83 research outputs found
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
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
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.Comment: Accepted to special issue on wave-ice interaction in Applied Ocean
Research. 15 pages, 7 figure
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
This paper focuses on the active flow control of a computational fluid
dynamics simulation over a range of Reynolds numbers using deep reinforcement
learning (DRL). More precisely, the proximal policy optimization (PPO) method
is used to control the mass flow rate of four synthetic jets symmetrically
located on the upper and lower sides of a cylinder immersed in a
two-dimensional flow domain. The learning environment supports four flow
configurations with Reynolds numbers 100, 200, 300 and 400, respectively. A new
smoothing interpolation function is proposed to help the PPO algorithm to learn
to set continuous actions, which is of great importance to effectively suppress
problematic jumps in lift and allow a better convergence for the training
process. It is shown that the DRL controller is able to significantly reduce
the lift and drag fluctuations and to actively reduce the drag by approximately
5.7%, 21.6%, 32.7%, and 38.7%, at =100, 200, 300, and 400 respectively.
More importantly, it can also effectively reduce drag for any previously unseen
value of the Reynolds number between 60 and 400. This highlights the
generalization ability of deep neural networks and is an important milestone to
active flow control
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
Turbulent kinetic energy dissipation from colliding ice floes
Increased knowledge about wave attenuation processes in sea ice, and hence
atmosphere-wave-ice-ocean energy transfer, is necessary to improve sea ice
dynamics models used for climate modeling and offshore applications. The aim of
this study is to generate such much needed data by investigating colliding ice
floes dynamics in a large-scale experiment and directly measuring and
quantifying the turbulent kinetic energy (TKE). The field work was carried out
at Van Mijen Fjord on Svalbard, where a 3x4 m ice floe was sawed out in the
fast ice. Wave motion was simulated by pulling the ice floe back and forth in
an oscillatory manner in a 4x6 m pool, using two electrical winches. Ice floe
motion was measured with a range meter and accelerometers, and the water
turbulence was measured acoustically with an acoustic Doppler current profiler
and optically with a remotely operated vehicle and bubbles as tracers. TKE
frequency spectra were found to contain an inertial subrange where energy was
cascading at a rate proportional to the -5/3 power law. The TKE dissipation
rate was found to decrease exponentially with depth. The total TKE dissipation
rate was estimated by assuming that turbulence was induced over an area
corresponding to the surface of the floe. The results suggest that
approximately 37% and 8% of the input power from the winches was dissipated in
turbulence and absorbed in the collisions, respectively, which experimentally
confirms that energy dissipation by induced turbulent water motion is an
important mechanism for colliding ice floe fields
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