Analysis of large-scale, realistic models incorporating detailed chemistry can be challenging because each simulation is computationally expensive, and a complete analysis may require many simulations. This paper addresses one such problem of this type, chemical-reaction selection in engine simulations. In this computationally challenging case, it is demonstrated how the important concept of sparsity can facilitate chemical-reaction selection, which is the process of finding the most important chemical reactions for modeling a chemical process. It is difficult to perform accurate reaction selection for engine simulations using realistic models of the chemistry, as each simulation takes processor weeks to complete. We developed a procedure to efficiently accomplish this selection process with a relatively small number of simulations using a form of global sensitivity analysis based on sparse regression. The chemical-reaction selection leads to an analysis of the ignition chemistry as it evolves within the compression-ignition engine simulations and allows for the spatial development of the selected chemical reactions to be studied in detail.

中文翻译：

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稀疏性有助于发动机模拟的化学反应选择

由于每个模拟的计算量都很大，并且要进行完整的分析可能需要进行许多模拟，因此分析包含详细化学成分的大规模，逼真的模型可能具有挑战性。本文解决了此类问题中的一个问题，即发动机仿真中的化学反应选择。在这种具有计算挑战性的情况下，证明了稀疏性的重要概念如何促进化学反应的选择，这是寻找最重要的化学反应以建模化学过程的过程。使用实际的化学模型为发动机模拟执行准确的反应选择非常困难，因为每个模拟都需要花费处理器数周才能完成。我们开发了一种程序，可以使用基于稀疏回归的全局敏感性分析的形式，通过相对较少的模拟有效地完成此选择过程。化学反应的选择会导致对点火​​化学的分析，因为它会在压缩点火发动机模拟中演变，并允许对选定化学反应的空间发展进行详细研究。