Abstract
A two-step optimization method is proposed for aerodynamic shape optimization (ASO), and the efficiency of the 2nd-step optimization is improved by proper orthogonal decomposition-based (POD-based) geometric parameterization. The two-step optimization method is constituted by particle swarm optimization (PSO) combined with Hicks-Henne shape functions and steepest descent algorithm (SDA) bonded with POD-based parameterization method. After the 1st-step optimization, superior data (SD) given with better values of objective functions which can satisfy all the constraints are filtered to extract POD bases. The POD bases only cover design space near the 1st-step optimized solution and can parameterize the geometric shape with fewer design variables (DVs). Fewer DVs and smaller design space can improve the efficiency of the 2nd-step optimization,. The two-step optimization method is validated by a case of NASA Rotor 37 aiming to increase peak adiabatic efficiency. The efficiency of the 2nd-step optimization is improved by using POD-based geometric parameterization, which is proved by comparing with SDA using the conventional parameterization method.
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Zhang, C., Duan, Y., Wang, G., Chen, H. (2023). A Two-Step Optimization Method Using POD-Based Geometric Parameterization for Aerodynamic Shape Optimization. In: Lee, S., Han, C., Choi, JY., Kim, S., Kim, J.H. (eds) The Proceedings of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), Volume 1. APISAT 2021. Lecture Notes in Electrical Engineering, vol 912. Springer, Singapore. https://doi.org/10.1007/978-981-19-2689-1_11
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DOI: https://doi.org/10.1007/978-981-19-2689-1_11
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