Faster solvers for large kinetic mechanisms using adaptive preconditioners

Abstract

The adaptive preconditioners developed in this paper substantially reduce the computational cost of integrating large kinetic mechanisms using implicit ordinary differential equation (ODE) solvers. For a well-stirred reactor, the speedup of the new method is an order of magnitude faster than recent approaches based on direct, sparse linear system solvers. Moreover, the new method is up to three orders of magnitude faster than traditional implementations of the ODE solver where the Jacobian information is generated automatically via finite differences, and the factorization relies on standard, dense matrix operations. Unlike mechanism reduction strategies, the adaptive preconditioners do not alter the underlying system of differential equations. Consequently, the new method achieves its performance gains without any loss of accuracy to within the local error controlled by the ODE solver. Such speedup allows higher fidelity mechanism chemistry to be coupled with multi-dimensional fluid dynamics simulations.