Nonlinear model reduction strategies for rapid thermal processing systems

We present a systematic method for developing low order nonlinear models from physically based, large scale finite element models of rapid thermal processing (RTP) systems. These low order models are extracted from transient results of a detailed finite element model using the proper orthogonal decomposition (POD) method. Eigenfunctions obtained from the POD method are then used as basis functions in spectral Galerkin expansions of the governing partial differential equations solved by the finite element model to generate the reduced models. Simulation results with the reduced order models demonstrate good agreement with steady state and transient data generated from the finite element model, with an order of magnitude reduction in execution time.