Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields

Abstract

A deep-learning based approach is developed for efficient evaluation of thermophysical properties in numerical simulation of complex real-fluid flows. The work enables a significant improvement of computational efficiency by replacing direct calculation of the equation of state with a deep feedforward neural network with appropriate boundary information (DFNN-BC). The proposed method can be coupled to a flow solver in a robust manner. Depending on the numerical formulation of the flow solver, the neural network takes in either the primitive or conservative variables, including the chemical composition of the system, and calculates all relevant fluid properties for the subsequent routines in the solver. Two test problems are employed to validate the proposed methodology. The first uses a preconditioning scheme with dual-time integration for the simulation of swirl rocket injector flow dynamics under supercritical conditions. The second uses a conservative-variable based formulation for the simulation of laminar counterflow diffusion flames for cryogenic combustion. A parametric analysis is performed to optimize the numbers of hidden layers and neurons per hidden layer. The computational accuracy, efficiency, and memory requirements of the neural network are examined. The DFNN-BC model accelerates the evaluation of real-fluid properties by a factor of 2.43 and 3.7 for the two test problems, respectively, and the overall flowfield simulation by 1.5 and 2.3, respectively. In addition, the memory usage is reduced by up to five orders of magnitude in comparison with the table look-up method.