The main bottleneck when performing computational fluid dynamics (CFD) simulations of combustion systems is the computation and integration of the highly non-linear and stiff chemical source terms. In recent times, machine learning has emerged as a promising tool to accelerate combustion chemistry, involving the use of regression models to predict the chemical source terms as functions of the thermochemical state of the system. However, combustion is a highly nonlinear phenomenon, and this often leads to divergence from the true solution when the neural network representation of chemical kinetics is integrated in time. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation (ODE) solvers. In this work, a novel neural ODE approach to combustion modeling, ChemNODE, is developed to address this issue. The source terms predicted by the neural network are integrated during training, and by backpropagating errors through the ODE solver, the neural network weights are adjusted accordingly to minimize the difference between the predicted and actual ODE solutions. It is shown that even when the dimensionality of the thermochemical manifold is trimmed to remove redundant species, the proposed approach accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism.
翻译:在对燃烧系统进行计算流体动态(CFD)模拟时,主要瓶颈是燃烧系统进行计算流体动态(CFD)模拟时,主要瓶颈是高度非线性和硬化化学源术语的计算和整合。最近,机器学习已成为加速燃烧化学的一个很有希望的工具,包括使用回归模型预测化学源术语,作为系统热化状态的功能。然而,燃烧是一种高度非线性的现象,当化学动因神经网络代表的化学动因能及时整合时,这往往导致与真正的解决方案的差异。这是因为,这些方法在培训期间尽量减少错误,而没有保证与普通差异方(ODE)溶解剂成功整合。在这项工作中,为解决这一问题,开发了一种新的神经源代码方法。神经网络预测的源术语在培训期间被整合,并通过ODE解析器反向再造错误,对神经网络的重量进行了相应调整,以尽量减少预测的与实际的ODE解决方案之间的差异。这表明,即使热化学元的维度是三成的,用以消除多余的物种,拟议的物理再造结果也是准确的。