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Found 7 papers, 4 papers with code
Learning to Optimise Wind Farms with Graph Transformers
This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions.
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models
We also demonstrate that when utilising the Curl model, WakeNet is able to provide similar power gains to FLORIS, two orders of magnitude faster (e. g. 10 minutes vs 36 hours per optimisation case).
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms.
E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation
We demonstrate that this approach is able to obtain the same accuracy with a reduced computational cost, for adaptive mesh test cases related to flow around tidal turbines, which interact via their downstream wakes, and where the overall power output of the farm is taken as the QoI.
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
General optical flow methods are typically designed for rigid body motion, and thus struggle if applied to fluid motion estimation directly.
M2N: Mesh Movement Networks for PDE Solvers
However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.
Unsupervised Learning of Particle Image Velocimetry
Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem.
