A good contribution of computational fluid dynamics (CFD) and GA-ANN methods to find the best type of helical wire inserted tube in heat exchangers

Researchers from all over the places have been reporting their experimental and simulation studies on thermal analysis of enhanced heat-exchangers, such as coiled wire inserts. If those researchers decide to optimize the heat-exchangers' performances under specific conditions, inevitably they will be in need of extracting an empirical or semi-empirical correlation to facilitate their calculations. Developing a correlation for a complex thermal system with loads of variables such as coiled wire inserts is truly hard and somewhat an impossible task. This paper steps forward to reveal the success of artificial neural network (ANN) and genetic algorithm (GA) together in this especial case. To do so, a three dimensional numerical simulation of the fluid flow under non-isothermal condition was initially proposed using computational fluid dynamics (CFD) method. After validation of the numerical model, twelve wire coil inserted tubes were tested through the validated model to get their heat transfer and friction coefficients at specific Reynolds ranges. Then, a prosperous ANN configuration was chosen for simulating the heat-exchangers and then obtaining a continuous function to get valuable output data with any values of input variables. Finally, to answer which type of the heat-exchangers works better, the optimization technique of GA was used along with the ANN's model. Results reveal the fact that while a suitable helical wire empowers the heat transfer efficiency, wrong choice of wire inserts may decrease the overall enhancement efficiency of the heat-exchanger unexpectedly.