arXiv subjects

Jean Rabault

Publications and source records attributed to Jean Rabault.

66 records · Page 4Linked to original sources

arXiv2019

Accelerating Deep Reinforcement Learning strategies of Flow Control through a multi-environment approach

Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to discover complex Active Flow Control (AFC) strategies [Rabault, J., Kuchta, M., Jensen, A., Reglade, U., & Cerardi, N. (2019): "Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control", Journal of Fluid Mechanics, 865, 281-302]. However, while promising results were obtained on a simple 2D benchmark flow at a moderate Reynolds number, considerable speedups will be required to investigate more challenging flow configurations. In the case of DRL trained with Computational Fluid Dynamics (CFD) data, it was found that the CFD part, rather than training the Artificial Neural Network, was the limiting factor for speed of execution. Therefore, speedups should be obtained through a combination of two approaches. The first one, which is well documented in the literature, is to parallelize the numerical simulation itself. The second one is to adapt the DRL algorithm for parallelization. Here, a simple strategy is to use several independent simulations running in parallel to collect experiences faster. In the present work, we discuss this solution for parallelization. We illustrate that perfect speedups can be obtained up to the batch size of the DRL agent, and slightly suboptimal scaling still takes place for an even larger number of simulations. This is, therefore, an important step towards enabling the study of more sophisticated Fluid Mechanics problems through DRL.

physics.comp-ph↗

arXiv2019

An Open Source, Versatile, Affordable Waves in Ice Instrument for Scientific Measurements in the Polar Regions

Sea ice is a major feature of the polar environments. Recent changes in the climate and extent of the sea ice, together with increased economic activity and research interest in these regions, are driving factors for new measurements of sea ice dynamics. Waves in ice are important as they participate in the coupling between the open ocean and the ice-covered regions. Measurements are challenging to perform due to remoteness and harsh environmental conditions. While progress has been made in observing wave propagtion in sea ice using remote methods, these are still relatively new measurements and would benefit from more in situ data for validation. In this article, we present an open source instrument that was developed for performing such measurements. The versatile design includes an ultra-low power unit, a microcontroller-based logger, a small microcomputer for on-board data processing, and an Iridium modem for satellite communications. Virtually any sensor can be used with this design. In the present case, we use an Inertial Motion Unit to record wave motion. High quality results were obtained, which opens new possibilities for in situ measurements in the polar regions. Our instrument can be easily customized to fit many in situ measurement tasks, and we hope that our work will provide a framework for future developments of a variety of such open source instruments.

physics.ao-ph↗

arXiv2019

Wave-ice interaction in the North-West Barents Sea

The results of field work on drift ice during wave propagation are analyzed and presented. The field work was performed in the Barents Sea, and the main focus of the paper is on wave processes in the MIZ. A model of wave damping in broken ice is formulated and applied to interpret the field work results. It is confirmed that waves of higher frequencies are subjected to stronger damping when they propagate below the ice. This reduces the frequency of most energetic wave with increasing distance from the ice edge. Difference of wave spectra measured in two relatively close locations within the MIZ is discussed. The complicated geometry and dynamics of the MIZ in the North-West Barents Sea allow waves from the Atlantic Ocean and south regions of the Barents Sea to penetrate into different locations of the MIZ.

physics.ao-ph↗

arXiv2019

A two layer model for wave dissipation in sea ice

Sea ice is highly complex due to the inhomogeneity of the physical properties (e.g. temperature and salinity) as well as the permeability and mixture of water and a matrix of sea ice and/or sea ice crystals. Such complexity has proven itself to be difficult to parameterize in operational wave models. Instead, we assume that there exists a self-similarity scaling law which captures the first order properties. Using dimensional analysis, an equation for the kinematic viscosity is derived which is proportional to the wave frequency and the ice thickness squared. In addition, the model allows for a two-layer structure where the oscillating pressure gradient due to wave propagation only exists in a fraction of the total ice thickness. These two assumptions lead to a spatial dissipation rate that is a function of ice thickness and wavenumber. The derived dissipation rate compares favourably with available field and laboratory observations.

physics.ao-ph↗

arXiv2019

Wing fold angle determines the terminal descent velocity of double winged autorotating seeds, fruits and other diaspores

Wind dispersal of seeds is one essential mechanisms for plants to proliferate and to invade new territories. In this paper we present a methodology that combines 3D-printing, a minimal theoretical model and experiments to determine the shape of multi-winged autorotating seeds, fruits and other diaspores that provide them with the optimal wind dispersion potential i.e. minimal terminal descent velocity. Experiments are performed on 3D-printed synthetic fruits for a wide range of wing fold angles, base wing angles and wing loadings to determine how these affect the flight. Our experimental and theoretical model find an optimal wing fold angle that minimizes the descent velocity, which may be an important parameter that improves the fitness of these plants in an ecological strategy.

physics.flu-dyn↗

arXiv2019

Curving to fly: Synthetic adaptation unveils optimal flight performance of whirling fruits

Appendages of seeds, fruits and other diaspores (dispersal units) are essential for their wind dispersal, as they act as wings and enable them to fly. Whirling fruits generate an auto-gyrating motion from their sepals, a leaf like structure, which curve upwards and outwards, creating a lift force that counteracts gravitational force. The link of the fruit's sepal shape to flight performance, however, is as yet unknown. We develop a theoretical model and perform experiments for double-winged bio-mimetic 3D-printed fruits, where we assume that the plant has a limited amount of energy that it can convert into a mass to build sepals and, additionally, allow them to curve. Both hydrodynamic theory and experiments involving synthetic, double-winged fruits show that to produce a maximal flight time there is an optimal fold angle for the desiccated sepals. A similar sepal fold angle is found for a wide range of whirling fruits collected in the wild, highlighting that wing curvature can aid as an efficient mechanism for wind dispersal of seeds and may improve the fitness of their producers in the context of an ecological strategy.

