Search for dissertations about: "gränsskikt"
Showing result 16 - 20 of 30 swedish dissertations containing the word gränsskikt.
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16. Experimental study on turbulent boundary-layer flows with wall transpiration
Abstract : Wall transpiration, in the form of wall-normal suction or blowing through a permeable wall, is a relatively simple and effective technique to control the behaviour of a boundary layer. For its potential applications for laminar-turbulent transition and separation delay (suction) or for turbulent drag reduction and thermal protection (blowing), wall transpiration has over the past decades been the topic of a significant amount of studies. READ MORE
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17. Boundary Conditions for Spectral Simulations of Atmospheric Boundary Layers
Abstract : An atmospheric boundary layer (ABL) is generally a very high Reynolds number boundary layer over a fully rough surface that is influenced by different external forces. Numerical simulations of ABLs are typically demanding, particularly due to the high Reynolds numbers. READ MORE
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18. Strategies for Molecular Engineering of Macroscopic Adhesion and Integrity Focusing on Cellulose Based Materials
Abstract : Many aspects of modern human life pose a strain on the delicate ecosystems around us. One example is marine litter – mainly various plastic items – which accumulate in the marine environment, where they cause problems for the fauna, such as ingestion and entanglement. READ MORE
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19. Adaptive and model-based control in laminar boundary-layer flows
Abstract : In boundary-layer flows it is possible to reduce the friction drag by breaking the path from laminar to turbulent state. In low turbulence environments, the laminar-to-turbulent transition is dominated by local flow instabilities – Tollmien-Schlichting (TS) waves – that exponentially grows while being con- vected by the flow and, eventually, lead to transition. READ MORE
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20. Time, space and control: deep-learning applications to turbulent flows
Abstract : In the present thesis, the application of deep learning and deep reinforcement learning to turbulent-flow simulations is investigated. Deep-learning models are trained to perform temporal and spatial predictions, while deep reinforcement learning is applied to a flow-control problem, namely the reduction of drag in an open channel flow. READ MORE