Zobrazeno 1 - 10
of 267
pro vyhledávání: '"Piggott, Matthew D."'
Autor:
Zhang, Mingrui, Wang, Chunyang, Kramer, Stephan, Wallwork, Joseph G., Li, Siyi, Liu, Jiancheng, Chen, Xiang, Piggott, Matthew D.
Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numeri
Externí odkaz:
http://arxiv.org/abs/2407.00382
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. The proposed model functions by enco
Externí odkaz:
http://arxiv.org/abs/2311.12750
Wind farm modelling has been an area of rapidly increasing interest with numerous analytical as well as computational-based approaches developed to extend the margins of wind farm efficiency and maximise power production. In this work, we present the
Externí odkaz:
http://arxiv.org/abs/2303.16274
Wind energy resource assessment typically requires numerical models, but such models are too computationally intensive to consider multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify a small number of re
Externí odkaz:
http://arxiv.org/abs/2302.05886
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a s
Externí odkaz:
http://arxiv.org/abs/2211.13649
Given a partial differential equation (PDE), goal-oriented error estimation allows us to understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and accumulate in a numerical approximation, for example using the finite elemen
Externí odkaz:
http://arxiv.org/abs/2207.11233
Extracting information on fluid motion directly from images is challenging. Fluid flow represents a complex dynamic system governed by the Navier-Stokes equations. General optical flow methods are typically designed for rigid body motion, and thus st
Externí odkaz:
http://arxiv.org/abs/2206.10480
Autor:
Clare, Mariana C A, Piggott, Matthew D
Neural networks are increasingly being used in a variety of settings to predict wind direction and speed, two of the most important factors for estimating the potential power output of a wind farm. However, these predictions are arguably of limited v
Externí odkaz:
http://arxiv.org/abs/2206.08953
Autor:
Song, Wenbin, Zhang, Mingrui, Wallwork, Joseph G., Gao, Junpeng, Tian, Zheng, Sun, Fanglei, Piggott, Matthew D., Chen, Junqing, Shi, Zuoqiang, Chen, Xiang, Wang, Jun
Mainstream numerical Partial Differential Equation (PDE) solvers require discretizing the physical domain using a mesh. Mesh movement methods aim to improve the accuracy of the numerical solution by increasing mesh resolution where the solution is no
Externí odkaz:
http://arxiv.org/abs/2204.11188
This research note documents the integration of the MPI-parallel metric-based mesh adaptation toolkit ParMmg into the solver library PETSc. This coupling brings robust, scalable anisotropic mesh adaptation to a wide community of PETSc users, as well
Externí odkaz:
http://arxiv.org/abs/2201.02806