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In order to relieve the stress caused by the surge of flight flow, Closely Spaced Parallel Runways (CSPRs) have been built in many hub airports, and a paired approach mode has been applied to CSPRs in some countries. This paper proposes a method for optimizing the wake separation between aircrafts which utilizes a paired approach, aiming at reducing longitudinal separation by using computational fluid dynamics technology. Firstly, the model of the wake vortex field of the paired lead aircraft is constructed. Secondly, the numerical simulation preparation for the characteristics of the wake vortex field is completed through the computational pretreatment of the model. Thirdly, a calculation model of wake safety interval based on paired approach operation is established. Finally, the proposed method shows its superiority comparing with other methods. This method realized visual analysis of wake vortex through optimization modeling based on computational fluid dynamics, contributing to increasing the capacity of the runway and improving the operation efficiency of an aerodrome.

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.

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