PSI - Issue 54

Wojciech Skarka et al. / Procedia Structural Integrity 54 (2024) 498–505 Bartosz Rodak/ Structural Integrity Procedia 00 (2019) 000 – 000

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• The gradient optimization algorithm will significantly thicken the search for the optimum in areas where the output parameter has the smallest values. • Gradient optimization shows how important it is to correctly select the search area of the input parameter. the algorithm strenuously seeks solutions at the limit of the input parameter range. This means that the optimum was outside the search area. • Both methods perform well about the search for optimal solutions. The DoE method uniformly searches the entire input parameter range, while the gradient method moves toward the most optimal solution all the time. • The DoE method gives us the answer to the question of which input parameters have what effect on the output parameter. • During the optimization process, problems were encountered with geometry generation. Linear splines can sometimes be unpredictable. Two splines with the same nodes can have a completely different path. • A very important aspect is the finite element mesh, the more accurate the better for the simulation results. In optimizing the length of the connecting element, a parameter was used to write the number of finite elements on the leading edge of this element. This was done in such a way that the longer the element, the more finite elements on its leading edge. This prevented a decrease in the accuracy of the solution. [1] - de Boor, C. (2001). A Practical Guide to Splines. Book series: Applied Mathematica Sciences, Springer.1978 [2] - Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M.. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons 2016 [3] – Kusiak J., Danielewska- Tułeck a A., Oprocha P.: Optimisation. Selected methods with examples (in polish) – WNT PWN 2019 [4] – Skinner N., Zare-Behtash H.: State-of-the-art in Aerodynamic Shape Optimisation Methods. Applied Soft Computing Volume 62, January 2018, p. 933-962 [5] - Wasik, M and Skarka, W.: Aerodynamic Features Optimization of Front Wheels Surroundings for Energy Efficient Car. 23rd ISPE Inc. International Conference on Transdisciplinary Engineering 2016 | TRANSDISCIPLINARY ENGINEERING: CROSSING BOUNDARIES 4 , pp.483-492 [6] - Skarka, W.: Model-Based Design and Optimization of Electric Vehicles. 25th ISPE Inc International Conference on Transdisciplinary Engineering 2018 | TRANSDISCIPLINARY ENGINEERING METHODS FOR SOCIAL INNOVATION OF INDUSTRY 4.0 7 , pp.566-575 [7] - Skarka, W.; Nalepa, R.; Musik, R. Integrated Aircraft Design System Based on Generative Modelling. Aerospace 2023, 10, 677. https://doi.org/10.3390/aerospace10080677 References