Model order reduction for bifurcating phenomena in Fluid-Structure Interaction problems

Abstract

This work explores the development and the analysis of an efficient reduced order model for the study of a bifurcating phenomenon, known as the Coandă effect, in a multi-physics setting involving fluid and solid media. Taking into consideration a Fluid-Structure Interaction problem, we aim at generalizing previous works towards a more reliable description of the physics involved. In particular, we provide several insights on how the introduction of an elastic structure influences the bifurcating behaviour. We have addressed the computational burden by developing a reduced order branch-wise algorithm based on a monolithic Proper Orthogonal Decomposition. We compared different constitutive relations for the solid, and we observed that a nonlinear hyper-elastic law delays the bifurcation w.r.t. the standard model, while the same effect is even magnified when considering linear elastic solid.

References in corpus (4)

• An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
• A monolithic and a partitioned Reduced Basis Method for Fluid-Structure Interaction problems
• Model order reduction for bifurcating phenomena in Fluid-Structure Interaction problems
• Projection based semi--implicit partitioned Reduced Basis Method for non parametrized and parametrized Fluid--Structure Interaction problems

Cited by in corpus (5)

• An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
• Model order reduction for bifurcating phenomena in Fluid-Structure Interaction problems
• Geometrically Parametrised Reduced Order Models for the Study of Hysteresis of the Coanda Effect in Finite Element-based Incompressible Fluid Dynamics
• Sparse Identification for bifurcating phenomena in Computational Fluid Dynamics
• Model Reduction of a Flexible Nonsmooth Oscillator Recovers its Entire Bifurcation Structure