Reduced Order Optimization of Large-Scale Nonlinear Systems with Nonlinear Inequality Constraints Using Steady State Simulators

Abstract

Technological advances have led to the widespread use of computational models of increasing complexity, in both industry and everyday life. This helps to improve the design, analysis and operation of complex systems. Many computational models in the field of engineering consist of systems of coupled nonlinear partial differential equations (PDEs). As a result, optimization problems involving such models may lead to computational issues because of the large number of variables arising from the spatiotemporal discretization of the PDEs. In this work, we present a methodology for steady-state optimization, with nonlinear inequality constraints of complex large-scale systems, for which only an input/output steady-state simulator is available. The proposed method is efficient for dissipative systems and is based on model reduction. This framework employs a two-step projection scheme followed by three different approaches for handling the nonlinear inequality constraints. In the first approach, partial reductio...