A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters
A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters
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DOI：
10.1016/j.combustflame.2019.04.023
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发表时间：
2018-03
影响因子：
4.4
通讯作者：
J. Bell;M. Day;J. Goodman;R. Grout;M. Morzfeld
中科院分区：
文献类型：
--
作者：
J. Bell;M. Day;J. Goodman;R. Grout;M. Morzfeld
采用第一性原理马尔可夫链蒙特卡罗采样方法研究了氢动力学参数的不确定性量化和不确定性传播。具体来说，我们对31个参数的后验分布进行了采样，重点是在91个实验中产生的h2o2和ho2反应。已建立的文献值用于机制中的其余参数以及指定流体性质所需的其他热力学和输运数据。使用仿射不变采样器从广泛的非信息先验开始计算样本。自相关分析表明，O（1M）个样本足以获得合理的后验抽样。所得分布确定了强的正相关和负相关以及几个非高斯特征。使用从后验中提取的样本，我们研究了参数不确定性对两种更复杂火焰预测的影响：二维预混火焰核和氢射流进入加热室的点火。前者代表了与用于校准机制的目标实验相似的燃烧制度，后者代表了不同的燃烧制度。对于预混火焰，给定时间间隔后的净产物量的标准差小于2%，而射流点火时间的标准差大于10%。用于这些研究的样本在网上公布。这些结果表明，在不同的燃烧模式下，与目标实验相一致的参数对预测行为的约束程度。这个过程提供了一个框架，既可以识别反应，进一步研究候选机制，也可以结合不确定性量化和传播，最终将实验室火焰实验的不确定性与更复杂场景的最终使用数值预测的不确定性联系起来。
First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution for thirty-one parameters focusing on the H2O2and HO2reactions resulting from conditioning on ninety-one experiments. Established literature values are used for the remaining parameters in the mechanism as well as other thermodynamic and transport data needed to specify fluid properties. The samples are computed using an affine invariant sampler starting with broad, noninformative priors. Autocorrelation analysis shows that O(1M) samples are sufficient to obtain a reasonable sampling of the posterior. The resulting distribution identifies strong positive and negative correlations and several non-Gaussian characteristics. Using samples drawn from the posterior, we investigate the impact of parameter uncertainty on the prediction of two more complex flames: a 2D premixed flame kernel and the ignition of a hydrogen jet issuing into a heated chamber. The former represents a combustion regime similar to the target experiments used to calibrate the mechanism and the latter represents a different combustion regime. For the premixed flame, the net amount of product after a given time interval has a standard deviation of less than 2% whereas the standard deviation of the ignition time for the jet is more than 10%. The samples used for these studies are posted online. These results indicate the degree to which parameters consistent with the target experiments constrain predicted behavior in different combustion regimes. This process provides a framework for both identifying reactions for further study from candidate mechanisms as well as combining uncertainty quantification and propagation to, ultimately, tie uncertainty in laboratory flame experiments to uncertainty in end-use numerical predictions of more complicated scenarios.