ChemNODE：化学动力学求解的神经元常微分方程方法
ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers
摘要：
求解详细的化学动力学仍然是使用有限速率化学方法的反应流的计算流体动力学模拟的主要瓶颈之一。这促使使用完全连接的人工神经网络来预测作为燃烧系统的热化学状态的函数的刚性化学源项。然而，由于燃烧的非线性和多尺度性质，当这些深度学习模型与计算流体动力学求解器耦合时，预测的解通常偏离真实解。这是因为这些方法在训练过程中最小化了误差，而不能保证与常微分方程求解器的成功集成。在目前的工作中，一种新的神经常微分方程方法来模拟化学动力学，称为ChemNODE，开发。在该深度学习框架中，在训练期间整合由神经网络预测的化学源项，并且通过计算所需的导数，相应地调整神经网络权重，以最小化预测解与地面实况解之间的差异。使用ChemNODE对氢-空气混合物在一系列组成和热力学条件下的均质自燃进行了概念验证研究。结果表明，ChemNODE准确地捕捉正确的物理行为，并再现了使用完整的化学动力学机制在一小部分的计算成本所获得的结果。
英文摘要：
Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach. This has motivated the use of fully connected artificial neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system. However, due to the nonlinearities and multi-scale nature of combustion, the predicted solution often diverges from the true solution when these deep learning models are coupled with a computational fluid dynamics solver. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers. In the present work, a novel neural ordinary differential equations approach to modeling chemical kinetics, termed as ChemNODE, is developed. In this deep learning framework, the chemical source terms predicted by the neural networks are integrated during training, and by computing the required derivatives, the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution. A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is shown that ChemNODE accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism at a fraction of the computational cost.
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