Tip-Vortex Localization for Cross-Stream Position Control of a Multi-Hole Probe Relative to a Stationary Wing in a Free-Jet Wind Tunnel

Lauer, Nathan; Yeo, Derrick; Snyder, David A.; Paley, Derek A.

doi:10.2514/6.2017-1484

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…This study uses CFD numerical simulation data and constructs a PA-TLA model based on a hybrid parallel architecture for predicting the evolution of wake vortex characteristic parameters at different initial heights during the approach phase. The results are as follows: (1). Using PA-TLA to predict the circulation, Q criterion, vorticity of wake vortices at different initial heights outperforms both the LSTM and TCN in various predictive indicators.…”

Section: Discussionmentioning

confidence: 90%

“…According to the Kutta-Joukowsky theorem [1], the circulation of an aircraft's wake is related to various factors such as flight speed and wing shape. When an aircraft with a lift coefficient…”

Section: Extraction Of Aircraft Wake Evolution Characteristic Parametersmentioning

confidence: 99%

“…He and others proposed a CNN-based wake vortex prediction model, predicting aircraft wake vortex evolution under different side wind speeds [12]. Mohan and others used a hybrid neural network combining CNN and LSTM to extract coherent structures of turbulence [1].…”

Section: Introductionmentioning

confidence: 99%

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Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Deng,

Pan,

Wang

et al. 2024

Preprint

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To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model based on a parallel architecture based on the concept of intelligent aerodynamics, named PA-TLA (Parallel Architecture combining TCN, LSTM, and Attention mechanism). This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) by 6.80%, mean absolute error (MAE) by 7.70%, and root mean square error (RMSE) by 4.47%, with an average increase in the coefficient of determination (R²) by 0.36%, and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulation and the PA-TLA deep learning architecture to analyze the characteristics of wake evolution during the near-ground phase, providing theoretical value for further reducing aircraft wake intervals and enhancing airport operational efficiency.

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Section: Discussionmentioning

confidence: 90%

Section: Extraction Of Aircraft Wake Evolution Characteristic Parametersmentioning

confidence: 99%

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Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Deng,

Pan,

Wang

et al. 2024

Preprint

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show abstract

“…He and others proposed a CNN-based wake vortex prediction model, predicting aircraft wake vortex evolution under different side wind speeds [20]. Mohan and others used a hybrid neural network combining a CNN and LSTM to extract coherent structures of turbulence [21].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Deng,

Pan,

Wang

et al. 2024

Aerospace

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To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model named PA-TLA (parallel architecture combining a TCN, LSTM, and an attention mechanism) based on the concept of intelligent aerodynamics and a parallel architecture. This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) of 6.80%, in mean absolute error (MAE) of 7.70%, and in root mean square error (RMSE) of 4.47%, with an average increase in the coefficient of determination (R2) of 0.36% and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulations and the PA-TLA deep learning architecture to analyze the near-ground wake vortex evolution. The results indicate that the ground effect increases air resistance and turbulence as vortices approach the ground, thereby slowing the decay rate of the wake vortex strength at lower altitudes. The ground effect also accelerates the dissipation and movement of vortex centers, causing more pronounced changes in vortex spacing at lower altitudes. Additionally, the vortex center height at lower altitudes initially decreases and then increases, unlike the continuous decrease observed at higher altitudes.

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Review of Microscale Flow-Sensor-Enabled Mechanosensing in Small Unmanned Aerial Vehicles

Mark

Dickinson

2019

Journal of Aircraft

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Tip-Vortex Localization for Cross-Stream Position Control of a Multi-Hole Probe Relative to a Stationary Wing in a Free-Jet Wind Tunnel

Cited by 3 publications

References 13 publications

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

Review of Microscale Flow-Sensor-Enabled Mechanosensing in Small Unmanned Aerial Vehicles

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