Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System
Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System
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DOI：
10.1080/01621459.2017.1295863
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发表时间：
2014-11
影响因子：
3.7
通讯作者：
K. S. Bhat;D. Mebane;P. Mahapatra;C. Storlie
中科院分区：
文献类型：
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作者：
K. S. Bhat;D. Mebane;P. Mahapatra;C. Storlie
模型参数的不确定性和模型与小尺度模型的不一致性影响着大尺度系统预测的准确性和可靠性。这些不确定性的代表性不足，可能会导致不准确和过度自信的预测，在规模扩大到更大的系统。因此，多尺度建模工作必须准确地量化不确定性传播的影响，在升级。使用贝叶斯方法，我们校准一个小规模的固体吸附剂模型的热重（TGA）数据的功能配置文件，使用基于化学的先验。这项工作的关键是模型差异的表示，它使用贝叶斯平滑样条（BSS-ANOVA）框架。我们的不确定性量化（UQ）方法可以被认为是侵入性的，因为它包括化学速率表达式内的差异函数，从而产生一组随机微分方程。这种方法允许通过将联合模型参数和差异后验传播到速率表达式的较大规模系统中来容易地传播不确定性。这里提出的广泛的UQ框架可以适用于几乎所有使用多尺度建模的科学领域。本文的补充材料可在网上查阅。
ABSTRACT Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger systems. Hence, multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian smoothing splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used. Supplementary materials for this article are available online.