Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session C01: Awards Session: Presentation of Awards and DFD Fellowships (Otto LaPorte Lecture, Stanley Corrsin Award)Invited
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Chair: Anne Juel, Univ of Manchester; Claudia Cenedese, Woods Hole Ocean Institution; Elisabeth Guazzelli, Université Paris Cité, CNRS, MSC Room: Ballroom ABC |
Sunday, November 19, 2023 10:30AM - 11:25AM |
C01.00001: Update from the DFD Ex. Committee, and Presentation of Awards and DFD Fellowships
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Sunday, November 19, 2023 11:25AM - 12:10PM |
C01.00002: Rheology of dense granular suspensions across flow regimes Invited Speaker: Elisabeth L Guazzelli Dense granular suspensions that consist of concentrated mixtures of non-Brownian particles suspended in a liquid are ubiquitous in many natural phenomena (e.g. landslides, debris flows, and sediment transport) and industrial processes (e.g. concrete and pastes). Their rheology is not fully understood, and establishing a unified theoretical framework across the different flow regimes is still challenging. The present work describes rheological measurements of granular suspensions in the dense regime undertaken at imposed volume fractions but also at imposed values of the particle normal stress. It addresses the transition from a Newtonian rheology in the Stokes limit to a Bagnoldian rheology when inertia is increased. It also examines the critical behavior near the jamming transition of suspensions of particles that can be rigid and rough but also soft and smooth. |
Sunday, November 19, 2023 12:10PM - 12:55PM |
C01.00003: Data-Driven Nonlinear Model Reduction for Fluids and Structures Invited Speaker: George Haller I discuss a recent dynamical-systems-based alternative to neural networks in the data-driven reduced-order modeling of nonlinear phenomena. Specifically, I show that spectral submanifolds (SSMs) provide very low-dimensional attractors in a broad family of physical problems ranging from structural vibrations to transitions in shear flows. A data-driven identification of the reduced dynamics on these SSMs gives a mathematically rigorous way to construct accurate and predictive reduced-order models without the use of governing equations. I illustrate SSM-based reduced modeling on several numerical and experimental data sets from fluid sloshing, hydrogel oscillations, transitions in plane Couette and pipe flows, and model-predictive control of soft robots. |
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