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 $Re$=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