A Review of Research on the Thermo-Force Characteristics of Scramjet Engine Wall Based on Neural Networks

With the development of high-performance computing and advanced experimental methods, data-driven machine learning, especially neural network technology, has shown great potential in fluid mechanics research and has become a fourth paradigm research tool. In particular, remarkable achievements have been made in turbulence modeling, near-wall flow prediction, and combustion dynamic evolution. Researchers use neural network model to assist turbulence control, improve Reynolds average turbulence model, and harnesses the deep learning method to solve the problem of complex flow phenomenon prediction driven by large-scale data, which effectively improves the accuracy and efficiency of internal flow and wall effect simulation of supersonic combustion ramjet (scramjet) engine. These studies not only promote the development of fluid mechanics but also provide strong support for the design optimization of scramjet engines.