A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations

TL;DR

This paper presents a comprehensive multiphase thermomechanical model of silica aerogels for building insulation, incorporating uncertainty analysis and Bayesian inference to improve predictive accuracy and understand parameter sensitivities.

Contribution

It introduces a novel noise model within Bayesian inversion and applies Sobol sensitivity analysis to silica aerogel modeling, enhancing understanding of parameter impacts.

Findings

Solid conductivity and elasticity are the most influential parameters.

The model predicts thermal data with 2% error despite microstructural randomness.

The model is less effective in simulating stress-strain behavior, leading to high uncertainty.

Abstract

This work develops a multiphase thermomechanical model of porous silica aerogel and implements an uncertainty analysis framework consisting of the Sobol methods for global sensitivity analyses and Bayesian inference using a set of experimental data of silica aerogel. A notable feature of this work is implementing a new noise model within the Bayesian inversion to account for data uncertainty and modeling error. The hyper-parameters in the likelihood balance data misfit and prior contribution to the parameter posteriors and prevent their biased estimation. The results indicate that the uncertainty in solid conductivity and elasticity are the most influential parameters affecting the model output variance. Also, the Bayesian inference shows that despite the microstructural randomness in the thermal measurements, the model captures the data with 2% error. However, the model is inadequate in simulating the stress-strain measurements resulting in significant uncertainty in the computational prediction of a building insulation component.