Bayesian inference of structural error in inverse models of thermal response tests
Bayesian inference of structural error in inverse models of thermal response tests
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DOI：
10.1016/j.apenergy.2018.06.147
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发表时间：
2018-10-15
期刊：
影响因子：
11.2
通讯作者：
Ooka, Ryozo
中科院分区：
文献类型：
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作者：
Choi, Wonjun;Menberg, Kathrin;Ooka, Ryozo
针对地源热泵的设计问题，通过对热响应试验数据的物理模型解释，估算了土壤导热系数和井眼热阻这两个设计参数。在大多数情况下，假设所选模型可以完全再现实际物理响应，则将参数拟合到测量数据。然而，两个重要的误差来源使估计不确定：来自实验的随机误差和描述模型与实际物理现象之间差异的结构偏差误差。一般来说，这两个误差源不会单独评估。因此，所选模型正确推断TRT参数的适用性还没有得到很好的理解。在这项研究中，贝叶斯校准框架提出的Kennedy和O 'Hagan估计地源热泵的设计参数和量化的随机和结构性错误的推断。的校准框架，使我们能够检查结构误差在常用的无限线源模型中产生的条件下，TRT发生。两个原位TRT数据集：TRT 1，从室外环境的背景干扰的影响，和TRT 2，强降雨引起的强地下水流的影响。我们表明，贝叶斯校准框架是能够量化的TRT解释的结构性错误，因此可以产生更准确的估计设计参数与充分量化的不确定性。
For the design of ground-source heat pumps (GSHPs), two design parameters, namely the ground thermal conductivity and borehole thermal resistance are estimated by interpreting thermal response test (TRT) data using a physical model. In most cases, the parameters are fitted to the measured data assuming that the chosen model can fully reproduce the actual physical response. However, two significant sources of error make the estimation uncertain: random error from experiments and structural bias error that describes the discrepancy between the model and actual physical phenomena. Generally, these two error sources are not evaluated separately. As a result, the suitability of selected models to correctly infer parameters from TRTs are not well understood. In this study, the Bayesian calibration framework proposed by Kennedy and O'Hagan is employed to estimate the GSHP design parameters and quantify the random and structural errors in the inference. The calibration framework enables us to examine structural errors in the commonly used infinite line source model arising due to the conditions in which the TRT takes place. Two in situ TRT datasets were used: TRT1, influenced by contextual disturbances from the outdoor environment, and TRT2, influenced by a strong groundwater flow caused by heavy rainfall. We show that the Bayesian calibration framework is able to quantify the structural errors in the TRT interpretation and therefore can yield more accurate estimates of design parameters with full quantification of uncertainties.