Numerical model parameters optimization of ventilated cavitating flow around an axisymmetric body by ensemble Kalman filter data assimilation

Abstract

To improve the predictive accuracy of numerical simulations for ventilated cavitating flows, this study presents a data assimilation framework based on the Ensemble Kalman Filter (EnKF) to calibrate four key parameters in a large eddy simulation model for an axisymmetric body: the Smagorinsky constant, the number of smoothing iterations for interface curvature calculation, the characteristic velocity, and the characteristic length. Thirty parameter sets generated via Latin hypercube sampling are utilized to establish the initial flow field ensemble. Using time-series cavity lengths from water tunnel high-speed photography as observational data, an iterative EnKF algorithm derives the optimal parameter combination (CS = 0.0773, MM = 4, U = 0.7873U0, and L = 3.646D). Results demonstrate that the optimized model reduces the relative error of time-averaged cavity length from 7.8% (baseline) to 1.37% against experimental data. Furthermore, while activating subgrid dissipation and interfacial smoothing successfully filters out unphysical grid-scale numerical currents and regularizes macroscopic phase-boundary stability, the single-scalar length observation primarily constrains global geometric scales, leaving fine-scale transient shedding frequencies and localized gas leakage dynamics loosely constrained. Cross-condition validations across varying ventilation rates and freestream velocities confirm the strong multi-condition generalizability of the calibrated parameter matrix for macroscopic cavity predictions.