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An Empirical Method for prediction of Hypersonic Rarefied Flow-Field Structure

Transactions of Nanjing University of Aeronautics and Astronautics 2017年第5期34卷 543-552页

作者： He Tao Wang Jiangfeng College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 P.R. China

Numerical simulations are presented about the effects of gas rarefaction on hypersonic flow *** to the extremely difficult experiment,limited wind-tunnel conditions and high cost,most problems in rarefied flow regime are investigated through numerical methods,in which the direct simulation Monte-Carlo(DSMC)method is widely *** the unstructured DSMC method is employed *** around a vertical plate at a given velocity 7 500 m/s are *** gas rarefaction is judged by the free-stream Knudsen number(Kn),two vital factors are considered:molecular number density and the plate′s *** in which Kn varies from 0.035 to13.36 are *** characters in the whole rarefied regime are described,and flow-field structure affected by Knis ***,the dimensionless position D*of a certain velocity in the stagnation line is chosen as the marker of flow field to measure its *** flow-field tracing and least-square numerical method analyzing,it is proved that hypersonic rarefied flow field expands outward linearly with the increase of 1/*** empirical method is proposed,which can be used for the prediction of the hypersonic flow-field structure at a given inflow velocity,especially the shock wave position.

关键词： hypersonic rarefied flow Kn direct simulation Monte-Carlo(DSMC)mothod linear expansion flowfield prediction

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A deep learning-based approach for flow field prediction in a dual-mode combustor

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Propulsion and Power Research 2024年第2期13卷 178-193页

作者： Chen Kong Ziao Wang Fuxu Quan Yunfei Li Juntao Chang Harbin Institute of Technology Harbin 150001China

Accurate acquisition of the distribution offlow parameters inside the supersonic combustor is of great significance for hypersonicflight *** is an interesting attempt to introduce a data-driven model to a supersonic combustor forflowfield *** paper proposes a novel method for predicting theflowfield in a dual-mode *** prediction convolutional neural network with multiple branches is *** investiga-tions for a strut variable geometry combustor have been conducted to obtainflowfield data for training the network as aflowfield prediction *** data are obtained by changing the equivalent ratio,incomingflow condition and geometry of the supersonic *** Mach number distribution can be obtained from the trainedflowfield prediction model using the combustor wall pressure as input with high *** accuracy offlowfield prediction is discussed in several ***,the combustion mode detection is im-plemented on the predictionflowfield.

关键词： flowfield prediction Deep learning(DL) Convolutional neural networks(CNNs) Data-driven model Dual-mode combustor Variable geometry combustor

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