Generalized preconditioning for accelerating simulations with large kinetic models
Generalized preconditioning for accelerating simulations with large kinetic models
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DOI：
10.1016/j.proci.2022.07.256
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发表时间：
2022-10
影响因子：
3.4
通讯作者：
Anthony S. Walker;R. Speth;Kyle E. Niemeyer
中科院分区：
文献类型：
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作者：
Anthony S. Walker;R. Speth;Kyle E. Niemeyer
对真实的运输燃料的燃烧和涉及其排放的大气反应的详细建模是极其昂贵的，这是由于化学动力学模型的大尺寸和刚性。自适应预处理是一种用于降低集成大型动力学模型的成本的方法，通过基于半解析雅可比矩阵形成预处理器，与稀疏线性代数程序配对。在这项研究中，我们将这种预处理方法扩展到一个更一般的摩尔为基础的状态矢量制定适用于通用反应堆类型和组合。我们使用恒压和恒容理想气体反应器模拟测试了该方案，与典型的稠密求解器相比，对于具有10至7171种的化学动力学模型，性能从3倍到近4000倍加速。该方法还提高了性能的一个因素为1.06至21.1，大于200种的模型，与完全精确的，分析雅可比矩阵作为预处理。总的来说，这种方法提高了性能高达三个数量级的大型动力学模型，并提供了只有10个物种的模型的好处。
Detailed modeling of the combustion of real transportation fuels and the atmospheric reactions involving their emissions is prohibitively expensive, due to the large size and stiffness of the chemical kinetic models. Adaptive preconditioning is a method used to reduce the cost of integrating large kinetic models by forming a preconditioner based on a semi-analytical Jacobian matrix, paired with sparse linear algebra procedures. In this study, we extend this preconditioning method to a more-general mole-based state vector formulation applicable to generic reactor types and combinations. We tested the scheme using constant-pressure and constant-volume ideal-gas reactor simulations, showing speedup in performance from a factor of 3 up to nearly 4000 times for chemical kinetic models with 10 to 7171 species, in comparison with typical dense solvers. The method also improves performance by a factor of 1.06 to 21.1, for models larger than 200 species, in comparison with a fully exact, analytical Jacobian used as the preconditioner. Overall, this method improves performance by up to three orders of magnitude for large kinetic models, and offers benefits for models with as few as 10 species.