Model reduction-based optimization using large-scale steady-state simulators

Most engineering systems can be accurately simulated using models consisting of Partial Differential Equations. Thus the challenging problem of PDE-constrained optimization arises naturally in engineering design. Issues surface due to the high number of variables involved and the use of specialized software for simulation which may not include an optimization option. In this work we present a methodology for the steady-state optimization of systems for which 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, first onto the low-dimensional, adaptively computed, dominant subspace of the system and second onto the subspace of independent variables. Hence only low order Jacobian and Hessian matrices are used in this formulation, computed efficiently with directional perturbations.

► We construct a new reduced optimization method for large-scale input/output systems.
► We exploit systems dissipativity to adaptively compute low-order dominant subspaces.
► We project large-scale system Jacobian/Hessian matrices onto the reduced subspaces.
► We make a 2nd projection on the decision variables subspace to get reduced Hessians.
► We illustrate this methodology using a tubular reactor model as a case study.