Abstract
AbstractA 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.KeywordASOPODHybrid optimizationRotor 37
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
