Surrogate model‐based strategy for cryogenic cavitation model validation and sensitivity evaluation

Abstract

Cryogenic cavitation experiences phase change in an environment where the vapor pressure is temperature dependent. The cavitation dynamics have critical implications on the performance and safety of liquid rocket engines, but there is no established method to estimate the actual loads due to cavitation on the inducer blades. To help develop such a computational capability, we conduct a systematic investigation of a transport-based, homogeneous cryogenic cavitation model for code validation and model improvement exercises. We assess the role of model parameters in the cavitation model and uncertainties in material properties via global sensitivity analysis coupled with multiple surrogate models including polynomial response surface, radial basis neural network, Kriging and a weighted average composite model. The results indicate that while the predictions are more sensitive to changes in cavitation model parameters than uncertainties in material properties, the impact of uncertainty in temperature dependent vapor pressure on the performance is significant. We calibrate the cryogenic cavitation model parameters using a multiple surrogates-based optimization strategy. The optimal parameters increase the importance of condensation terms and show improved prediction performance on a number of benchmark problems.

Modeling of turbulent, isothermal and cryogenic cavitation under attached conditions

“…Equation 3 clearly shows the temperature dependency of cavitation, and the local temperature drop in cryogenic cavitation will produce a noticeable rise for the local cavitation number σ and hence suppress the cavitation intensity compared with that under isothermal assumption [9]. The detail impact for the thermal-sensible material properties to cavitation model will be introduced later.…”

“…For cryogenic cavitation, the actual local cavitation number σ needs to be corrected according to the local temperature: σ = (P ∞ − P v (T ))/0.5ρ l U 2 ∞ . Utturkar et al [1] and Goel et al [9] use the first order approximation as follows…”

“…As the results, in order to maintain comparable cavity size (keep B-factor as a constant), there will be substantial temperature drop T due to the greater heat transfer involved for Table 1 Variation of physical properties for water (298 K), liquid nitrogen (83 K), and liquid hydrogen (22 K) on saturation curves [6] Substance C p /(J kg −1 K −1 ) Density ratio Thermal conductivity/(W m −1 K −1 ) L/(kJ kg −1 ) v cryogenic liquids (larger reference temperature drop T * ) [1,8,9]. Equations 1 and 2 clearly show that the isothermal assumption, which we often use for water under room temperature, is inappropriate for cryogenic cavitation.…”

“…The cavitation model in this category [1,9,[36][37][38][39][40][41][42][43][44][45][46][47][48][49] can be generally termed as…”

“…The apparent advantage in transport-based cavitation model is that this model comes from the convective character of equation, which allows modeling of the impact of inertial forces and the detachment or drift of bubbles. Numerically, [1,9,36,40,41,44] utilized pressure-based methods, and [38,39,[45][46][47][48][49] employed the density-based methods.…”