USE OF GENETIC ALGORITHMS IN THERMAL-PROPERTY ESTIMATION - PART II - SIMULTANEOUS ESTIMATION OF THERMAL-PROPERTIES

This two-part study is on the use of genetic algorithms (GAs) to desig n experiments and develop estimation methodologies for the determinati on of thermal properties; Part I is focused on the development of an i mproved GA, called an extended elitist genetic algorithm (EEGA) and on the implementation of this algorithm to optimize experimental designs used in thermal property estimation, while Part II is directed toward the application of this algorithm to the simultaneous estimation of t hermal properties. In Part II the EEGA Is used to minimize a least squ ares objective function containing calculated and measured temperature s. While the EEGA was shown to be an effective strategy for the optimi zation of experiments ill Part I, its potential for use in the estimat ion of thermal properties is shown here in Part II through the use of case studies. In addition, the effect of the choice of the criterion u sed to optimize the experimental designs on the accuracy of the proper ty estimates was analyzed for one of the case studies. These Ease stud ies include the simultaneous estimation of two, three, and five therma l properties, with some of them being highly correlated. Correlation o r near-correlation among simultaneously estimated properties can be a limiting factor of commonly used gradient-based methods. The results f rom the analysis of the case studies demonstrate that the proposed GA is a useful tool in the simultaneous estimation of correlated thermal properties.