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Sylvain Quoilin Chapter 5 : Models In order to discover the best parameters for the whole experiments, an “optimization script” is developed : for a given set of parameters, the model is run successively with the inputs corresponding to each test. The outputs are then compared to the measured values of each test and a “mean error function” is defined. In the case of the expander, this function has the following form : The error function it thus a weighted mean value between the quadratic errors of each predicted output values. This function is calculated for each point. The best set of parameters for the model corresponds to the one minimizing the mean value of the error function calculated on each point. In order to minimize that function, a genetic algorithm is used, the “genes” of the individuals being the parameters of the model. The reason justifying the use of the genetic algorithm is its characteristics of not converging to local minimums. The set of parameter resulting from this optimization is hence supposed to be the best set possible. For this optimization, the number of individuals is set to 32, and the number of generation to 64. This optimization is performed two times : ● One time for the two first set of tests ● A second time for the third set of tests, since the expander has been insulated and its parameters have been modified. 80PDF Image | Modeling of a Low Temperature Rankine Cycle for Small Scale Cogen
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