The work focuses on numerical simulation of the cavitating flow inside high-pressure injectors. Cavitation is modeled through a transport equation for the void fraction closed by the Schnerr-Sauer relation, containing four free parameters. As for turbulence, the URANS equations are considered, together with two different closure models. The sensitivity of URANS predictions to the parameters contained in the cavitation model is investigated for a throttle geometry, for which experimental and LES data are available. In order to obtain continuous response surfaces of the quantities of interest in the parameter space starting from a limited number of simulations, a stochastic approach is adopted. First, two out of the four parameters are identified as the most important through a preliminary analysis based on 2D simulations. Then, the sensitivity of 3D simulation results to the previously identified most important parameters is investigated. The stochastic range of variability of the results contains the reference data. Finally, a parameter optimization is carried out in order to obtain the values giving the best agreement with the LES data.
Stochastic sensitivity analysis of numerical simulations of high-pressure injectors to cavitation modeling parameters
Anderlini, A.
Primo
;Salvetti, M. V.Secondo
;
2017-01-01
Abstract
The work focuses on numerical simulation of the cavitating flow inside high-pressure injectors. Cavitation is modeled through a transport equation for the void fraction closed by the Schnerr-Sauer relation, containing four free parameters. As for turbulence, the URANS equations are considered, together with two different closure models. The sensitivity of URANS predictions to the parameters contained in the cavitation model is investigated for a throttle geometry, for which experimental and LES data are available. In order to obtain continuous response surfaces of the quantities of interest in the parameter space starting from a limited number of simulations, a stochastic approach is adopted. First, two out of the four parameters are identified as the most important through a preliminary analysis based on 2D simulations. Then, the sensitivity of 3D simulation results to the previously identified most important parameters is investigated. The stochastic range of variability of the results contains the reference data. Finally, a parameter optimization is carried out in order to obtain the values giving the best agreement with the LES data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.