Data-driven surrogate modeling and optimization of supercritical jet into supersonic crossflow  

作　　者：Siyu DING Longfei WANG Qingzhou LU Xingjian WANG 

机构地区：[1]Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Chinese Journal of Aeronautics》2024年第12期139-155,共17页中国航空学报（英文版）

基　　金：supported by the Science Center for Gas Turbine Project,China(No.P2022-B-II-020-001);the National Natural Science Foundation of China(No.52276123).

摘　　要：For the design and optimization of advanced aero-engines,the prohibitively computational resources required for numerical simulations pose a significant challenge,due to the extensive exploration of design parameters across a vast design space.Surrogate modeling techniques offer a viable alternative for efficiently emulating numerical results within a notably compressed timeframe.This study introduces parametric Reduced-Order Models(ROMs)based on Convolutional Auto-Encoders(CAE),Fully Connected AutoEncoders(FCAE),and Proper Orthogonal Decomposition(POD)to fast emulate spatial distributions of physical variables for a supercritical jet into a supersonic crossflow under different operating conditions.To further accelerate the decision-making process,an optimization model is developed to enhance fuel-oxidizer mixing efficiency while minimizing total pressure loss.Results indicate that CAE-based ROMs exhibit superior prediction accuracy while FCAE-based ROMs show inferior predictive accuracy but minimal uncertainty.The latter may be ascribed to the markedly greater number of hyperparameters.POD-based ROMs underperform in regions of strong nonlinear flow dynamics,coupled with higher overall prediction uncertainties.Both AE-and POD-based ROMs achieve online predictions approximately 9 orders of magnitude faster than conventional simulations.The established optimization model enables the attainment of Pareto-optimal frontiers for spatial mixing deficiencies and total pressure recovery coefficient.

关 键 词：Reduced-Order Model(ROM) SUPERCRITICAL Jet in crossflow SCRAMJET Uncertainty quantification Pareto-optimal frontier 

分 类 号：V235.21[航空宇航科学与技术—航空宇航推进理论与工程]