A comprehensive simulation and optimization on heat transfer characteristics of subcooled seawater falling film around elliptical tubes. (5th May 2021) - Wan, Zhihua; Li, Yanzhong; Wang, Sophie
Highlights: CFD models of falling film on horizontal tubes are verified by experimental data. Film thickness and heat transfer on the elliptical tubes are systematically studied. Effects of seawater salinity and tube ellipticity on heat transfer are explored. An effective optimization algorithm combining Kriging RSM and MOGA is adopted. The empirical correlation of Nusselt number considering tube ellipticity is fitted. Abstract: In this paper, 2-D elliptical tube models based on the volume of fluid (VOF) method are established. The effects of tube perimeter, Reynolds number, inlet temperature, seawater salinity, and tube ellipticity (the ratio of the major axis to the minor axis) on the falling film thickness and heat transfer coefficients are studied. A mathematical algorithm combing the Kriging response surface method (RSM) and the multi-objective genetic algorithm (MOGA) is adopted to explore the parameter sensitivity and optimization results. The ranges for parameters are: tube perimeter from 31.4 to 94.2 mm, Reynolds number from 500 to 1500, inlet temperature from 10 to 90 °C, seawater salinity from 0 to 120 g kg −1, tube ellipticity from 1 to 4. The results show the local film thickness increases with Reynolds number and seawater salinity, but decreases with tube perimeter, inlet temperature and tube ellipticity. The increase of Reynolds number, inlet temperature, and tube ellipticity enhance heat transfer, while tube perimeter and seawater salinity have a negative effect. The tube ellipticity has a negligible impact on heat transfer when it exceeds 3.25. The tube perimeter and seawater salinity are the most and least influential factors, and three optimal parameter points are obtained. A correlation considering the effect of tube ellipticity is fitted to predict the overall external heat transfer coefficients, and 97% of prediction data are within 10% relative errors with the numerical results.