PNNL

Abstract

We study parametric uncertainties involved in plasma flows and apply stochastic sensitivity analysis to rank the importance of all inputs to guide large-scale stochastic simulations. Specifically, we employ different gradient-based sensitivity methods, namely Morris, multi-element probabilistic collocation method (ME-PCM) on sparse grids, Quasi-Monte Carlo, and Monte Carlo methods. These approaches go beyond the standard ``One-At-a-Time" sensitivity analysis and provide a measure of the nonlinear interaction effects for the uncertain inputs. The objective is to perform systematic stochastic simulations of plasma flows treating only as {\em stochastic processes} the inputs with the highest sensitivity index, hence reducing substantially the computational cost. Two plasma flow examples are presented to demonstrate the capability and efficiency of the stochastic sensitivity analysis. The first one is a two-fluid model in a shock tube while the second one is a one-fluid/two-temperature model in flow past a cylinder.