An Interpretable Aerodynamic Identification Model for Hypersonic Wind Tunnels

Li, Shichao; Sun, Yi; Gao, Hongli; Zhang, Xiaoqing; Lv, Jinzhou

doi:10.1109/tii.2023.3254644

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…Under the same sampling frequency and interval time, the minimum number of collected samples results in insufficient training of DNNs. Compared with the other six typical DNNs, 28 the RUL prediction curves of DD-cCycleGAN possess the best fit with the real-life curves, which more intuitively reflects the effectiveness of DD-cCycleGAN. This is because that DD-cCycleGAN is utilized to generate massive new samples, which enables LSTM networks to be trained more fully.…”

Section: Experimental Validationmentioning

confidence: 88%

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

Yu,

Zhao,

Guo

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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To accurately evaluate the remaining life (RUL) of rolling bearings under small sample conditions and strong noise interference, a RUL prediction scheme using adaptive variational mode decomposition (VMD) and double-discriminator conditional CycleGAN (DD-cCycleGAN) is put forward. Combining chimp optimization algorithm (ChOA) with VMD, an adaptive VMD algorithm based on ChOA is presented, which selects effective mode components for reconstruction and reduces interference from strong background noise. A DD-cCycleGAN is developed to generate new samples which not only retain sample information of source domain, but also resemble samples of target one. A LSTM network after training is utilized to predict the bearing RUL in test samples. The performance of this scheme was validated by using the XJTU-SY bearing test dataset. The comparison analyses demonstrate this scheme has strong noise resistance and high accuracy.

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Section: Experimental Validationmentioning

confidence: 88%

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

Yu,

Zhao,

Guo

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…In summary, deep learning methods can predict both fluid and acoustic characteristics. Southwest Jiaotong University [51,52]. In the design of hypersonic aircraft aerodynamic characteristics, the feasibility of CNN and transfer learning network models for identifying aerodynamic signals was separately validated through wind tunnel experiments.…”

Section: Deep Learning For Wind Noise Predictionmentioning

confidence: 99%

Deep learning-based wind noise prediction study for automotive clay model

Huang,

Wang,

Cao

et al. 2024

Meas. Sci. Technol.

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Analyzing and mitigating wind noise in automobiles under high-speed conditions is a significant issue within the realm of Noise, Vibration, and Harshness (NVH). Due to the intricate nature of aeroacoustics generation mechanisms, current conventional methods for wind noise prediction have limitations. Hence, deep learning methods are introduced to investigate wind noise in the side window area of an automotive clay model.During aeroacoustic wind tunnel experiments, side window vibration data and noise data from the driver were collected under vehicle speed conditions of 100 km/h, 120 km/h, and 140 km/h, respectively. These data samples were obtained to be used for training and validation of the wind noise model. Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Network (LSTM) algorithms were separately employed to reveal the complex nonlinear relationship between wind noise and its influencing factors, leading to the establishment of a wind noise prediction model.Simultaneously, these two deep learning methods were compared with Backpropagation Neural Networks (BPNN), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) methods. Our findings revealed that the LSTM wind noise prediction model not only exhibits higher accuracy but also demonstrates superior generalization capabilities, thereby substantiating the superiority of this method.

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“…The identification model is convenient to guide the design of controller. Note that compared with the related new intelligent data-driven modelling methods, the proposed leastsquares identification algorithm has the advantages of simplicity and low complexity for wind tunnel system with non-linear input [30,31]. The identified model not only provides a baseline model for MPC, but can also be further combined with intelligent control technology to improve the control performance of the system [32].…”

Section: Identification Testmentioning

confidence: 99%

Precision positioning control of wind tunnel centrosome

Liu,

Yuan,

Mao

2023

IET Control Theory & Appl

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The centrosome plays a crucial role in regulating the flow field of a continuous wind tunnel, and its positioning precision directly impacts the control accuracy of the Mach number. A high‐precision positioning control algorithm for the wind tunnel centrosome is proposed based on a model predictive control framework. Since that the centrosome is a typical non‐linear system with a Hammerstein structure comprising an input backlash and a linear dynamic block in series, a parameterized least‐squares identification method is constructed based on an adaptive forgetting factor strategy and a centrosome position prediction model with input backlash is established. To counteract the weakening effect of the backlash non‐linear block on the control signal, a compensator is constructed to compensate for the non‐linear input block. Considering that the input undetectable disturbance signal in the actuator may lead to unpredictable responses, an intermediate state observer is designed and the stable convergence of the observer algorithm is also proved. Finally, the closed‐loop stability of the proposed control algorithm is analyzed by constructing a Lyapunov function. Experimental testing verifies the superiority and applicability of the proposed algorithm.

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An Interpretable Aerodynamic Identification Model for Hypersonic Wind Tunnels

Cited by 11 publications

References 28 publications

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

Deep learning-based wind noise prediction study for automotive clay model

Precision positioning control of wind tunnel centrosome

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