Modeling and Simulation of Wake Safety Interval for Paired Approach Based on CFD

He, Xiao‐Gang; Ma, Yilong; Yang, Hong; Chen, Yaqing

doi:10.1155/2021/7891475

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…In 2021, Xin et al. used CFD to simulate the aircraft wake flow field during the approach phase and established a safety interval for paired approach based on simulation results 17 . The technology of using CFD method to study aircraft wake has been relatively mature, but no scholar has applied CFD technology to the study of PD wake spacing yet.…”

Section: Introductionmentioning

confidence: 99%

Research on wake interval of paired departure at closely spaced parallel runways

Gao

Liu²,

Chang

et al. 2023

Quality & Reliability Eng

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To assess and improve the operation system of paired takeoff separation and reduce the risk of wake turbulence encounters during paired takeoff, a computational fluid dynamics (CFD) method is used to study the operational conditions and wake turbulence safety intervals of paired takeoff separation mode. Firstly, the safe area of wake turbulence under the paired takeoff separation mode is analyzed, and a safety interval model is constructed. Secondly, the SST k-ω turbulence model is selected, and a numerical simulation method for the wake flow of the leading aircraft before the pairing is established based on CFD. Finally, a calculation method for the safety interval of paired takeoff separation is established.Taking Hongqiao Airport as an example, A320-200 and B737-800 are selected as the leading and trailing aircraft, and the wake flow field is simulated at 0, −1, and −2 m/s crosswind conditions. The simulation results are then used to analyze the operational conditions and safety intervals of paired takeoff separation mode. The results provide data support for the implementation of the paired takeoff separation mode and demonstrate the applicability of the CFD method in studying aircraft wake turbulence.

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Section: Introductionmentioning

confidence: 99%

Research on wake interval of paired departure at closely spaced parallel runways

Gao

Liu²,

Chang

et al. 2023

Quality & Reliability Eng

Self Cite

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“…The numerical simulation results demonstrated that the evolution of vortices upstream and downstream of the aircraft was asymmetric, with the downstream vortices decaying at a faster rate, and the wake vortex dissipation velocity increasing with higher crosswind velocities. He et al [20] established an aircraft wake vortex model for paired approach procedures, and conducted CFD numerical simulations to analyze the wake characteristics. Based on the simulation results, they proposed an optimization method for longitudinal safety separation of paired aircraft wake vortices.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks

He,

Zhao,

Gao

et al. 2023

Sustainability

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In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. The study focuses on the B737-800 aircraft, and employs CFD numerical simulations to obtain the evolutionary characteristics of wake vortex parameters under crosswind velocities ranging from 0 to 7 m/s. The wake vortex velocity and Q-criterion vorticity values are collected and partitioned into mutually exclusive training and testing datasets. A CNN model is constructed, and the training dataset is used to tune hyperparameters to minimize loss and achieve accurate predictions. After saving the trained model, the desired crosswind velocity value is input to obtain the predicted wake vortex velocity and Q-criterion vorticity values. The results indicate that the convolutional neural network model exhibits an average absolute percentage error of 1.5%, which is 2.3% lower than that of the fully connected neural network model. This suggests that convolutional neural networks can enhance the accuracy of wake vortex predictions, as demonstrated in this study. Compared to traditional CFD methods, the proposed model reduces the computation time by approximately 40 times, effectively improving computational efficiency and offering valuable insight for studies involving numerous numerical simulations, such as analyzing the safety separation between aircraft wake vortices during paired approach procedures.

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Assessing bus users satisfaction using discrete choice models: a case of Bhopal

Lodhi,

Jaiswal,

Sharma

2024

Innov. Infrastruct. Solut.

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Modeling and Simulation of Wake Safety Interval for Paired Approach Based on CFD

Cited by 8 publications

References 24 publications

Research on wake interval of paired departure at closely spaced parallel runways

Research on wake interval of paired departure at closely spaced parallel runways

Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks

Assessing bus users satisfaction using discrete choice models: a case of Bhopal

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