[1] In this work, we propose a new collocation method for uncertainty quantification in strongly nonlinear problems. Based on polynomial construction, the traditional probabilistic collocation method (PCM) approximates the model output response, which is a function of the random input parameter, from the Eulerian point of view in specific locations. In some cases, especially when the advection dominates, the model response has a strongly nonlinear profile with a discontinuous shock or large gradient. This nonlinearity in the space domain is then translated to nonlinearity in the random parametric domain, which causes nonphysical oscillation and inaccurate estimation using the traditional PCM. To address this issue, a new location-based transformed probabilistic collocation method (xTPCM) is developed in this study, inspired by the Lagrangian point of view, in which model response is represented by an alternative variable, i.e., the location of a particular response value, which is relatively linear to the random parameter with a smooth profile. The location is then approximated by polynomial construction, from which a sufficient number of location samples are randomly generated and transformed back to obtain the response samples and to estimate the statistical properties. The advantage of the xTPCM is demonstrated through applications to multiphase flow and solute transport in porous media, which shows that the xTPCM achieves higher statistical accuracy than does the PCM, and produces more reasonable realizations without oscillation, while computational effort is greatly reduced compared to the direct sampling Monte Carlo method.
The probabilistic collocation method (PCM) is widely used for uncertainty quantification and sensitivity analysis. In paper 1 of this series, we demonstrated that the PCM may provide inaccurate results when the relation between the random input parameter and the model response is strongly nonlinear, and presented a location-based transformed PCM (xTPCM) to address this issue, relying on the transform between response and location. However, the xTPCM is only applicable for one-dimensional problems, and two or three-dimensional problems in homogeneous media. In this paper, we propose a displacementbased transformed PCM (dTPCM), which is valid in two or three-dimensional problems in heterogeneous media. In the PCM, we first select collocation points and run model/simulator to obtain response, and then approximate the response by polynomial construction. Whereas, in the dTPCM, we apply motion analysis to transform the response to displacement. That is, the response field is now represented by the displacement field. Next, we approximate the displacement instead of the response by polynomial, since the displacement is more linear to the input parameter than the response. Finally, we randomly generate a sufficient number of displacement samples and transform them back to obtain response samples to estimate statistical properties. Through multiphase flow and solute transport examples, we demonstrate that the dTPCM provides much more accurate statistics than does the PCM, and requires considerably less computer time than does the Monte Carlo (MC) method.
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