Neural Network Approach to the Simulation of Liquid Oxygen Droplet Evaporation in a Gaseous Hydrogen Atmosphere

Abstract

this paper presents a neural network approach for modeling the non-equilibrium evaporation of a liquid oxygen droplet in a gaseous hydrogen atmosphere. We developed a deep neural network to approximate solutions of a system of transcendental equations describing the quasi-stationary state of the gas mixture near the droplet. The network used a fully connected architecture with four hidden layers and a piecewise linear activation function. We trained the model on a dataset of 5,000 points generated by numerically solving the system using the fsolve method. The input parameters include pressure, ambient temperature, oxygen mass fraction, liquid temperature, and the non-equilibrium parameter.
The model achieved high approximation accuracy, with a coefficient of determination exceeding 0.99 for both the Peclet number, which characterizes the mass evaporation rate, and the droplet surface temperature. The mean absolute error for surface temperature was 1.18 K. The neural network provides a computational speedup of more than 150 times compared to a direct numerical solution while maintaining accuracy within 3%. We incorporated the physical constraint on the critical temperature of oxygen (154.58 K).
We demonstrated that accounting for non-equilibrium effects increases droplet lifetime by 2–5 times, which must be considered in the design of liquid rocket engine combustion chambers.