Heat transfer of silver/water nanofluid in a two-phase closed thermosyphon which is thermally enhanced by magnetic field has been predicted by optimized artificial Neural Network. Artificial neural network is a technique with flexible mathematical structure which is capable of identifying complex non-linear relationship between input and output data. A multi layer perception neural network was used to estimate the thermal efficiency and resistance of thermosyphon during applying magnetic field and using nanoparticle in the water as a working fluid. The magnetic field strength, volume fraction of nanofluid in water and inlet power was used as input parameters and the thermal efficiency and thermal resistance were used as output parameters. The results were compared with experimental data and it was found that the estimated thermal efficiency and resistance by multi layer perception neural network is accurate. It has been observed that the GA-ANN (Genetic Algorithm- Artificial Neural network) predicts the thermosyphon behavior correctly within the given range of training data. In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm, has been used to predict collection output of closed thermosyphon.

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