A deep learning approach for the velocity field prediction in a scramjet isolator

The accurate parameter prediction of a flow field is of practical significance to promote the development of hypersonic flight. Velocity field prediction using deep learning is a promising method to provide an accurate velocity field in a scramjet isolator. A new approach for the velocity field pred...

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Bibliographic Details
Published in:	Physics of fluids (1994) 2021-02, Vol.33 (2)
Main Authors:	Kong, Chen, Chang, Juntao, Li, Yunfei, Wang, Ziao
Format:	Article
Language:	eng
Subjects:	Artificial neural networks Computational fluid dynamics Datasets Deep learning Feature extraction Fluid dynamics Hypersonic flight Machine learning Mathematical models Model testing Modules Physics Supersonic combustion ramjet engines Three dimensional flow Velocity Velocity distribution Wall pressure
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Summary:	The accurate parameter prediction of a flow field is of practical significance to promote the development of hypersonic flight. Velocity field prediction using deep learning is a promising method to provide an accurate velocity field in a scramjet isolator. A new approach for the velocity field prediction in a scramjet isolator is developed in this study. A data-driven model is proposed for the prediction of the velocity field in a scramjet isolator by convolutional neural networks (CNNs) using measurements of the pressure on the isolator. Numerical simulations of flow in a three-dimensional scramjet isolator at various Mach numbers and backpressures are carried out to establish the dataset capturing the flow mechanism over various operating conditions. A CNN architecture composed of multiple reconstruction modules and feature extraction modules is designed. The CNN is trained using the computational fluid dynamics dataset to establish the mapping relationship between the wall pressure on the isolator and the velocity field in the isolator. The trained model is then tested over various Mach numbers and backpressures. The data-driven model successfully learns the relationship between the velocity field and pressure experienced on the wall of the isolator, i.e., the trained CNN model successfully reconstructed the velocity field based on the wall pressure on the isolator with high accuracy.
ISSN:	1070-6631 1089-7666