Replication Data for: Closure Law Model Uncertainty Quantification

The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify the uncertainty in predictions. We have developed two methods for optimization, based on the integrated quadratic distance and the energy score. The proposed methods were applied to the commercial multiphase flow simulator LedaFlow with the liquid volume fraction and pressure gradient as output variables. Two datasets were analyzed. Both describe two-phase gas-liquid flow, but are otherwise fundamentally different. One is gas-dominated stratified/annular flow and the other is liquid-dominated slug flow.

在工业流程模拟器中，预测的不确定性源于输入变量的不确定性以及对模型规格，尤其是封闭定律的不确定性。在本研究中，对每个封闭定律的不确定性进行了随机变量的建模，并优化其分布参数以准确量化预测的不确定性。我们开发了两种优化方法，基于积分二次距离和能量分数。所提出的方法应用于商业多相流模拟器 LedaFlow，以液体体积分数和压力梯度作为输出变量。分析了两个数据集。两者均描述了气液两相流动，但本质上存在根本差异。其中一个为以气体为主的分层/环形流动，另一个为以液体为主的段塞流动。