Related papers: Machine-Learning enabled analysis of ELM filament …
In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge transport barrier. These events are known as edge localized modes (ELMs). ELMs cause a loss of energy and damage the vessel walls. Understanding the physics of ELMs…
During a tokamak discharge, the plasma can vary between different confinement regimes: Low (L), High (H) and, in some cases, a temporary (intermediate state), called Dithering (D). In addition, while the plasma is in H mode, Edge Localized…
In H-mode tokamak and stellarator plasmas, edge localized modes (ELMs) lead to the expulsion of heat and particles beyond the edge transport barrier. ELMs cause a loss of energy and have the potential to damage the divertor and other plasma…
Ion cyclotron emission (ICE) is detected during edge localised modes (ELMs) in the KSTAR tokamak at harmonics of the proton cyclotron frequency in the outer plasma edge. The emission typically chirps downward (occasionally upward) during…
Magnetically confined plasmas are often subject to relaxation oscillations accompanied by large transport events. This is particularly the case for the high confinement regime of tokamaks where these events are termed edge localized modes…
The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on…
Fusion power production in tokamaks uses discharge configurations that risk producing strong Type I Edge Localized Modes. The largest of these modes will likely increase impurities in the plasma and potentially damage plasma facing…
During the operation of tokamak devices, addressing the thermal load issues caused by Edge Localized Modes (ELMs) eruption is crucial. Ideally, mitigation and suppression measures for ELMs should be promptly initiated as soon as the first…
A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from…
We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating and current drive locations in tokamak plasmas. TorbeamNN provides more than a 100 times speed-up…
Edge localised mode (ELM) measurements from reproducibly similar plasmas in the Joint European Torus (JET) tokamak, which differ only in their gas puffing rate, are analysed in terms of the pattern in the sequence of inter-ELM time…
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation…
Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by…
A new data cleaning procedure for electron cyclotron emission imaging (ECEI) of EAST tokamak is constructed. Machine learning techniques, including SVM and Decision tree, are applied to identifying saturated, zero, and weak signals of ECEI…
An analytic theory of edge localized mode (ELM) suppression in an H-mode tokamak plasma via the application of a static, externally generated, resonant magnetic perturbation (RMP) is presented. This theory is based on the plausible…
The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp…
Pedestal is the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by…
Electromagnetic (EM) instabilities and turbulence driven by the electron-temperature gradient are considered in a local slab model of a tokamak-like plasma. The model describes perturbations at scales both larger and smaller than the…
An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM. First, the importance of practical…
Edge-localized-mode (ELM) filaments are crucial for cross-field transport at the tokamak edge; yet, their dynamics are often analyzed using the cold-ion approximation, despite experimental data indicating that Ti~Te . This study employs a…