Continuous-Time Emulation of Large Distributed Parameter Dispersion Models.

Abstract

The paper discusses the emulation of large, distributed parameter, computer models by low order, continuous-time, transfer function models obtained using the SRIVC method of identification and estimation for continuous-time models. This yields a minimally parameterized, reduced order, ‘nominal’ emulation model that often reproduces the dynamic behavior of the large model to a remarkable degree. In full Dynamic Model Emulation (DEM), the objective is to emulate the high order model over a whole, user-defined range of parameter values, so that it can act as a surrogate for the high order model in applications that demand fast, repeated solution, as in Monte Carlo simulation and sensitivity analysis, or be used as a low order model in automatic control system design and adaptive forecasting applications. Most of the paper deals with the ‘stand-alone’ emulation of two high order, distributed parameter, computer models for the transport and dispersion of solutes in water systems.