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首页> 外文期刊>Experimental Thermal and Fluid Science: International Journal of Experimental Heat Transfer, Thermodynamics, and Fluid Mechanics >Uncertainty quantification of a graphite nitridation experiment using a Bayesian approach
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Uncertainty quantification of a graphite nitridation experiment using a Bayesian approach

机译：使用贝叶斯方法的石墨氮化实验的不确定度量化

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摘要

In this paper, a stochastic system based Bayesian approach is applied to estimate different model parameters and hence quantify the uncertainty of a graphite nitridation experiment. The Bayesian approach is robust due to its ability to characterize modeling uncertainties associated with the underlying system and is rigorous due to its exclusive foundation on the axioms of probability theory. We choose an experiment by Zhang et al. [1] whose main objective is to measure the reaction efficiency for the active nitridation of graphite by atomic nitrogen. To obtain the primary physical quantity of interest, we need to model and estimate the uncertainty of a number of other physical processes associated with the experimental setup. We use the Bayesian method to obtain posterior probability distributions of all the parameters relevant to the experiment while taking into account uncertainties in the inputs and the modeling errors. We use a recently developed stochastic simulation algorithm which allows for efficient sampling in the high-dimensional parameter space. We show that the predicted reaction efficiency of the graphite nitridation and its uncertainty is ~3.1±1.0×10~(-3) that is slightly larger than the ones deterministically obtained by Zhang et al. [1].
机译：在本文中，基于贝叶斯方法的随机系统被用于估计不同的模型参数，从而量化了石墨氮化实验的不确定性。贝叶斯方法之所以具有鲁棒性，是因为它具有表征与底层系统相关的建模不确定性的能力，并且由于其基于概率论公理的专有基础而十分严格。我们选择张等人的实验。 [1]的主要目的是测量原子氮主动氧化石墨的反应效率。为了获得感兴趣的主要物理量，我们需要对与实验设置相关联的许多其他物理过程的不确定性进行建模和估计。我们使用贝叶斯方法获得与实验相关的所有参数的后验概率分布，同时考虑到输入的不确定性和建模误差。我们使用最近开发的随机仿真算法，该算法允许在高维参数空间中进行有效采样。我们发现，石墨氮化的预测反应效率及其不确定性为〜3.1±1.0×10〜（-3），比Zhang等人确定性获得的结果稍大。 [1]。

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