Identification of reduced order average linear models from nonlinear dynamic simulations

Presents a general methodology for the identification of average linear low order models (ALLOM) from data collected from detailed nonlinear models. While there are many methods available in the literature for identifying linear models, these methods tend to produce inaccurate and ill-conditioned models when used on nonlinear data sets. The method in this paper differs from traditional linearization methods in that it better approximates the dynamic characteristics over a wider area around the reference steady state