Abstract
An improved Reduced-Order Model(ROM)is proposed based on a flow-solution pre-processing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the pro-posed fast prediction method reduces the error of the conventional ROM by at least 45％,with a speedup of approximately 2.0 x 105 compared to the Computational Fluid Dynamic(CFD)simu-lations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.
基金项目
National Natural Science Foundation of China(11902271)
National Natural Science Foundation of China(91952203)
Fundamental Research Funds for the Central Universities of China(G2019KY05102)
111 project on"Aircraft Complex Flows and the Control"of China(B17037)
National Supercomputer Center in Guangzhou,China(NSCC-GZ)()
NETL
NSTL
维普
万方数据