activity
20182022
most citedBringing optical fluid motion analysis to the field: a methodology using an open source ROV as camera system and rising bubbles as tracers

11 citations · 16 across the 7 of their papers we have counts for

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6 papers · 1 filter

physics.flu-dyn2022

Homogenized scattering model of water wave attenuation in marginal ice zone

Takahito Iida, Atle Jensen

A theoretical model to explain the scattering process of wave attenuation in a marginal ice zone is developed. Many field observations offer wave energy decay in the form of expone…

physics.flu-dyn20221 cited

A method to estimate the size of particles using the open source software OpenPTV

R. G. Ramirez de la Torre, Atle Jensen

A method to obtain particle sizes from images that are used for particle tracking velocimetry is proposed. This is an open source method, developed to use together with the open so…

physics.flu-dyn202011 cited

Bringing optical fluid motion analysis to the field: a methodology using an open source ROV as camera system and rising bubbles as tracers

Trygve K. Løken, Thea J. Ellevold, Reyna G. Ramirez de la Torre +2

Detailed water kinematics are important for understanding atmosphere-ice-ocean energy transfer processes in the Arctic. There are few in situ observations of 2D velocity fields in…

physics.flu-dyn2018

Experiments on wave propagation in grease ice: combined wave gauges and PIV measurements

Jean Rabault, Graig Sutherland, Atle Jensen +2

Water wave attenuation by grease ice is a key mechanism for the polar regions, as waves in ice influence many phenomena such as ice drift, ice breaking, and ice formation. However,…

physics.flu-dyn2018

Deep Reinforcement Learning achieves flow control of the 2D Karman Vortex Street

Jean Rabault, Ulysse Reglade, Nicolas Cerardi +2

The Karman Vortex Street has been investigated for over a century and offers a reference case for investigation of flow stability and control of high dimensionality, non-linear sys…

physics.flu-dyn2018

Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control

Jean Rabault, Miroslav Kuchta, Atle Jensen +2

We present the first application of an Artificial Neural Network trained through a Deep Reinforcement Learning agent to perform active flow control. It is shown that, in a 2D simul…