An Exact-Steady-state Adaptive Chemistry method for combustion simulations: Combining the efficiency of reduced models and the accuracy of the full model

摘要

Many reduced-model methods have been developed to alleviate the computational expense of simulating chemically reacting flows with detailed kinetics. However, it is still impossible to determine exactly the loss in accuracy relative to the full model when reduced kinetic models are used for predicting quantities of interest (typically state variables). Ideally, one wishes to obtain the predictions of the full chemistry model at the fast speed of the simplified model(s). This paper describes a technique for achieving this goal for steady-state simulations. The new method, called Exact-Steady-state Adaptive Chemistry (ESAC), performs multiple fast reduced-model simulations of the steady-state problem, each time refining the accuracy of the solution by using increasingly accurate reduced models. Smaller (less accurate, but faster) reduced models are used when the simulation is far from (the full-model) steady-state; and more accurate (larger, slower) models are used as the simulation approaches the final steady-state solution. The simulation is completed by applying the trusted full kinetic model, guaranteeing the accuracy of the steady-state solution obtained using ESAC. We have developed a basic algorithm that applies this method and we present results from 2-D CFD simulations of steady-state methane and ethylene flames. ESAC simulations yielded the full-model solution (as guaranteed by the method) and were generally a factor of 3-4 times faster than the equivalent standard full-model-everywhere simulations. Future refinement of the basic implementations described here can further increase the speedup obtained when using ESAC. In applications where computational time rather than computer memory availability is the limiting factor, this technique enables efficient computation of the steady-state predicted by the full, detailed chemical kinetics model.