Abstract Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. The models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

中文翻译：

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算子分裂方案中刚性化学 ODE 求解器的数据驱动选择

摘要 实际燃烧应用的大多数计算流体动力学模拟采用算子拆分方案，其中化学和传输分离并使用不同的数值方法进行整合。通过在计算域的每个单元格中求解常微分方程 (ODE) 来评估化学引起的成分变化，当考虑详细的化学时，这通常在计算成本中占主导地位。在这项工作中，提出了一种在算子拆分方案中选择化学 ODE 求解器的数据驱动方法。神经网络用于预测给定热化学状态的 ODE 求解器 CPU 时间和误差。这允许在逐个像元、逐个时间步长的基础上选择最佳 ODE 求解器。这些模型使用通过部分搅拌反应器和火焰模拟生成的一组广泛的热化学状态进行训练。该方法通过量化预测误差、分类精度和计算加速来验证。该模型为 70% 到 95% 的验证案例预测了最佳 ODE 求解器，并将计算成本降低了 3 倍或更多。评估了该方法对不同化学机制和不同燃料的普遍性，结果表明，如果将神经网络的输入限制为存在的一小组热化学状态变量，则该模型的性能仅略有下降，并且其适用性显着增强在大多数化学机制中。