Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2602.21151

A coherent structure transport model for scrape-off layer turbulence

Zhichen Feng·James Myra·Junyi Cheng·Calder Haubrich·Yang Chen·Xinxing Ma·Darin R. Ernst·Scott Parker
View original source ↗Read source document ↗Export metadata ↓

Abstract

Understanding the locality of high-temperature plasma energy deposition on material surfaces in fusion reactors is critical for design. Here, we utilize the Gyrokinetic ElectroMagnetic turbulence including X-points (GEMX) simulation, together with SOLPS-ITER solutions for the background equilibrium electric field including drifts, to model the heat flux at the divertor plate and characterize the heat load width using realistic X-point geometry. We use a theory-based blobby transport model called the "Coherent Structure Transport" (CST) model to include the effect of plasma transport in the edge scrape-off layer. The CST model is extremely fast and can be used to quickly analyze any SOLPS-ITER solution. SOLPS-ITER provides the steady state, or equilibrium on which we superimpose blobby turbulence characterized by blob size, amplitude and frequency. We obtain the $1/B_p$ scaling of the heat load exponential decay width $\lambda_q$, in agreement with the Eich empirical scaling and with the Goldston heuristic theory. When including blobby turbulence in combination with the SOLPS-ITER electric field, we find a secondary peak in the heat flux radial profile, outwardly displaced from the strike point radius, with a relative amplitude that increases with the initial blob density. We describe the CST model in detail and provide initial investigations of the scaling of $\lambda_q$ and the secondary heat flux peak with blob size and amplitude.

Explore related subjects

physics.plasm-ph

Keep this discovery

BibTeXRIS

Zhichen Feng, James Myra, Junyi Cheng, Calder Haubrich, Yang Chen, Xinxing Ma, Darin R. Ernst, Scott Parker. 2026-02-24. A coherent structure transport model for scrape-off layer turbulence. https://arxiv.org/abs/2602.21151

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗
arXiv2026

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↗