors. This examination was carried out within a well-defined parametric framework, utilizing a combination of software tools provided by the Ansys suite and Minitab. The findings demonstrate the effectiveness of the Design of Experiments analysis in achieving improved performance and provide valuable insights for engineers and researchers involved in the design of conveyor systems. Furthermore, this comprehensive approach clarifies how conveyor systems respond to changes in parameters and highlights the complex interaction between transported particles and the conveyor system. We present a detailed analysis that clarifies the complex relationships and dependencies among different parameters, providing engineers and researchers with valuable insights. By understanding the interactions of these factors, the methodology provides not only results but also a strategic framework for advancing conveyor system design and engineering practices.
Ključne besede: discrete element method, design optimization, horizontal screw conveyors, parametric study, conveying equipment, bulk handling, bulk solids, abrasive wear, screw conveyor, FEA, performance analysis
Objavljeno v DKUM: 09.04.2024; Ogledov: 275; Prenosov: 65
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10. Improvement of biogas production utilizing a complex anaerobic digestion model and gradient-based optimizationTina Kegl, Breda Kegl, Marko Kegl, 2024, izvirni znanstveni članek Opis: Anaerobic digestion (AD) is a promising technology for renewable energy production from organic waste. In order to maximize the produced biogas quantity and quality, this paper deals with the optimization of the AD process in a CSTR bioreactor of a full-scale biogas plant. For this purpose, a novel approach was adopted coupling, a highly complex BioModel for AD simulation, and a gradient-based optimization method. In order to improve AD performance, the dosages of various types of biological additives, the dosages of inorganic additives, and the temperature in the bioreactor were optimized in three different scenarios. The best biogas quality was obtained using multi-objective optimization, where the objective function involves the following two conflicting objectives: the maximization of biogas production and minimization of the needed heating energy. The obtained results show that, potentially, the content of CH4 can be increased by 11%, while the contents of H2, H2S, and NH3 can be reduced by 30%, 20%, and 81% when comparing the simulation results with the experimental data. The obtained results confirm the usefulness of the proposed approach, which can easily be adapted or upgraded for other bioreactor types.
Ključne besede: additives, anaerobic digestion, approximation method, BioModel, complex substrate, gradient-based optimization, process conditions
Objavljeno v DKUM: 12.03.2024; Ogledov: 298; Prenosov: 41
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