4.7 Article

Estimating turbulence intensity along the glide path using wind tunnel experiments combined with interpretable tree-based machine learning algorithms

期刊

BUILDING AND ENVIRONMENT
卷 239, 期 -, 页码 -

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2023.110385

关键词

Turbulence intensity; Wind tunnel; Machine learning

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In this study, a scaled-down model of Hong Kong International Airport and its surrounding terrain were built in a wind tunnel to investigate wind turbulence intensity. Machine learning algorithms were used to predict turbulence intensity, and the results showed that terrain, distance from the runway, and wind direction significantly contributed to high turbulence intensity along the glide path. The most favorable conditions for high turbulence intensity were the presence of complex terrain, a shorter distance from the runway, and an inflow wind direction between 125 and 200 degrees.
Wind fluctuations near airport runways can make aircraft landings risky. Consequently, an aircraft may veer off its glide path, missed approach, or crash. In this study, a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain in its vicinity were built in a TJ-3 atmospheric boundary layer wind tunnel to investigate wind turbulence intensity. Cobra probes were placed along the glide slope of the models' runway to compute turbulence intensity at various locations under different inflow wind directions, with and without surrounding terrain. Next, advanced tree-based machine learning algorithms, including Random Forest, Extreme Gradient Boosting, Adaptive Boosting, and Light Gradient Boosting Machine optimized via Bayesian Optimization, were used to estimate turbulence intensity along the glide path. The Bayesian optimized-random forest model outperforms all other models in prediction performance, as measured by MAE (0.521), MSE (1.046), RMSE (1.024), and R2 (0.934). Furthermore, according to SHAP analysis, Terrain, Distance from Runway, and Wind Direction significantly contributed to high turbulence intensity along the glide path. The most optimistic predictions for high turbulence intensity were the presence of complex terrain, a shorter distance from the runway, and an inflow wind direction between 125 and 200 degrees.

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