Calibration of RNG k-ε Model Constants Based on Experimental Data Assimilation: A Study on the Flow Characteristics of Air-Lifted Plunger Interstitial Flow

This study optimized the constants of the RNG k-ε model using the Ensemble Kalman Filter (ENKF) data assimilation method to improve the accuracy of air-lift plunger gap flow predictions. For high Reynolds number turbulent flow, we conducted numerical simulations integrating experimental data with a library of predicted data generated via optimal Latin hypercube sampling. ENKF was employed to assimilate these data and adjust the model constants, significantly reducing prediction errors and enhancing the accuracy of plunger models. Specifically, mean square errors for rectangular and circular plungers decreased from 60.67 and 61.48 to 7.12 and 7.20, respectively. The study also revealed significant changes in vortex dynamics and flow distribution following data assimilation, providing insights for optimizing plunger design and improving system energy efficiency. These findings underscore the potential of data assimilation in advancing oil and gas production.