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.

In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine Learning (ML) provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications. Furthermore, machine learning algorithms can enhance flow information and automatically perform tasks that involve active flow control and optimization. This article provides an overview of the past history, current development, and promising prospects of machine learning in the field of fluid mechanics. In addition, to facilitate understanding, this article outlines the basic principles of machine learning methods and their applications in engineering practice, turbulence models, flow field representation problems, and active flow control. In short, machine learning provides a powerful and more intelligent data processing architecture, and may greatly enrich the existing research methods and industrial applications of fluid mechanics.