面向紧凑动力学模型生成的机器学习技术开发
Toward Development of Machine Learned Techniques for Production of Compact Kinetic Models
AI总结：
本文提出MLOCK算法，通过自动化扰动和优化化学动力学模型的子模型与参数，将包含2789物种的详细机理压缩至15物种，保持约87%保真度，超越现有方法。
摘要：
化学动力学模型是燃烧装置开发与优化中的关键组成部分，其通过与计算流体动力学（CFD）等多维模拟的耦合来实现。需要保留对现实良好保真度的低维动力学模型，而这类模型的生成需要大量的人力时间成本和专家知识。在此，我们提出一种新颖的自动化计算强化方法，用于生成过度简化且优化的（紧凑型）化学动力学模型。该算法被称为化学动力学机器学习优化（MLOCK），它系统地扰动化学动力学模型的四个子模型中的每一个，以发现哪些术语组合能产生良好的模型。首先，使用常规机理简化方法获得一个由n个物种组成的虚拟反应网络。为了抵消由此带来的模型性能下降，对虚拟反应网络中每个节点（物种）之间重要连接（虚拟反应）的权重（虚拟反应速率常数）进行数值优化，以在四个连续阶段中复现选定的计算结果。MLOCK的第一个版本（MLOCK1.0）同时扰动重要连接的所有三个虚拟Arrhenius反应速率常数参数，并通过目标误差函数评估新参数的适用性，这些函数量化每个紧凑模型候选在计算优化目标（可能包括详细模型计算和/或实验数据）时的误差。MLOCK1.0通过为甲烷空气燃烧这一典型情况创建紧凑模型得到验证。结果表明，包含2,789个物种的NUGMECH1.0详细模型被可靠地压缩至15个物种（节点），同时保持对详细模型计算约87%的整体保真度，超越了先前的最先进水平。
英文摘要：
Chemical kinetic models are an essential component in the development and optimisation of combustion devices through their coupling to multi-dimensional simulations such as computational fluid dynamics (CFD). Low-dimensional kinetic models which retain good fidelity to the reality are needed, the production of which requires considerable human-time cost and expert knowledge. Here, we present a novel automated compute intensification methodology to produce overly-reduced and optimised (compact) chemical kinetic models. This algorithm, termed Machine Learned Optimisation of Chemical Kinetics (MLOCK), systematically perturbs each of the four sub-models of a chemical kinetic model to discover what combinations of terms results in a good model. A virtual reaction network comprised of n species is first obtained using conventional mechanism reduction. To counteract the imposed decrease in model performance, the weights (virtual reaction rate constants) of important connections (virtual reactions) between each node (species) of the virtual reaction network are numerically optimised to replicate selected calculations across four sequential phases. The first version of MLOCK, (MLOCK1.0) simultaneously perturbs all three virtual Arrhenius reaction rate constant parameters for important connections and assesses the suitability of the new parameters through objective error functions, which quantify the error in each compact model candidate's calculation of the optimisation targets, which may be comprised of detailed model calculations and/or experimental data. MLOCK1.0 is demonstrated by creating compact models for the archetypal case of methane air combustion. It is shown that the NUGMECH1.0 detailed model comprised of 2,789 species is reliably compacted to 15 species (nodes), whilst retaining an overall fidelity of ~87% to the detailed model calculations, outperforming the prior state-of-art.
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