期刊
SUSTAINABILITY
卷 15, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/su151310219
关键词
turbulence; jet flow; numerical simulation; data assimilation; ensemble Kalman filter
This study uses 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 accurately predict the flow characteristics of subsonic jet exhaust. The recalibrated model constants effectively improve the prediction of jet flow characteristics, and 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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据