physics.flu-dyn↗

arXiv2019

Laboratory investigations of the bending rheology of floating saline ice, and physical mechanisms of wave damping, in the HSVA ice tank

An experiment on the propagation of flexural-gravity waves was performed in the HSVA ice tank. Physical characteristics of the water-ice system were measured in different locations in the tank during the tests, with a number of sensors deployed in the water, on the ice and in the air. Water velocity was measured with an acoustic doppler velocimeter (ADV) and an acoustic doppler current profiler (ADCP); wave amplitudes were measured with ultrasonic sensors and the optical system Qualisys; in-plane deformations of the ice and the temperature of the ice and water were measured by fiber optic sensors, and acoustic emissions were recorded with compressional crystal sensors. All together 61 tests were performed, with ice thicknesses of 3 cm and 5 cm. The experimental setup and selected results of the tests are discussed in this paper. We show that cyclic motion of the ice along the tank, imitating ice drift, causes an increase in wave damping. We also show that the formation of non-through cracks in the ice, caused by the action of waves, increases wave damping.

physics.ao-ph↗

arXiv2018

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

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 simulation of the Karman vortex street at moderate Reynolds number (Re = 100), our Artificial Neural Network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the Artificial Neural Network successfully stabilizes the vortex alley and reduces drag by about 8%. This is performed while using small mass flow rates for the actuation, on the order of 0.5% of the mass flow rate intersecting the cylinder cross section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control.

physics.flu-dyn↗

arXiv2018

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

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, the models presented so far in the literature are limited in a number of regards, and more insights are required from either laboratory experiments or fieldwork for these models to be validated and improved. Unfortunately, performing detailed measurements of wave propagation in grease ice, either on the field or in the laboratory, is challenging. As a consequence, laboratory data are relatively scarce, and often consist of only a couple of wave elevation measurements along the length of the wave tank. We present combined measurements of wave elevation using an array of ultrasonic probes, and water kinematics using Particle Image Velocimetry (PIV), in a small-scale wave tank experiment. Experiments are performed over a wider frequency range than what has been previously investigated. The wave elevation measurements are used to compute the wave number and exponential damping coefficient. By contrast with a previous study in grease ice, we find that the wave number is consistent with the mass loading model, i.e. it increases compared with the open water case. Wave attenuation is compared with a series of one-layer models, and we show that they satisfactorily describe the viscous damping that is taking place. PIV data are also consistent with exponential wave amplitude attenuation, and a POD analysis reveals the existence of mean flows under the ice that are a consequence of the displacement and packing of the ice induced by the gradient in the wave-induced stress. Finally, we show that the dynamics of grease ice can generate eddy structures that inject eddy viscosity in the water under the grease ice, which would lead to enhanced mixing and participating in energy dissipation.

physics.flu-dyn↗

arXiv2018

An investigation into the interaction between waves and ice

This thesis is submitted in the partial fulfillment of the requirements for the degree of Doctor of Philosophy at the University of Oslo. It represents work that has been carried out between 2015 and 2018, under the supervision of Pr. Atle Jensen, Dr. Graig Sutherland, and Dr. Kai H. Christensen, in collaboration with Pr. Aleksey Marchenko and Pr. Brian Ward. The work presented was carried at the University of Oslo and the University Center in Svalbard. Financial support for the work was provided by the Norwegian Research Council under the Petromaks 2 scheme, through the project WOICE (Experiments on Waves in Oil and Ice), NFR Grant number 233901. The thesis consists of an introduction, and a selection of 7 publications. The introduction presents the scientific context in which the work was undertaken, the methodology used, the results obtained, as well as some personal thoughts about unsuccessful directions encountered during the project and possible future work. I certify that this dissertation is mine and that the results presented are the result of the work of our research group, to which I brought significant contribution.

physics.ao-ph↗

arXiv2018

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

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 systems. Active flow control, while of considerable interest from a theoretical point of view and for industrial applications, has remained inaccessible due to the difficulty in finding successful control strategies. Here we show that Deep Reinforcement Learning can achieve a stable active control of the Karman vortex street behind a two-dimensional cylinder. Our results show that Deep Reinforcement Learning can be used to design active flow controls and is a promising tool to study high dimensionality, non-linear, time dependent dynamic systems present in a wide range of scientific problems.

physics.flu-dyn↗

arXiv2015

The dynamics of a capsule in a wall-bounded oscillating shear flow

The motion of an initially spherical capsule in a wall-bounded oscillating shear flow is investigated via an accelerated boundary integral implementation. The neo-Hookean model is used as the constitutive law of the capsule membrane. The maximum wall-normal migration is observed when the oscillation period of the imposed shear is of the order of the relaxation time of the elastic membrane; hence, the optimal capillary number scales with the inverse of the oscillation frequency and the ratio agrees well with the theoretical prediction in the limit of high-frequency oscillation. The migration velocity decreases monotonically with the frequency of the applied shear and the capsule-wall distance. We report a significant correlation between the capsule lateral migration and the normal stress difference induced in the flow. The periodic variation of the capsule deformation is roughly in phase with that of the migration velocity and normal stress difference, with twice the frequency of the imposed shear. The maximum deformation increases linearly with the membrane elasticity before reaching a plateau at higher capillary numbers when the deformation is limited by the time over which shear is applied in the same direction and not by the membrane deformability. The maximum membrane deformation scales as the distance to the wall to the power 1/3 as observed for capsules and droplets in near-wall steady shear flows.

physics.flu-dyn↗