An Efficient Computational Framework For Uncertainty Quantification In Multiscale Systems
| dc.contributor.author | Ma, Xiang | en_US |
| dc.contributor.chair | Zabaras, Nicholas John | en_US |
| dc.contributor.committeeMember | Mukherjee, Subrata | en_US |
| dc.contributor.committeeMember | Koutsourelakis, Phaedon-Stelios | en_US |
| dc.contributor.committeeMember | Samorodnitsky, Gennady | en_US |
| dc.date.accessioned | 2013-07-23T18:20:33Z | |
| dc.date.available | 2013-07-23T18:20:33Z | |
| dc.date.issued | 2011-01-31 | en_US |
| dc.description.abstract | To accurately predict the performance of physical systems, it becomes essential for one to include the effects of input uncertainties into the model system and understand how they propagate and alter the final solution. The presence of uncertainties can be modeled in the system through reformulation of the governing equations as stochastic partial differential equations (SPDEs). The spectral stochastic finite element method (SSFEM) and stochastic collocation methods are the most popular simulation methods for SPDEs. However, both methods utilize global polynomials in the stochastic space. Thus when there are steep gradients or finite discontinuities in the stochastic space, these methods converge slowly or even fail to converge. In order to resolve the above mentioned issues, an adaptive sparse grid collocation (ASGC) strategy is developed using piecewise multi-linear hierarchical basis functions. Hierarchical surplus is used as an error indicator to automatically detect the discontinuity region in the stochastic space and adaptively refine the collocation points in this region. However, this method is limited to a moderate number of random variables. To address the solution of high-dimensional stochastic problems, a computational methodology is further introduced that utilizes the High Dimensional Model Representation (HDMR) technique in the stochastic space to represent the model output as a finite hierarchical correlated function expansion in terms of the stochastic inputs starting from lower-order to higher-order component functions. An adaptive version of HDMR is also developed to automatically detect the important dimensions and construct higherorder terms using only the important dimensions. The ASGC is integrated with HDMR to solve the resulting sub-problems. Uncertainty quantification for fluid transport in porous media in the presence of both stochastic permeability and multiple scales is addressed using the developed HDMR framework. In order to capture the small scale heterogeneity, a new mixed multiscale finite element method is developed within the framework of the heterogeneous multiscale method in the spatial domain. Several numerical examples are considered to examine the accuracy of the multiscale and stochastic frameworks developed. A summary of suggestions for future research in the area of stochastic multiscale modeling are given at the end of the thesis. | en_US |
| dc.identifier.other | bibid: 8213959 | |
| dc.identifier.uri | https://hdl.handle.net/1813/33485 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Uncertainty Quantification | en_US |
| dc.subject | Stochastic | en_US |
| dc.subject | Collocation | en_US |
| dc.subject | Adaptive Sparse Grid | en_US |
| dc.subject | High | en_US |
| dc.subject | Dimensional Model Representation | en_US |
| dc.title | An Efficient Computational Framework For Uncertainty Quantification In Multiscale Systems | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Mechanical Engineering | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Mechanical Engineering |
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