Complexity Reduction for Parametrized Catalytic Reaction Model

This work presents an application of a nonlinear model reduction approach to decrease the complexity in simulating a steady-state catalytic reactions, which are essential in facilitating many chemical processes. This approach is based on combining the proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM). This work illustrates the applicability of the POD-DEIM approach with the finite volume discretization. POD is used to generate a low dimensional basis set that captures the dominant behaviour of the solutions from the finite volume discretization with various parameter values, and hence provides a substantial reduction in the number of unknowns. Due to the nonlinearity of this problem, this work also applies DEIM to reduce the complexity in computing the POD projected nonlinear term. The numerical experiments demonstrate the accuracy and efficiency of these model reduction approaches through the parametric study of catalytic reactions.