Applications

ExCALIBUR Project NEPTUNE: Nektar++ for fusion plasma simulationProject NEPTUNE (NEutrals and Plasma Turbulence Numerics for the Exascale; part of the UK's ExCALIBUR programme) aims to provide fusion plasma simulation capability compatible with the coming landscape of exascale-class hardware, focussing in particular on the `edge' region of the plasma where it comes into contact with the material of the tokamak. This represents an extremely challenging problem, particularly in view of the need to use kinetic theory where the plasma is not in local thermal equilibrium (though equilibrium fluids must be used where possible in order to manage computational expense). The plasma fluid component of NEPTUNE is expected to be a set of solvers built on Nektar++, treating the equations of motion for a charged, multicomponent, and strongly magnetized fluid, and leveraging the advantages of the spectral / hp method over existing finite-difference plasma codes. The non-equilibrium matter is treated using a particle simulation code developed in-house by the NEPTUNE team, which interfaces with, and in particular is able to leverage the meshing capabilities of, Nektar++. This talk will explain the rationale and structure of code developed under Project NEPTUNE and will outline current work toward coupled 3D plasma turbulence, neutral kinetics, and atomic reactions capability. Work toward true performance portability will be shown, with SYCL as the chosen DSL and including SYCL implementation of some existing Nektar++ code.The support of the UK Meteorological Office and Strategic Priorities Fund is acknowledged.Deep learning-based prediction of flow behind circular cylinders using solutions from Nektar++ Solver Much attention has been paid to deep learning and machine learning techniques to reduce the computational cost of computational fluid dynamics simulations. This work addresses the prediction of steady-state flows through many stationary cylinders using a deep-learning model and examines the accuracy of the predicted velocity fields. A deep learning model predicts the x and y components of the velocity field for a given cylinder configuration. The accuracy of the predicted velocity field is investigated with a focus on the velocity profile of the fluid flow and the fluid forces acting on the cylinder. This research focuses on the flow around a large number of cylinders and consists of the following two studies. The first of two studies in this study predicted a steady flow through a stationary cylinder, which is reported in this study. In this study, all cylinders are considered immobile and fixed in space. Fluid flow is guided by boundary conditions. In this research, we use a U-Net-like architecture to build a deep learning model. U-Net is typically applied to image segmentation problems. However, in this research, we apply U-Net to physical problems. Examine the accuracy of the predicted velocity field associated with the velocity profile of the fluid flow and the fluid forces acting on the cylinder. The current model accurately predicts flow when the number of cylinders is equal to or close to the number in the training data set. Extrapolating the predictions to a smaller number of cylinders introduces an error that can be interpreted as internal friction in the fluid. The fluid force results acting on cylinders suggest that the current deep learning model has good generalization performance for systems with a large number of cylinders. This study will help in designing the wind turbine arrangements for achieving the optimal power output for a given flow condition.