Multi-objective optimization of helical baffles in the shell-and-tube heat exchanger by computational fluid dynamics and genetic algorithm

Abstract

Baffles are significant components to control shell-side flow distribution and enhance heat transfer in the shell-and-tube heat exchangers (STHXs). The helical baffles have become popular as a suitable alternative to segmental baffles The thermal-hydraulic performance in the shell and tube heat exchanger with helical baffles was modeled by the computational fluid dynamics (CFD) technique. The effects of the two main geometrical parameters including baffle pitch and baffle angle on heat transfer coefficient and pressure drop were studied. The CFD modeling results were validated with experimental data. The validated data were employed to develop predictive correlations. Finally, the optimum geometries of the investigated helical baffles were suggested using multi-objective genetic algorithm (MOGA) optimization. The optimum results presented a trade-off between heat transfer coefficient and pressure drop in the heat exchangers.