Corpus ID: 236429057

A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations

  title={A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations},
  author={Pedram Maleki and Jingye Tan and Lu An and Massimigliano Di Luigi and Umberto Villa and Chi Zhou and Shenqiang Ren and Danial Faghihi},
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… Expand


Using Bayesian framework to calibrate a physically based model describing strain-stress behavior of TRIP steels
This work presents a Bayesian framework for both the calibration of physical models as well as the quantification of likely uncertainty in experimental observations and applies it to a model for the plastic response of multi-phase Transformation Induced Plasticity (TRIP) steels. Expand
Uncertainty propagation in reduced order models based on crystal plasticity
Abstract In this work, an uncertainty propagation study is performed based on simulated ensembles of statistical volume elements (SVE) used to inform a reduced order internal state variable model,Expand
Bayesian inference of ferrite transformation kinetics from dilatometric measurement
Abstract A Bayesian approach is presented for clarifying the best kinetic model explaining the transformation kinetics of a low-carbon steel under different continuous cooling conditions only fromExpand
A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty
The outcomes of this study indicate that the discrete-continuum multiscale model can accurately simulate the plastic deformation of micro-pillars, despite the significant uncertainty in the DDD results. Expand
A generalized poroelastic model using FEniCS with insights into the Noordbergum effect
A poroelastic model built within the framework of FEniCS is presented which solves the system monolithically and can produce a continuous and mass-conserving solution for specific discharge and suggests a novel explanation of the physical mechanism generating the Noordbergum effect: strain gradients. Expand
Bayesian probabilistic prediction of precipitation behavior in Ni-Ti shape memory alloys
Abstract Ni-Ti alloys are the most popular shape memory alloys in different industrial applications due to their especial properties provided, for at least some variants, by the precipitation ofExpand
Real-time inference of stochastic damage in composite materials
Abstract This study describes a control system designed for real-time monitoring of damage in materials that employs methods and models that account for uncertainties in experimental data andExpand
A Bayesian approach to 2D triple junction modeling
Abstract We propose a Bayesian analytical approach to evaluate the 2D local transition probabilities model developed by Frary and Schuh in 2004 [1] . Their model characterizes the statisticalExpand
Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach
An approach to inversely quantify the uncertainty of MCFD simulations through a data-driven modular Bayesian inference is presented, and a forward uncertainty propagation of the MCFD solver with the obtained uncertainties shows that the agreement between the solver predictions and experimental measurements are significantly improved. Expand
Maximum entropy-based uncertainty modeling at the elemental level in linear structural and thermal problems
AbstractA novel approach is proposed for the modeling of uncertainties in finite element models of linear structural or thermal problems. This uncertainty is introduced at the level of each finiteExpand