4.8k citations · 20.2k across the 183 of their papers we have counts for
9 papers · 2 filters
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
Esther Lagemann, Steven L. Brunton, Christian Lagemann
Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology,…
Lagrangian Gradient Regression for the Detection of Coherent Structures from Sparse Trajectory Data
Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon
Lagrangian Coherent Structures (LCS) are flow features which are defined to objectively characterize complex fluid behavior over a finite time regardless of the orientation of the…
An experimental evaluation of the interplay between geometry and scale on cross-flow turbine performance
Aidan Hunt, Benjamin Strom, Gregory Talpey +5
Cross-flow turbines harness kinetic energy in wind or moving water. Due to their unsteady fluid dynamics, it can be difficult to predict the interplay between aspects of rotor geom…
Nonlinear parametric models of viscoelastic fluid flows
Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz +1
Reduced-order models have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics,…
Control of Vortex Dynamics using Invariants
Kartik Krishna, Aditya G. Nair, Anand Krishnan +2
Vortex-dominated flows are ubiquitous in engineering, and the ability to efficiently manipulate the dynamics of these vortices has broad applications, from wake shaping to mixing e…
Data-driven modeling of an oscillating surge wave energy converter using dynamic mode decomposition
Brittany Lydon, Brian Polagye, Steven Brunton
Modeling wave energy converters (WECs) to accurately predict their hydrodynamic behavior has been a challenge for the wave energy field. Often, this results in either low-fidelity,…
Data-Driven Modeling for Transonic Aeroelastic Analysis
Nicola Fonzi, Steven L. Brunton, Urban Fasel
Aeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we int…
The transformative potential of machine learning for experiments in fluid mechanics
Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon
The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the origin…
From resolvent to Gramians: extracting forcing and response modes for control
Benjamin Herrmann, Peter J. Baddoo, Scott T. M. Dawson +3
During the last decade, forcing and response modes produced by resolvent analysis have demonstrated great potential to guide sensor and actuator placement and design in flow contro…