A predictive multiphase model of silica aerogels for building envelope insulations
A predictive multiphase model of silica aerogels for building envelope insulations
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DOI：
10.1007/s00466-022-02150-5
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发表时间：
2021-07
影响因子：
4.1
通讯作者：
J. Tan;Pedram Maleki;Lu An;M. Di Luigi;Umberto Villa;Chi Zhou;Shenqiang Ren;D. Faghihi
中科院分区：
文献类型：
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作者：
J. Tan;Pedram Maleki;Lu An;M. Di Luigi;Umberto Villa;Chi Zhou;Shenqiang Ren;D. Faghihi
这项工作开发了一个系统的不确定性量化框架，以评估在存在不完整测量数据和建模误差的情况下基于物理的材料模型所提供的预测的可靠性。该框架由全局敏感性分析、贝叶斯推理和通过计算模型的不确定性前向传播组成。该框架在新型多孔二氧化硅气凝胶材料的新多相模型上的实施被证明可以预测建筑围护结构隔热组件的热机械性能。不确定性分析依赖于采样方法，包括马尔可夫链蒙特卡罗和多相模型的混合有限元解。这项工作的显着特点是研究贝叶斯反演中的新噪声模型，以防止有偏差的估计并表征各种不确定性来源，例如测量变异性、模型在捕获微观结构随机性方面的不足以及理论模型和数值解产生的建模误差。
This work develops a systematic uncertainty quantification framework to assess the reliability of prediction delivered by physics-based material models in the presence of incomplete measurement data and modeling error. The framework consists of global sensitivity analysis, Bayesian inference, and forward propagation of uncertainty through the computational model. The implementation of this framework on a new multiphase model of novel porous silica aerogel materials is demonstrated to predict the thermomechanical performances of a building envelope insulation component. The uncertainty analyses rely on sampling methods, including Markov-chain Monte Carlo and a mixed finite element solution of the multiphase model. Notable features of this work are investigating a new noise model within the Bayesian inversion to prevent biased estimations and characterizing various sources of uncertainty, such as measurements variabilities, model inadequacy in capturing microstructural randomness, and modeling errors incurred by the theoretical model and numerical solutions.