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

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