Optimal Design of Three-Dimensional Circular-to-Rectangular Transition Nozzle Based on Data Dimensionality Reduction

Haoqi Yang (), Qingzhen Yang, Zhongqiang Mu, Xubo Du and Lingling Chen
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Haoqi Yang: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China
Qingzhen Yang: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China
Zhongqiang Mu: Science and Technology on Scramjet Laboratory, CARDC, Mianyang 621000, China
Xubo Du: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China
Lingling Chen: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China

Energies, 2022, vol. 15, issue 24, 1-23

Abstract: The parametric representation and aerodynamic shape optimization of a three-dimensional circular-to-rectangular transition nozzle designed and built using control lines distributed along the circumferential direction were investigated in this study. A surrogate model based on class/shape transformation, principal component analysis and radial basis neural network was proposed with fewer design parameters for parametric representation and performance parameter prediction of the three-dimensional circular-to-rectangular transition nozzle. The surrogate model was combined with Non-dominated Sorting Genetic Algorithm-II to optimize the aerodynamic shape of the nozzle. The results showed that the surrogate model effectively achieved the parametric representation and aerodynamic shape optimization of the three-dimensional circular-to-rectangular transition nozzle. The geometric dimensions and performance parameters of the parametric reconstructed model were comparable to that of the initial model, implying that they can meet the needs of optimal design. The axial thrust coefficient and lift of the optimized nozzle were increased by approximately 0.742% and 15.707%, respectively.

Keywords: asymmetric expansion nozzle; parameterization method; principal component analysis; aerodynamic shape optimization; surrogate model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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