Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation

Abstract

A projection-based formulation is presented for non-linear model reduction of problems with extreme scale disparity. The approach allows for the selection of an arbitrary, but complete, set of solution variables while preserving the conservative form of the governing equations. Least-squares-based minimization is leveraged to guarantee symmetrization and discrete consistency with the full-order model (FOM) at the sub-iteration level. Two levels of scaling are used to achieve the conditioning required to effectively handle problems with extremely disparate physical phenomena, characterized by extreme stiffness in the system of equations. The formulation -referred to as structure-preserving least-squares with variable transformation (SP-LSVT) -provides global stabilization for both implicit and explicit time integration schemes. To achieve computational efficiency, a pivoted QR decomposition is used with oversampling, and adapted to the SP-LSVT method. The framework is demonstrated in representative two-and three-dimensional reacting flow problems, and the SP-LSVT is shown to exhibit improved stability and accuracy over standard projection-based ROM techniques. Physical realizability is promoted by enforcing limiters in both temperature and species mass fractions, thus contributing to local stability enhancement. These limiters are demonstrated to be important in eliminating regions of spurious burning, thus enabling the ROMs to provide accurate representations of the heat release rate and flame propagation speed. In the 3D application, it is shown that more than two orders of magnitude acceleration in computational efficiency can be achieved, while also providing reasonable future-state predictions. A key contribution of this work is the development and demonstration of a comprehensive ROM formulation that targets highly challenging multi-scale transport-dominated problems.

Data Driven Models for the Design of Rocket Injector Elements

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Efficient Space–Time Reduced Order Model for Linear Dynamical Systems in Python Using Less than 120 Lines of Code

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“…Part of the challenge is due to the high computational cost of solving stiff ODEs as well as handling the ill-conditioned gradients of loss functions with respect to neural network weights (Anantharaman et al, 2020). In addition, it has been shown that stiffness could lead to failures in many data-driven modeling approaches, such as reduced order of modeling (Huang et al, 2020) and physics-informed neural networks . It was suggested that the stiffness could lead to gradient pathologies and ill-conditioned optimization problems, which leads to the failure of stochastic gradient descent based optimization (Wang et al, 2020).…”