Dry-out vapor quality incipience in flow boiling: Empirical correlation development with particle Swarm algorithm

Abstract

With the growing demand for electronic cooling, flow boiling has emerged as a critical mechanism in modern thermal systems. Considering the complexity of flow boiling mechanism, which is influenced by many parameters such as operating conditions, tube geometry, type of refrigerant and other factors, predicting parameters such as critical heat flux and vapor quality at the incipience of dry-out is rather challenging and often requires high computational effort. Concluding a comprehensive review of studies on flow boiling dry-out, the current article garners 418 data points regarding vapor quality at the incipience of dry-out in flow boiling of refrigerants inside tubes, with diameters from 0.6 to 6 mm. Heat fluxes, mass fluxes and reduced pressures in the database ranged from 5 to 400 kW m⁻², 150 to 1500 kg m⁻² s⁻¹ and 0.1 to 0.9 respectively. Utilizing the Particle Swarm Algorithm and seven dimensionless numbers which influence the flow boiling mechanism, a new empirical correlation is proposed which can achieve an accuracy of 15.9 % mean average error. A secondary database comprised of 133 data points is also collected for validation of the model. The proposed model of vapor quality at the incipience of dry-out is considerably accurate and facilitates the predictions of dry-out for tubes with inner diameter between 0.6 mm and 3 mm.