High-Efficiency Simulation of Dynamic Stability Derivatives Based on a Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) Coupling Aerodynamic Model

Cheng, Wenqi; Mi, Baigang

doi:10.3390/app132011511

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Cheng and Mi 24 integrated the PSO algorithm into LSTM to predict the aerodynamic performance of aircraft in unknown states according to the known simulated aerodynamic data and added the PSO algorithm to optimize the hyperparameters of the neural network, which improved the prediction accuracy, reduced the prediction error, and increased the efficiency by 70%. 24 However, few scholars have applied the optimization algorithm to precipitation prediction. In this paper, the optimization algorithm is applied to precipitation to improve the prediction speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

Hou,

Deng,

Xia

2024

AIP Advances

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Accurate precipitation prediction is very important for meteorological disaster prevention, water resources management, and agricultural decision making. To improve the accuracy of precipitation prediction, a hybrid model based on variational mode decomposition (VMD), crested porcupine optimization algorithm (CPO), and long short-term memory model (LSTM) is proposed in this paper. The model first uses VMD to decompose the precipitation time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of precipitation data. Then, the CPO algorithm is used to optimize LSTM adaptive parameters to improve the global search ability and robustness of the model. Finally, the decomposed precipitation component is input into the LSTM network to learn the spatiotemporal dependence relationship and improve the ability of long-term prediction. The experimental results show that compared with the traditional LSTM model, CPO-LSTM model, and VMD-LSTM model, the hybrid model achieves better performance in many evaluation indices and effectively improves the accuracy of precipitation prediction. The application of the model can provide an effective tool for the fields of meteorology and water resources management, as well as provide new ideas for related research.

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Section: Introductionmentioning

confidence: 99%

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

Hou,

Deng,

Xia

2024

AIP Advances

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“…The use of algorithms and computational capabilities has led to the widespread adoption of data-driven aerodynamic modeling methods based on neural networks, commonly used neural network methods in aerodynamic prediction include MLP, RBFNNs, CNNs, RNNs, and GANs [13][14][15]. These methods do not require the establishment of complex mathematical formulae based on physical mechanisms [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol

Wang,

Li,

2024

Applied Sciences

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A data-driven aerodynamic modeling method is proposed to address the problem that traditional modeling methods based on physical mechanisms cannot fully represent the special aerodynamic characteristics of tiltrotor evtol aircraft. By analyzing the uniquely complex aerodynamic characteristics of electric vertical take-off and landing (evtol) aircraft, an MLP neural network model has been constructed that reflects the coupling characteristics between influencing factors. Using the XV15 wind tunnel test data, a dataset was constructed, and the neural network model was trained and validated. Simulation results show that the selected data-driven method can accurately predict the aerodynamic characteristics of the longitudinal transition phase of the tiltrotor evtol.

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Adaptive PSO-SO algorithm with Sobol sequence for aerodynamic physical parameter identification of projectiles

Yang,

Wang,

2024

AIP Advances

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In the domain of aerodynamic physical parameter identification, conventional optimization algorithms are often limited by falling into local optima. To overcome this limitation, a novel adaptive PSO-SO algorithm based on Sobol sequences (SAPSO-SO) algorithm is proposed in this study. The algorithm integrates particle swarm optimization algorithms and snake optimization algorithms, utilizing Sobol sequences for initialization, which enhances the global search and local development ability of the algorithm by adaptively adjusting the inertia weights and learning factors. In addition, this study introduced a local optimal discriminant mechanism and a local search function to further enhance the optimization performance of the algorithms. In this study, the small interval constant method was used to subdivide the trajectory, relying on the three-degree-of-freedom ballistic model to identify the starting ballistic parameters and aerodynamic physical parameters of each small interval. The performances of the snake optimization algorithm, particle swarm optimization algorithm, C-K method, and SAPSO-SO algorithm in the identification of ballistic physical parameters were compared using the full ballistic simulation data of a high-speed rotating projectile as measurement data. The results show that the SAPSO-SO algorithm demonstrates excellent accuracy and effectiveness, especially in noisy simulation data, where its recognition accuracy is improved by 7.79% over the C-K method, highlighting its superior anti-noise performance and global optimization capability. It is comprehensively analyzed that the SAPSO-SO algorithm has strong global optimization potential in theory and shows a high degree of accuracy and stability in practical applications, independent of the selection of initial parameters.

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High-Efficiency Simulation of Dynamic Stability Derivatives Based on a Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) Coupling Aerodynamic Model

Cited by 3 publications

References 13 publications

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

Precipitation prediction based on variational mode decomposition combined with the crested porcupine optimization algorithm for long short-term memory model

A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol

Adaptive PSO-SO algorithm with Sobol sequence for aerodynamic physical parameter identification of projectiles

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