Flow prediction and dimensionless feature analysis in orifice microchannels under cavitation conditions based on similarity criteria

A novel set of dimensionless numbers for predicting cavitation flow in gas-liquid flow through a throttling orifice is proposed. Multiple sets of cavitation data are obtained from the literature, and eight dimensionless groups are extracted using dimensional analysis. Subsequently, self-similarity among these groups is established, leading to the proposal of new dimensionless correlations (CfW and CfWd). Investigation results indicate that the established correlations between dimensionless groups can predict cavitation flow in orifices under a wide range of hydrodynamic and geometric conditions. The predictive model is validated using different sets of experimental data, improving accuracy (average relative error decreased by more than 57%) and covering more physical conditions compared to previous correlation predictions. Furthermore, dimensionless parameters with significant influence on the cavitation discharge coefficient are identified by the explanatory machine learning technique SHapley Additive exPlanations (SHAP) and sensitivity analysis. Based on these findings, this investigation contributes to a reduction in the number of variables involved in cavitation experiments or simulations.