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.

开发了一种基于深度学习的方法，用于在复杂的真实流体流动的数值模拟中有效评估热物理特性。该工作通过用具有适当边界信息的深度前馈神经网络 (DFNN-BC) 代替状态方程的直接计算，显着提高了计算效率。所提出的方法可以以稳健的方式耦合到流动求解器。根据流动求解器的数值公式，神经网络接受原始变量或保守变量，包括系统的化学成分，并计算求解器中后续程序的所有相关流体属性。使用两个测试问题来验证所提出的方法。第一个使用具有双时间积分的预处理方案来模拟超临界条件下的旋流火箭喷射器流动动力学。第二个使用基于保守变量的公式来模拟低温燃烧的层流逆流扩散火焰。执行参数分析以优化隐藏层的数量和每个隐藏层的神经元。检查了神经网络的计算精度、效率和内存要求。DFNN-BC 模型将两个测试问题的真实流体特性评估速度分别提高了 2.43 和 3.7 倍，整体流场模拟速度分别提高了 1.5 和 2.3 倍。此外，与查表方法相比，内存使用量最多减少五个数量级。