SearcharxivSearch

arXiv · 1411.6744

Investigation of toroidal acceleration and potential acceleration forces in EAST and J-TEXT plasmas

Abstract

In order to produce intrinsic rotation, bulk plasmas must be collectively accelerated by the net force exerted on them, which results from both driving and damping forces. So, to study the possible mechanisms of intrinsic rotation generation, it is only needed to understand characteristics of driving and damping terms because the toroidal driving and damping forces induce net acceleration which generates intrinsic rotation. Experiments were performed on EAST and J-TEXT for ohmic plasmas with net counter- and co-current toroidal acceleration generated by density ramping up and ramping down. Additionally on EAST, net co-current toroidal acceleration was also formed by LHCD or ICRF. For the current experimental results, toroidal acceleration was between - 50 km/s^2 in counter-current direction and 70 km/s^2 in co-current direction. According to toroidal momentum equation, toroidal electric field (E\-(\g(f))), electron-ion toroidal friction, and toroidal viscous force etc. may play roles in the evolution of toroidal rotation. To evaluate contribution of each term, we first analyze characteristics of E\-(\g(f)). E\-(\g(f)) is one of the co-current toroidal forces that acts on the plasma as a whole and persists for the entire discharge period. It was shown to drive the co-current toroidal acceleration at a magnitude of 10^3 km/s^2, which was much larger than the experimental toroidal acceleration observed on EAST and J-TEXT. So E\-(\g(f)) is one of co-current forces producing cocurrent intrinsic toroidal acceleration and rotation. Meanwhile, it indicates that there must be a strong counter-current toroidal acceleration resulting from counter-current toroidal forces. Electron-ion toroidal friction is one of the counter-current toroidal forces because global electrons move in the counter-current direction in order to produce a toroidal plasma current.

Explore related subjects

Keep this discovery

BibTeXRIS

Fudi Wang, Bo Lyu, Xiayun Pan, Zhifeng Cheng, Jun Chen, Guangming Cao, Yuming Wang, Xiang Han, Hao Li, Bin Wu, Zhongyong Chen, Manfred Bitter, Kenneth Hill, John Rice, Shigeru Morita, Yadong Li, Ge Zhuang, Minyou Ye, Baonian Wan, Yuejiang Shi, EAST team. 2014-11-25. Investigation of toroidal acceleration and potential acceleration forces in EAST and J-TEXT plasmas. https://doi.org/10.1585/pfr.10.3402069

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Particle-resolved pathways to energetic-ion formation in a fluctuating low-current hollow-cathode plume

Energetic-ion formation in a low-current hollow-cathode plume is investigated using experiments, self-consistent electrostatic particle-in-cell (PIC) simulation, and particle-resolved analysis. Retarding potential analyzer measurements show a substantial energetic-ion population over discharge currents of 0.8-3.5 A, while probe measurements reveal broadband plume fluctuations. Two-point phase-derived frequency-wavenumber measurements do not resolve a continuous ion-acoustic dispersion branch within the principal apparent-wavenumber interval. Because the inferred wavenumber is obtained from a cross-spectral phase defined modulo 2pi, the fluctuation diagnostics do not provide an unambiguous modal attribution for the energetic-ion population. A representative PIC plume, used as a qualitative kinetic reference, likewise develops broadband time-dependent electrostatic fluctuations together with a nonthermal energetic-ion population. Particle-resolved analysis shows that the energetic outflow is dominated by ions generated through ionization inside the plume, while source localization biases access to distinct trajectory and escape families. Matched field controls further show that time-averaged and frozen fields strongly suppress access to high-energy trajectories relative to the full time-dependent field over the analyzed interval. At the single-particle level, ion kinetic-energy gain is determined by electrostatic-field work accumulated along the actual trajectory, with different escape families exhibiting distinct radial and axial work contributions. These results establish a source-trajectory-field-work pathway for energetic-ion formation that can be identified without first assigning the fluctuating plume to a unique resolved plasma mode.

physics.plasm-ph

kobra: a new Vlasov code intended for plasma-wall modeling

In a fusion device plasma-wall interactions \edit{on the sheath scale} can be modeled as a collisionless problem. When modeling these regions particle-in-cell codes suffer from statistical error originating from undersampling the velocity space. On the other hand, Vlasov codes do not have this issue as they evolve the full distribution function. Here, we present a new finite-volume Vlasov code, kobra, equipped with adaptive-mesh refinement to reduce computational effort. Currently, the code solves the Vlasov-Poisson equations. We validate our code in 1d1v and 1d2v using established benchmarks, i.e. the two-stream instability, Landau damping, the Dory-Guest-Harris instability, and also a classical electrostatic plasma sheath. We find that the code reproduces the theoretical properties of these problems well. More importantly, the adaptive grid provides a computational gain that is likely to scale to higher dimensional, plasma-wall simulations.

physics.plasm-ph

Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices

In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $\kappa_\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $\kappa_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.

physics.plasm-ph