Iskanje po katalogu digitalne knjižnice

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: 2
Celotno besedilo (6,81 MB)

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: 24
Celotno besedilo (5,96 MB)

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: 50
Celotno besedilo (10,51 MB)
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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: 29
Celotno besedilo (7,33 MB)
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Opis: The basis of this thesis is research and development of a genetic algorithm for material parameters optimization. It is written in collaboration with AVL, which already has a solution for this problem, but is looking into better alternatives. Chaboche material model is a nonlinear isotropic and kinematic hardening model which can describe elasto-viscoplastic constitutive relations. Parameters of such complex nature do not have a physical interpretation in the real-world and must be defined with inverse analysis. Genetic algorithms (GA) are a promising tool to help with such tasks. They have been widely used and recognized for various optimization problems. Material data available are low cycle fatigue (LCF), creep, and tensile experiments. For each experiment a corresponding finite element model in Abaqus is prepared. Comparing experimental and simulation data is the objective function GA will try to minimize. For this reason, a corresponding fitness function was developed to score each individual. It makes use of similarity measure algorithm proposed in this paper [10]. GA was implemented in Python with Pygad library. Instead of bits, genes are represented with real-valued numbers with defined limits. Performance of developed GA was tested based on various population sizes, mutation probabilities, and crossover operators. The main parameter that impacts algorithms performance is population size. Paired with right mutation probability the algorithm can find a global minimum of described optimization problem. Making it a viable alternative to existing approach used at AVL.
Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 16.12.2022; Ogledov: 872; Prenosov: 0
Celotno besedilo (1,90 MB)