Nonlinear programming applied to calibrating thermal and fluid models to test data

Thermal modeling is fraught with uncertainties such as film coefficients, contact resistances, dissipation rates, and effective conductances and capacitances of complex components. Adjusting the values of uncertainties in a thermal/fluid model to achieve a better fit with test data is a necessary step; this procedure is even codified into military standards for electronic equipment design, for example. Nonetheless, such "correlation" or "calibration" activities are typically done haphazardly and without any mathematical rigor, and are often impeded rather than aided by software. This paper shows how readily available nonlinear programming (NLP) techniques that were developed for optimization problems have been successfully used to automate this critical but laborious calibration task. This paper briefly introduces NLP concepts, and then demonstrates their application both to a simplified curve-fitting exercise as well as a real case: a transient with a serpentine condenser plate.