RSM and ANN Modeling Techniques to Forecast How Various Parameters will Affect the Improvement of Electronics Cooling using Radial Heat Sink.

This research provides the mathematical modeling for temperature difference for natural convection using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based modelling. Length of fin (L), height of fin (H), number of fins (N) and the heat input (Q) for the Radial heat sink are the parameters selected under natural convection heat transfer. Looking at the pattern of the data, feed forward back propagation type neural network is chosen. The RSM mathematical model of temperature difference is used to compare the performance of the created ANN models. ANN Simulations proved to be successful in terms of agreement with actual values of experimentation. ANN simulations perform accurate to validate the experimental results and the results obtained from the RSM for the output under natural convection. The optimum values for the dimensional parameters namely length of fin, height of the fin, number of fins are obtained by RSM method. Also the optimum operating parameter that is heat input for minimum temperature difference is obtained.