Prediction of temperature performance of a two-phase closed thermosyphon using Artificial Neural Network

Here, the temperature performance of a two-phase closed thermosyphon (TPCT) was investigated using two synthesized nanofluids, including carbon nano-tube (CNT)/water and CNT-Ag/water. In order to determine the temperature performance of a TPCT, the experiments were performed for various values of weight fraction and input power. To predict the other experimental conditions, a reliable and accurate tool should be applied. Therefore Artificial Neural Network (ANN) was applied to predict the process performance. Using ANN, the operating parameters, including distribution of wall temperature (T) and the temperature difference between the input and the output water streams of condenser section (∆T) were determined. To achieve this goal, the multi-layer perceptron network was employed. The Levenberg–Marquardt algorithm was chosen as learning algorithm of this network. The results of simulation showed an excellent agreement with the data resulted from the experiments. Therefore it is possible to say that ANN is a powerful tool to predict the performance of different processes.