Fluid–Structure Interaction Problems

Ohayon, Roger; Schotté, Jean-Sébastien

doi:10.1002/9781119176817.ecm2045

Cited by 5 publications

(8 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, in the field of engineering, complex PDE systems of field coupled nature, as in Fluid-Structure Interaction, are of great research value and they are widely used in the aerospace industry, nuclear engineering, etc. [71], so we will also study such complex PDEs in the future. (7) So far, there have been many promising applications of neural networks in computational engineering.…”

Section: Discussionmentioning

confidence: 99%

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

et al. 2020

View full text Add to dashboard Cite

The various studies of partial differential equations (PDEs) are hot topics of mathematical research. Among them, solving PDEs is a very important and difficult task and, since many partial differential equations do not have analytical solutions, numerical methods are widely used to solve PDEs. Although numerical methods have been widely used with good performance, researchers are still searching for new methods for solving partial differential equations. In recent years, deep learning has achieved great success in many fields, such as image classification and natural language processing. Studies have shown that deep neural networks have powerful function-fitting capabilities and have great potential in the study of partial differential equations. In this paper, we introduce an improved Physics Informed Neural Network (PINN) for solving partial differential equations. PINN takes the physical information that is contained in partial differential equations as a regularization term, which improves the performance of neural networks. In this study, we use the method to study the wave equation, the KdV–Burgers equation, and the KdV equation. The experimental results show that PINN is effective in solving partial differential equations and deserves further research.

show abstract

Section: Discussionmentioning

confidence: 99%

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

et al. 2020

View full text Add to dashboard Cite

show abstract

“…When dealing with problems of coupling between the structure and the fluid inside a cavity, the latter is typically discretized in space by using the finite element method (Refs. [8][9][10][11][12]). This approach responds to the need to effectively provide vibration modes and frequencies of fluid-structure coupled systems.…”

Section: Introductionmentioning

confidence: 99%

“…Applications with two-phase fluid with free surface can be found in Refs. [9][10][11][12]. In these works a completely Lagrangian formulation is provided in which the state is composed of displacements of the structural nodes, of the interface nodes and of the nodes of the fluid domain.…”

Section: Introductionmentioning

confidence: 99%

A linearized reduced-order model approach for sloshing to be used for aerospace design

Saltari

Traini

Gambioli

et al. 2021

Aerospace Science and Technology

View full text Add to dashboard Cite

“…Those, in turn, have a large influence on the fluid flow. A comprehensive overview of FSI and its challenges is given by the monographs of Ohayon (2004), Bazilevs et al (2013) and Bazilevs and Takizawa (2016). The classical focus in FSI problems is on solid structures.…”

Section: Introductionmentioning

confidence: 99%

A monolithic fluid–structure interaction formulation for solid and liquid membranes including free-surface contact

Sauer

Luginsland

2018

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

A unified fluid-structure interaction (FSI) formulation is presented for solid, liquid and mixed membranes. Nonlinear finite elements (FE) and the generalized-α scheme are used for the spatial and temporal discretization. The membrane discretization is based on curvilinear surface elements that can describe large deformations and rotations, and also provide a straightforward description for contact. The fluid is described by the incompressible Navier-Stokes equations, and its discretization is based on stabilized Petrov-Galerkin FE. The coupling between fluid and structure uses a conforming sharp interface discretization, and the resulting non-linear FE equations are solved monolithically within the Newton-Raphson scheme. An arbitrary Lagrangian-Eulerian formulation is used for the fluid in order to account for the mesh motion around the structure. The formulation is very general and admits diverse applications that include contact at free surfaces. This is demonstrated by two analytical and three numerical examples exhibiting strong coupling between fluid and structure. The examples include balloon inflation, droplet rolling and flapping flags. They span a Reynolds-number range from 0.001 to 2000. One of the examples considers the extension to rotation-free shells using isogeometric FE.

show abstract

Fluid–Structure Interaction Problems

Cited by 5 publications

References 55 publications

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

A linearized reduced-order model approach for sloshing to be used for aerospace design

A monolithic fluid–structure interaction formulation for solid and liquid membranes including free-surface contact

Contact Info

Product

Resources

About