Enhancing Cryogenic Cavitation Prediction Through Incorporating Modified Cavitation and Turbulence Models

Cavitating flow prediction is essential for designing cavitation-resistant hydraulic machines. Despite the advances achieved in normal-temperature cavitation prediction, cryogenic cavitation prediction has remained a challenging task in which thermal effects play a significant role. This study aims to enhance the prediction of cryogenic cavitation, and both the cavitation and turbulence models are improved simultaneously. The original cavitation model embedded in the CFX flow solver is modified by incorporating additional source terms (such as mass and heat transfer rates) for dual evaporation and condensation processes. The renormalization group k–ε turbulence model is modified on the basis of the filter-based turbulence model and density correction method to permit a smooth prediction of turbulence eddy viscosity, which mitigates the overestimation of the turbulence length scale in the cryogenic cavity (which is intrinsic to the original renormalization group k–ε turbulence model). The modified cavitation and turbulence models are implemented through CFX Expression Language (CEL) within the CFX frame. To verify the modified models and the enhancement of cryogenic cavitation prediction, Hord's liquefied nitrogen (LN2) and liquefied hydrogen (LH2) experiments over a hydrofoil and ogive are used, and cavitating flow simulation is conducted for each of the test cases. When using the modified models, the predicted temperature and pressure curves agree well with the measured values, and the predicted cavity lengths are much closer to the measured lengths. It is proven that the cryogenic cavitating flow can be well depicted by the modified models.