Searched for: subject%3A%22neural%255C%252Bnetworks%22
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Kaniewski, Tadeusz (author)
The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...
master thesis 2023
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Bourier, Sébastien (author)
Evaluation of aircraft performance for design, certification, and maintenance purposes requires aerodynamic knowledge for the entire flight envelope of an aircraft. Simplified models that relate geometric properties and flight conditions of an aircraft to its aerodynamic properties are simply not sufficient anymore, as non-linear aerodynamic...
master thesis 2021
document
Pal, Somnath (author)
The total electrical energy consumption by all the operational data centers (located all over the world) is enormous (approx. 1% of the global electricity demand 20000TWh). This electrical energy is required 24x7 to operate and cool all the IT equipment present in the data center. The electrical energy required to cool all...
master thesis 2020
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Mulder, Boris (author)
Very complex flows can be expensive to compute using current CFD techniques. In this thesis, models based on deep learning were used to replace certain parts of the flow domain, with the objective of replacing well-known regions with simplified models to increase efficiency. To keep the error produced by the deep learning model bounded, a...
master thesis 2019
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Papp, David (author)
New generation combat aircraft are expected to operate over extended flight envelopes, including flight at high flow angles and rapid maneuvers. Conditions beyond traditional limits are giving rise to nonlinear phenomena, such as flow separation, large scale energetic vortices, fluctuations etc. These phenomena have significant impact on...
master thesis 2018
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