Optimization and design of cooling systems using a hybrid neural network and genetic algorithm methodology

In this paper a novel method for the design and optimization of cooling systems is presented. The numerical solution of free. convection from a heated horizontal cylinder confined between adiabatic walls obtained from a finite element solver is used to propose a non-linear heat transfer model of GMDH type approach. In the context of GMDH model, three different methods depending on the structure of neural network are implemented. The system of orthogonal equations is solved using a SVD scheme. The coefficients of second order polynomials are computed and their behavior is discussed. In addition, to demonstrate the performance of the predicted model, the numerical data are divided into trained and prediction data, respectively. The model is based on trained data and it is validated using the prediction data. In the next step, using the above-mentioned model and the genetic algorithms, the optimum coefficient of heat transfer is obtained. The results reveal the robustness and excellent performance of the hybrid procedure introduced in this paper.