Ammonia, as a hydrogen carrier and clean fuel, has an increasingly urgent demand for large-scale transportation. Utilizing the existing refined oil pipeline network for sequential transportation of ammonia and refined oil is an economically and efficiently feasible solution. However, the unique micro-solubility characteristics of ammonia and refined oil can cause significant differences in the mixing mechanism of the two substances during sequential transportation in the pipeline compared to traditional oil products. This study conducts transient flow numerical simulation and mechanism research on the mixing problem during the sequential transportation process of ammonia and refined oil under the influence of micro-solubility transfer. Using the ANSYS Fluent platform and combining it with the dynamic mesh technology, a sequential transportation pipeline model was constructed. In the VOF multiphase flow model framework, the Fick diffusion and convective transfer theories were coupled. Through the development of user-defined functions, a transfer model was established to describe the ammonia dissolution process in refined oil during sequential transportation. This model characterizes the axial transfer process of the two-phase flow and the dissolution transfer in the pipeline. Then, the correctness and accuracy of the transfer model were verified, proving that the model has reliable simulation capabilities. To evaluate the comprehensive influence of various engineering factors on the mixing law, this study selected seven key parameters. It then designed and simulated multiple sets of comparative conditions. The influence of each parameter on the development of the mixing section was analyzed, and a sensitivity analysis was conducted. Subsequently, using the growth rate of the mixing length (dL/dt) as the dependent variable to represent the dynamic development of the mixing process, and using the above seven parameters as independent variables, a semi-empirical fitting formula was established. This formula can comprehensively reflect the coupling effect of multiple factors. The results show that the model has good generalization ability and extrapolation robustness. It provides a prediction model and theoretical tool with certain engineering practical value. This can be used for predicting the amount of mixing and optimizing operating parameters in actual pipeline sequential transportation systems.
Wang et al. (Tue,) studied this question.