Study of Paired Approach Wake Separation Based on Crosswinds

Pan, Weijun; Jiang, Yanqiang; Zhou, Junjie; Ye, Wei; Zhang, Yuqin

doi:10.3390/aerospace11020146

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…The MAE provides a direct average m gin of error, as shown in Equation (30). The MSE highlights large errors, as seen in Eq tion (32). The RMSE presents the error in the same units as the predicted value, provid interpretability, as shown in Equation (31).…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…This paper references the value for r c /b 0 used by Holzäpfel in their research, which is 5.2%. The air density is assumed to be constant at 1.225 kg/m 3 , following the standard atmospheric pressure for ideal gases [31,32]. According to the Biot-Savart law, the induced velocity at the core when the wake vortex is fully rolled up (i.e., the characteristic velocity) w 0 is given by Equation ( 28):…”

Section: Attention-based Tensor Concatenation Modulementioning

confidence: 99%

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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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Section: Evaluation Criteriamentioning

confidence: 99%

Section: Attention-based Tensor Concatenation Modulementioning

confidence: 99%