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Adaptive Preconditioning Strategies for Integrating Large Kinetic Mechanisms

机译：用于整合大型动力学机制的自适应预处理策略

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摘要

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 divide 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.
机译：本文开发的自适应预处理器显着降低了使用隐式常微分方程（ODE）求解器集成大型动力学机制的计算成本。对于搅拌良好的反应器​​，新方法的加速是基于直接，稀疏线性系统溶剂的最近方法的数量级。此外，新方法比通过划分差异自动生成的颂歌信息的传统实现，该方法高达三个数量级，并且通过划分差异自动生成，并且对标准的致密矩阵操作依赖于标准。与机制减少策略不同，自适应预处理器不会改变微分方程的底层系统。因此，新方法实现了其性能增益，而不会在由ODE求解器控制的本地错误中进行任何精度丢失。这种加速允许更高的保真机构化学与多维流体动力学模拟耦合。

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