A deep learning approach for velocity field prediction in a scramjet isolator from Schlieren images
Chen Kong, Ziao Wang, Yunfei Li, Juntao Chang
Abstract
Accurate measurements of physical parameters in a scramjet isolator are very important to promote the design and optimization of the isolator and even the scramjet. In a ground experiment, limited by the inherent characteristics of measurement technology and equipment, it is a big challenge to obtain the velocity field inside an isolator. In this study, a deep learning approach was introduced to combine data obtained from ground experiments and numerical simulations, and a velocity field prediction model was developed for obtaining the velocity field inside an isolator based on experimental Schlieren images. The velocity field prediction model was designed with convolutional neural networks as the main structure. Ground experiments of a scramjet isolator under continuous Mach number variation were carried out, and Schlieren images of the flow field inside the isolator were collected. Numerical simulations of the isolator were also carried out, and the velocity fields inside the isolator under various Mach numbers were obtained. The velocity field prediction model was trained using flow field datasets containing experimental Schlieren images and velocity field, and the mapping relationship between the experimental Schlieren images and the predicted velocity field was successfully established.