Computational modeling of boiling droplets of multi-component fuels integrating bubble evolution: A review and enhancement

Abstract

With the utilization of new practical fuels in engines, including mixed fuels, the droplet evaporation model may not adequately describe the phenomenon of the multi-component fuel droplets undergoing boiling at high ambient temperatures due to the large difference in the boiling points between the different components. Therefore, the construction of the droplet boiling model becomes important to broaden the applicability of the droplet vaporization model in engine simulations. In this study, a comprehensive evaluation framework for the boiling droplet model, which integrates bubble evolution, is constructed. The available sub-models of bubble nucleation, growth, and breakup are analyzed and evaluated. Then, the validation and comparison of the droplet vaporization model integrating bubble evolution for multi-component fuels are systematically conducted under wide conditions. The results indicate that the accuracy of the bubble evolution sub-models determines the prediction accuracy of the droplet boiling model. The enhanced bubble sub-models, taking into account the effects of the liquid viscosity, surface tension, and molecular diffusion of the multi-component droplet, show satisfactory performance in predicting the boiling behavior of the multi-component droplets, and reduce the choice of empirical parameters in applications. The updated droplet vaporization model integrating the enhanced bubble sub-models aligns more consistently with the actual physical processes at high-temperature environments, enabling quantitative reproduction of the fluctuation and evolution of the droplet diameter in the boiling stage. Moreover, it is found that the accuracy of the droplet vaporization model is significantly influenced by the prediction of the boiling critical point of the multi-component mixture, and the change of the bubble point of the liquid mixture with the liquid composition and operating conditions should be considered.