Shock-induced bubble collapse near solid materials: effect of acoustic impedance

Cao, Shunxiang; Wang, G.; Coutier-Delgosha, Olivier; Wang, K.

doi:10.1017/jfm.2020.810

Cited by 28 publications

(21 citation statements)

References 67 publications

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“…and Σ = , (11) at the -th iteration. This guarantees that the converged mean and covariance well approximate those of the posterior distribution (Eq.…”

Section: Unscented Kalman Inversionmentioning

confidence: 99%

“…Fluid-Structure Interaction (FSI) problems arise in many scientific and engineering applications including, to name only a few, aircraft aeroelasticity 1,2,3 , parachute inflation dynamics 4,5,6 , hemodynamics 7,8,9 , and lithotripsy 10,11 . Besides the development of mathematical models, seamless integration of observation data with these models starts to play a significant role to improve the prediction and quantify uncertainty for FSI, for example, calibration of hemodynamic model parameters to match patient data 12,13,9 and structural damage detection using sensor data and a digital twin 14,15,16 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework

Cao¹,

Huang

2021

Preprint

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Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex systems in the presence of both computer models and observation data. In the present work, we focus on large-scale fluid-structure interaction systems characterized by large structural deformations. Numerical methods to solve these problems, including embedded/immersed boundary methods, are typically not differentiable and lack smoothness. We propose a framework that is built on unscented Kalman filter/inversion to efficiently calibrate and provide uncertainty estimations of such complicated models with noisy observation data. The approach is derivative-free and non-intrusive, and is of particular value for the forward model that is computationally expensive and provided as a black box which is impractical to differentiate.The framework is demonstrated and validated by successfully calibrating the model parameters of a piston problem and identifying the damage field of an airfoil under transonic buffeting.

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“…and Σ = , (11) at the -th iteration. This guarantees that the converged mean and covariance well approximate those of the posterior distribution (Eq.…”

Section: Unscented Kalman Inversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework

Cao¹,

Huang

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Fluid-structure interaction (FSI) problems arise in many scientific and engineering applications including, to name only a few, aircraft aeroelasticity, [1][2][3] parachute inflation dynamics, [4][5][6] hemodynamics, [7][8][9] and lithotripsy. 10,11 Besides the development of mathematical models, seamless integration of observation data with these models starts to play a significant role to improve the prediction and quantify uncertainty for FSI, for example, calibration of hemodynamic model parameters to match patient data 9,12,13 and structural damage detection using sensor data and a digital twin. [14][15][16] The integration can be formulated as a data-based calibration problem.…”

Section: Introductionmentioning

confidence: 99%

Bayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary framework

Cao

Huang

2022

Numerical Meth Engineering

View full text Add to dashboard Cite

Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex systems in the presence of both computer models and observation data. In the present work, we focus on large-scale fluid-structure interaction systems characterized by large structural deformations. Numerical methods to solve these problems, including embedded/immersed boundary methods, are typically not differentiable and lack smoothness. We propose a framework that is built on unscented Kalman filter/inversion to efficiently calibrate and provide uncertainty estimations of such complicated models with noisy observation data. The approach is derivative-free and non-intrusive, and is of particular value for the forward model that is computationally expensive and provided as a black box which is impractical to differentiate. The framework is demonstrated and validated by successfully calibrating the model parameters of a piston problem and identifying the damage field of an aircraft wing under transonic buffeting.

show abstract

“…In this paper, we model the shock-induced collapse of surface nanobubbles using three-dimensional (3D) Molecular Dynamics (MD) simulations, capturing both the fluid and solid response, which would otherwise require complex multiphysics computation. 15,55,56 We also make comparisons to spherical nanobubble simulations, highlighting the differences in jet development, and resulting substrate damage.…”

Section: Introductionmentioning

confidence: 99%

Shock-induced collapse of surface nanobubbles

2021

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Shock-induced bubble collapse near solid materials: effect of acoustic impedance

Abstract: Abstract

Cited by 28 publications

References 67 publications

Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework

Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework

Bayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary framework

Shock-induced collapse of surface nanobubbles

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