...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy and Buildings >Quantifying uncertainty in thermophysical properties of walls by means of Bayesian inversion
【24h】

Quantifying uncertainty in thermophysical properties of walls by means of Bayesian inversion

机译：通过贝叶斯反演量化壁的热物理性质的不确定性

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We introduce a computational framework to statistically infer thermophysical properties of any given wall from in-situmeasurements of air temperature and surface heat fluxes. The proposed framework uses these measurements, within a Bayesian calibration approach, to sequentially infer input parameters of a one-dimensional heat diffusion model that describes the thermal performance of the wall. These inputs include spatially-variable functions that characterise the thermal conductivity and the volumetric heat capacity of the wall. We encode our computational framework in an algorithm that sequentially updates our probabilistic knowledge of the thermophysical properties as new measurements become available, and thus enables an on-the-fly uncertainty quantification of these properties. In addition, the proposed algorithm enables us to investigate the effect of the discretisation of the underlying heat diffusion model on the accuracy of estimates of thermophysical properties and the corresponding predictive distributions of heat flux. By means of virtual/synthetic and real experiments we show the capabilities of the proposed approach to (i) characterise heterogenous thermophysical properties associated with, for example, unknown cavities and insulators: (ii) obtain rapid and accurate uncertainty estimates of effective thermal properties (e.g. thermal transmittance): and (iii) accurately compute an statistical description of the thermal performance of the wall which is, in turn, crucial in evaluating possible retrofit measures. (C) 2018 Elsevier B.V. All rights reserved.
机译：我们引入了一个计算框架，可以通过现场测量空气温度和表面热通量来统计推断任何给定壁的热物理性质。提出的框架在贝叶斯校准方法中使用这些测量值，以顺序推断描述墙体热性能的一维热扩散模型的输入参数。这些输入包括空间可变函数，这些函数描述了壁的热导率和体积热容。我们使用一种算法对我们的计算框架进行编码，该算法会在新的测量结果可用时顺序更新我们对热物理性质的概率知识，从而能够对这些性质进行实时不确定性量化。另外，提出的算法使我们能够研究基础热扩散模型的离散化对热物理性质估计值和相应的热通量预测分布精度的影响。通过虚拟/合成和真实实验，我们证明了所提出方法的能力（i）表征与例如未知型腔和绝缘子相关的异质热物理性质：（ii）快速而准确地确定有效热性质的不确定性估计（例如，热透射率）：和（iii）准确计算壁的热性能的统计描述，这反过来对于评估可能的改造措施至关重要。 （C）2018 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号