Approach to combustion calculation using neural network.

Numerical simulations of combustion processes in rocket engines requires a long run time of supercomputer systems even for a very short physical time. Therefore, creating digital twins of rocket engines needs enormous processor time, and is not very effective. This computational time surpasses the actual physical time of the process in many orders of magnitude. To speed up numerical simulations the paper presents a solution of the chemical kinetics problem using artificial neural network approach. Using the architecture of a multilayer neural network with bypass connections, namely residual network, it is possible to obtain a fast and reliable solution to the problem. The neural network is trained to predict the state of the system only one time step ahead. Using it in a recursive mode, it is possible to forecast for thousands of steps without loss of accuracy. • Simulation of a chemical kinetic problem by the neural network method was carried out. • The network was trained on a wide range of data. • A shortened neural network with preservation of accuracy was obtained. • The considered network used a small amount of RAM (Random Access Memory). [ABSTRACT FROM AUTHOR]