Abstract
For the design and optimization of advanced aero-engines,the prohibitively computa-tional 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 super-sonic crossflow under different operating conditions.To further accelerate the decision-making pro-cess,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.
维普
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