Calibration of Turbulent Model Constants Based on Experimental Data Assimilation: Numerical Prediction of Subsonic Jet Flow Characteristics

Abstract

Experimental measurements and numerical simulations are two primary methods for studying turbulence. However, these methods often struggle to balance the accuracy and breadth of results. In order to accurately predict the flow characteristics of subsonic jet exhaust and provide a research foundation for the runway crossing operation after the takeoff point, this study utilizes the ensemble Kalman filter algorithm to recalibrate the SA turbulence model constants by integrating NASA’s experimental particle image velocimetry (PIV) data with a sample library generated using Latin hypercube sampling to obtain corresponding flow field calculations. The modified model constants effectively improve the prediction of jet flow characteristics, reducing the spatially averaged relative error along the horizontal axis behind the nozzle from 13.04% to 4.6%. This study focuses on enhancing the accuracy of numerical predictions for subsonic jet flows via the adjustment of turbulence model constants. The recalibrated model constants are then validated to improve the prediction of jet flows under various conditions. The findings have important implications for acquiring high-fidelity data on rear engine jet flows after takeoff, enabling precise determination of safety separation distances, and enhancing the operational efficiency of airports.