Intelligent Time-Scale Operator-Splitting Integration for Chemical Reaction Systems

摘要

The wide range of time scales in chemical reaction systems has become an important problem in reactive flow simulations. This work proposes an intelligent time-scale operator-splitting (OS) chemistry integration method, which is effective in reduction of numerical stiffness and model complexity. Different from most existing publications, a pretrained backpropagation neural network is used to identify the slow and fast reactions and detect the sources of model stiffness on the fly, which replaces the expensive eigendecomposition of Jacobian matrix. With the fast-slow decomposition, the chemical source term can be represented as the sum of a stiff part and a nonstiff part. A stable time-scale OS integration is performed to solve the stiff chemical ordinary differential equations, which balances the computational cost with accuracy. In the simulation, a favorable comparison of the proposed integration method with the existing ODE solvers, such as implicit Euler, explicit Euler, and Runge-Kutta, is included to show its effectiveness and merits.