A perspective on the aerodynamics and aeroelasticity of tapering: Partial reattachment

Chen, Zengshun; Fu, Xianzhi; Xu, Yemeng; Li, Cruz Y.; Kim, Bubryur; Tse, Kam Tim

doi:10.1016/j.jweia.2021.104590

Cited by 46 publications

(26 citation statements)

References 33 publications

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“…Network. CNN, one of the most representative networks in the field of deep learning, was extensively used in civil structures, mechanical structures, and wind engineering [29,30]. It has three layers such as convolution layer, pooling layer, and full connection layer.…”

Section: Convolutional Neuralmentioning

confidence: 99%

Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

Chen

et al. 2021

Shock and Vibration

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In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

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Section: Convolutional Neuralmentioning

confidence: 99%

Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

Chen

et al. 2021

Shock and Vibration

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“…Several tall buildings are densely concentrated in modern cities, and they are near each other. The wind-induced response and aerodynamics, such as wind velocities and vectors around the buildings, wind pressures on surfaces, and wind loads, vary significantly among tall standalone structures [1][2][3][4][5][6][7]. Therefore, researchers have studied the wind velocities and vectors around numerous buildings using flow visualization or particle image velocimetry (PIV) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Characteristics and Lateral Displacements of a Set of Two Buildings in a Linked Tall Building System

Chen

Kim

Lee

2021

Sensors

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This study evaluates the aerodynamic characteristics and lateral displacements of two staggered buildings in a linked-building (LB) system. Particle image velocimetry and pressure measurements are employed, and the lateral displacement is evaluated using a 3-dimensional analytical model. When the gap distance between two non-linked buildings is small, the wind flows in a narrow jet, and a strong suction is generated on the inner surfaces of the two buildings, leading to a large cross-wind-induced response. However, the cross-wind-induced response is significantly reduced when a link is installed, because the suction forces generated from the buildings are in opposite directions and have a negative aerodynamic correlation. Conversely, with a large gap distance, the buildings at the front obstruct the wind blowing toward the rear buildings. Therefore, while the pressure distribution, wind-force coefficients, and wind-induced responses of the front and rear buildings show similar trends, the magnitude of impact on the front building is larger than that on the rear building. Installing a link is demonstrated to reduce the wind-induced response of the buildings in an LB system. However, the reduction in the along-wind-induced response is less than that in the cross-wind-induced response when the gap distance is small.

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“…e structural vibration data of large-scale water conservancy projects is the main carrier that reflects the structural vibration characteristics [1][2][3]. With the continuous upgrading and transformation of the level of monitoring automation, in the face of massive monitoring data, selecting effective vibration data analytical length and extracting data feature information with the help of efficient and reliable data processing methods is an important basis for realizing real-time safety monitoring of hydraulic structures.…”

Section: Introductionmentioning

confidence: 99%

The Method for Determining Optimal Analysis Length of Vibration Data Based on Improved Multiscale Permutation Entropy

Zhang

Yan

et al. 2021

Shock and Vibration

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Research on damage diagnosis or safety monitoring based on structural vibration response is one of the hot issues in the engineering field. The characteristic information of the structure is obtained by analyzing the structure response data. In the process of data analysis, the choice of data length is very important, which is related to the validity of the structure monitoring results. At present, the selection of data length is usually subjective, which reduces the rigor of the structure monitoring process. Therefore, a method based on improved multiscale permutation entropy (IMPE) is proposed to determine the optimal data analytical length (ODAL) of vibration data. This method creatively applies multiscale permutation entropy (MPE) to the field of data length analysis when processing nonlinear and nonstationary signals and optimizes MPE with the help of the improved coarse-grained method to obtain IMPE. IMPE is sensitive to different data lengths, and the entropy changes with the increase of the data length and tends to be stable. Here, the stable value is defined as a standard entropy. The entropy satisfying 97% of the standard entropy is used as the effective entropy, and the corresponding data length value of the effective entropy is selected as the ODAL of the vibration data. This method is suitable for many fields, provides a reliable data analytical length for data analysis, and has good engineering practicability.

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A perspective on the aerodynamics and aeroelasticity of tapering: Partial reattachment

Cited by 46 publications

References 33 publications

Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

Aerodynamic Characteristics and Lateral Displacements of a Set of Two Buildings in a Linked Tall Building System

The Method for Determining Optimal Analysis Length of Vibration Data Based on Improved Multiscale Permutation Entropy

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