Reduced order models of elastic glass plate under low velocity impact

Janda, Tomáš; Schmidt, Jaroslav; Hála, Petr; Konrád, Petr; Zemanová, Alena; Sovják, Radoslav; Zeman, Jan; Šejnoha, Michal

doi:10.1016/j.compstruc.2020.106430

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…with r = 1.5 is suitable for contact between two isotropic structures. This is also named Hertz's contact law (HCL), which can frequently found in the related works to the impact on LG [42].…”

Section: Contact Model For Hard Body Impactmentioning

confidence: 96%

Nonlinear analytical study of structural laminated glass under hard body impact in the pre-crack stage

Huang

Wang

Yang

et al. 2021

Thin-Walled Structures

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Section: Contact Model For Hard Body Impactmentioning

confidence: 96%

Nonlinear analytical study of structural laminated glass under hard body impact in the pre-crack stage

Huang

Wang

Yang

et al. 2021

Thin-Walled Structures

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“…𝑊 ̅ 𝑖 (t) and 𝑊 ̅ (X, Y, t) represent the displacement of impactor and the deflection of the MLG panel, respectively. For the Hertz's contact law (HCL), which is frequently used in the impact problem [50], r is taken to be 1.5.…”

Section: Contact Model For Hard Body Impactmentioning

confidence: 99%

Experimental and analytical study on the pre-crack impact response of thick multi-layered laminated glass under hard body impact

Wang

Huang

Yang

et al. 2021

International Journal of Mechanical Sciences

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“…The dimension of the system model can be reduced through DL, and further system simulation and analysis can be realized by reconstructing high-dimensional system model features into low-dimensional system model features. At present, it is widely used in aerospace [4], material engineering [8]and structural engineering [9].Halder et al [15]used the recurrent neural network(RNN) to predict the force at the next moment according to the aileron motion of the wing-gust and aeroelastic interaction system at this time. Zhang et al [14]used deep neural network(DNN) to predict the force at the next moment according to the fluid parameters and boundary position of VIV system at this time.…”

Section: Introductionmentioning

confidence: 99%

Research and application of ROM based on Res-PINNs neural network in fluid system

Liu,

Hou,

Wei

et al. 2024

Preprint

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The reduced-order-model(ROM) method provides a strong support for the rapid iteration and simulation verification of supporting fluid system design. This study focuses on the problems of gradient disappearance or explosion and incomplete learning of flow field characteristics in the training process of PINN ROM. Based on PINN model, an innovative ROM Res-PINNs is proposed. By embedding ResNet module into PINN neural network structure, it strives to improve the training stability of the model while retaining physical knowledge. In addition, parallel network structure is added to the model to improve its perception and learning ability of flow field state.At last, in order to verify the validity of the proposed model, two classical fluid problems, the flow around a cylinder and Vortex-Induced Vibration(VIV), are selected to compare and verify the proposed Res-PINNs model. The results show that Res-PINNs can reconstruct the flow field state more accurately, effectively overcome the problems of gradient disappearance or explosion and poor learning ability of PINN model during training, and provide a new solution for the application of deep learning order reduction method in aerospace system modeling and simulation.

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Reduced order models of elastic glass plate under low velocity impact

Cited by 11 publications

References 31 publications

Nonlinear analytical study of structural laminated glass under hard body impact in the pre-crack stage

Nonlinear analytical study of structural laminated glass under hard body impact in the pre-crack stage

Experimental and analytical study on the pre-crack impact response of thick multi-layered laminated glass under hard body impact

Research and application of ROM based on Res-PINNs neural network in fluid system

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