Design and optimization of staggered fin structure of heat exchanger based on Machine learning

Abstract

A multi-scale coupling method based on the unit heat transfer model is proposed to simulate the flow heat transfer performance of a heat exchanger, in view of the complex structure of the heat transfer unit model inside the staggered tooth type heat exchanger and the characteristics of coolant flow. The numerical simulation results are compared with experimental results, and the errors of the simulation results under different working conditions are small, indicating that the modelling method is reliable. On this basis, the heat transfer unit model parameters of staggered fins were established by three-dimensional numerical simulations. The heat transfer performance of the heat exchanger under different structures was compared using a comprehensive evaluation factor, i.e., j/f. The results and experimental data show that the best flow heat transfer performance is achieved with a liquid-side fin length of 7.2 mm, an oil-side fin length of 4.8 mm, a liquid-side fin height of 3.1 mm and an oil-side fin height of 3.1 mm, an inlet flow velocity of 0.2 m/s and a flow angle of 90°. Later, an agent model was established using backward propagation (BP) artificial neural network (ANN) to predict the flow heat transfer performance of the heat transfer unit model, and it was concluded that the f-factor, j-factor and j/f predicted by the ANN model matched well with the simulated values, and the predicted trends of different indicators were the same as the real law, which proved that the error between the predicted and actual values was small, and the above structural parameters were the best architectural parameters, which eventually improved the heat transfer performance by 17 %.