When using a chain of numerical models in a stochastic simulation, the distribution of the observed output depends on both the input parameter uncertainty and the errors of the individual models in the chain. In this work, the propagation of model uncertainty is studied in a simple one-dimensional heat transfer system. The errors in temperature are found to depend on the heat flux coupling scenario and on the type of the input parameter distributions. The radiation heat flow boundary condition limits the error propagation by compensating the gas temperature errors through enhanced heat losses. Model biases were found to be detrimental to the accuracy of the predicted probabilities of exceeding safety criteria. Finally, corrections to the predicted distribution moments are proposed and tested, showing that the error contributions can be effectively eliminated from the observed distributions if the properties of the individual models are well known.

中文翻译：

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使用确定性模型链在随机传热模拟中传播建模不确定性

在随机模拟中使用数值模型链时，观察到的输出分布取决于输入参数的不确定性和链中各个模型的误差。在这项工作中，模型不确定性的传播在一个简单的一维传热系统中进行了研究。发现温度误差取决于热通量耦合方案和输入参数分布的类型。辐射热流边界条件通过增强热损失补偿气体温度误差来限制误差传播。发现模型偏差对超出安全标准的预测概率的准确性有害。最后，提出并测试了对预测分布矩的修正，