Effect of Heat Input and Filling Ratio on Raise in Temperature of the Oscillating Heat Pipe with Different Working Fluids Using ANN Model

Abstract

In the present article, feed forward multilayer perceptron neural network (FFMLPNN) model has been used to predict the rise in temperature in closed loop oscillating heat pipe filled with three different fluid i.e., Acetone, methanol and ethanol respectively. Experimental test was carried out for the inner diameter of 1.7mm copper tube for all the combinations of filling ratio, heat input and time taken to evaluate the performance of the OHP. Totally 2000 data sets have been used for Acetone and Methanol, 1500 data sets is used for ethanol in the present NN model. ANN model with FFMLPNN using three input parameter (Filling ratio, heat input and time taken) and rise in temperature has output parameter respectively. Levenberg-Marquardt algorithm with a 4-10 neurons has been used for the determination of optimal model. The 3-8-1 combinations predict the rise in temperature for ethanol and acetone whereas for methanol 3-7-1 is the optimal combinations was achieved. For all the combinations RMSE values are 0.3414, 0.1285 and 0.1237 (Training-70%), 0.3526, 0.1375, 0.1234 (testing-15%) and 0.3010, 0.1515, 0.1425 (validation-15%). The values for coefficient of determinations are 0.9941, 0.9975 and 0.9971 for methanol, acetone and ethanol was achieved. The results clearly indicated that the proposed MLPANN model can successfully predict the rise in temperature.