The role of sensitivity and uncertainty analysis in combustion modelling
The role of sensitivity and uncertainty analysis in combustion modelling
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DOI：
10.1016/j.proci.2012.07.043
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发表时间：
2013
影响因子：
4.9
通讯作者：
A. Tomlin
中科院分区：
文献类型：
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作者：
A. Tomlin
模型在提高我们对燃烧过程的理解方面发挥着重要作用，越来越多的模型能够帮助设计先进的能量转换装置。由于计算能力和自动动力学机制生成等技术的不断改进，我们有能力以更高的细节水平来表示燃烧过程。对于动力学过程尤其如此，其中正在开发描述常规燃料和替代燃料的氧化的复杂机制。这些机制可能包括多达数百种物质和数千种反应，其中热动力学数据来自各种来源，包括直接测量，全球燃烧实验和理论计算。然而，用于参数化燃烧模型的数据仍然存在重大的不确定性。这些输入不确定性通过燃烧装置的模型传播，导致关键燃烧特性的预测中的不确定性。为了提高这些模型的可信度，使其能够成功地用于设计，需要尽可能减少输入的不确定性。这就需要把精力集中在那些驱动预测不确定性的参数上，这些参数可以通过敏感性分析来确定。本文将描述燃烧模型的灵敏度和不确定性分析的方法，重点是化学动力学的例子。然后，它将讨论如何将这些技术纳入模型改进的策略，并将试图提供一些未来的观点，我们如何可以在这个方向上进行作为一个研究社区。
Models play an important role in improving our understanding of combustion processes and more and more are able to assist in the design of advanced energy conversion devices. Due to constant improvements in computing power and techniques such as automatic kinetic mechanism generation, we have the ability to represent combustion processes with increasing levels of detail. This is particularly true for kinetic processes where complex mechanisms are being developed which describe the oxidation of both conventional and alternative fuels. These mechanisms may comprise of up to hundreds of species and thousands of reactions with thermo-kinetic data derived from a wide variety of sources including direct measurements, global combustion experiments, and theoretical calculations. However, significant uncertainties in the data used to parametrise combustion models still exist. These input uncertainties propagate through models of combustion devices leading to uncertainties in the prediction of key combustion properties. In order to improve confidence in these models to the extent where they can successfully be used in design, input uncertainties need to be reduced as far as possible. This requires focussing efforts on those parameters which drive predictive uncertainty, which may be identified through sensitivity analysis. The paper will describe the methodologies available for the sensitivity and uncertainty analysis of combustion models with examples focussed on chemical kinetics. It will then discuss how such techniques can be incorporated into strategies for model improvement and will try to provide some future perspectives on how we can proceed in this direction as a research community.