Surrogate-based analysis and optimization of the transport-based cryogenic cavitation model

The cryogenic cavitation has critical implications on the performance and safety of liquid rocket engines, with the transport-based model playing an important role in numerical simulations of the cryogenic cavitation. Based on the surrogate model, global sensitivity analysis is conducted to assess the performance of a transport-based cryogenic cavitation model, which takes the model parameters regulating the condensation and evaporation rates, and the uncertainties in material properties, specifically, the vapor density and latent heat, into account. The parameters that significantly affect predictions are characterized and then the cryogenic cavitations model parameters are calibrated. It reveals that the performance of cavitation model is more sensitive to the changes of the condensation term, with the total sensitivity of 44% and 29%, to the prediction of pressure and temperature respectively. As for the evaporation term C_evap, the total sensitivity weights for both objectives are less than 5%. With the surrogate-based analysis and optimization, the performance variation of Merkle cavitation model to the condensation term can be concluded: when the condensation coefficient C_cond is less than 20, the predicting precision of pressure improves and that of temperature weakens with the condensation term increasing, while when the condensation coefficient is larger than 20, the performance of the cavitation model improves with the condensation term decreasing. C_evap=3.8 and C_cond=19.43 are recommended, which belongs to the Pareto optimal solutions, and better prediction of the cryogenic turbulent cavitation can be attained.