Pith. sign in

REVIEW 4 major objections 6 minor 152 references

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Time-dependent TRANSP simulations of 37 high-performing NSTX discharges show the Multi-Mode Model reproduces electron and ion temperature profiles more consistently than the Trapped Gyro-Landau Fluid model, at orders-of-magnitude lower comp

desk verdict Solid, honestly-scoped TRANSP benchmark: MMM beats TGLF in PT SOLVER on 37 NSTX shots and costs far less, but don't read the abstract as a model-vs-model verdict; the paper's own FUSE results and section VII provide the needed caveat. read the letter →

arxiv 2509.04359 v1 pith:EKXB46E3 submitted 2025-09-04 physics.plasm-ph

J.B. Lestz , G. Avdeeva , T.F. Neiser , M.V. Gorelenkova , F.D. Halpern , S.M. Kaye , J. McClenaghan , A.Y. Pankin
show 1 more author
K.E. Thome
This is my paper · ORCID
classification physics.plasm-ph PACS 52.55.Fa
keywords tokamaktransportsphericalNSTXreducedmodelsTGLFMulti-ModeModeltemperatureprofilepredictionelectromagneticturbulence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests whether two mature reduced turbulent transport models, MMM and TGLF, can predict electron and ion temperature profiles in high-performing spherical tokamak plasmas when run inside time-dependent TRANSP simulations. It claims that MMM agrees with NSTX observations more consistently than TGLF, with median profile errors around 28% for Te and 27% for Ti, while TGLF overpredicts Te by 46% and underpredicts Ti by 25%, with a strong and experimentally conflicting beta-dependent flattening of Ti. The paper also shows that TGLF's agreement improves substantially when run in a time-slice flux-matching solver with neural-net surrogates instead of TRANSP's time-dependent solver, indicating that the ranking is partly tied to the simulation method. The practical stakes are which reduced model can be trusted for scenario development and reactor design in the spherical tokamak regime, where TGLF is far more expensive than MMM.

What carries the argument

The central object is the comparison of two reduced turbulent transport models coupled to TRANSP's implicit time-dependent solver PT SOLVER. MMM combines submodels for ITG/TEM/KBM, ETG, microtearing modes, and drift-resistive inertial ballooning modes, while TGLF is a trapped gyro-Landau fluid model with quasilinear saturation rules; both include electromagnetic effects and E×B shear. The load-bearing comparison is quantified by root-mean-square profile errors, relative offset errors, peaking factors, and correlations with beta, and is complemented by a time-slice flux-matching solver with neural-net surrogates to separate model error from solver error.

What would settle it

Take the same set of NSTX discharges and run both MMM and electromagnetic TGLF in a time-slice flux-matching solver, as the paper did for TGLF surrogates, and compare median RMSEs; if TGLF's median errors drop below MMM's roughly 28/27% across the full database, the paper's claimed ordering is a property of PT SOLVER rather than of the turbulence models.

Watch

Extended reading notes

Core claim

For a large, well-analyzed set of high-performing NSTX discharges, predictive TRANSP simulations using MMM produce temperature profiles closer to experiment than electromagnetic TGLF with SAT0, and far closer than electrostatic TGLF with SAT1. Median RMSEs are 28% for Te and 27% for Ti with MMM, versus 46% and 25% with electromagnetic TGLF; electrostatic TGLF overpredicts Te by 93%. TGLF's predictions have a strong beta dependence: as beta rises, TGLF predicts lower Te and progressively flatter Ti profiles, in conflict with NSTX data, while MMM shows weaker and more consistent trends. Stored energy is predicted to about 18% median error by both MMM and electromagnetic TGLF. The paper additio

Load-bearing premise

The ranking rests on PT SOLVER's time-dependent setup, where only Te and Ti are evolved inside rho=0.7 while density, rotation, magnetic equilibrium, and fast-ion confinement are fixed to experimental or classical inputs; the paper itself shows TGLF's agreement improves markedly when a time-slice flux-matching solver replaces that setup, so the ranking may reflect solver choice as much as model quality.

Editorial extensions

If this is right

  • MMM becomes a practical reduced model for full-pulse, non-inductive scenario development on NSTX-U, with characteristic Te and Ti errors near 28% and stored energy within about 18%.
  • TGLF-based time-dependent TRANSP predictions for high-beta spherical tokamaks should be treated cautiously, since its beta-dependent Ti flattening and large Te error variance can produce opposite errors depending on beta.
  • Electrostatic TGLF is not suitable for NSTX-class plasmas; the factor-of-two Te overprediction shows electromagnetic fluctuations must be retained in reduced models at high beta.
  • Conclusions about turbulence-model quality are entangled with the solver choice: the same TGLF physics agrees much better in a time-slice flux-matching framework than in PT SOLVER.
  • TGLF's orders-of-magnitude higher CPU cost makes it impractical for full-pulse integrated modeling, unless fast surrogate models are embedded into a time-dependent transport solver.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I read MMM's advantage as partly a calibration effect: MMM's ETG submodel was previously fitted to NSTX data, so part of its 28/27% performance may encode device-specific knowledge that TGLF lacks.
  • The striking gap between TRANSP and flux-matching TGLF results suggests that validation studies should report solver-induced error separately, or they risk attributing a solver artifact to the physics model.
  • A like-for-like comparison could be made by training neural-net surrogates for MMM on the same NSTX time slices and running them in the same flux-matching solver used for TGLF, which would isolate the turbulence-model contribution to the ranking.
  • If TGLF's beta-dependent Ti flattening persists across improved saturation rules, it may point to a missing stabilization mechanism in high-beta spherical tokamaks that the current MMM submodel set captures.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper reports a large-scale validation of two reduced turbulent transport models, MMM and TGLF, in time-dependent predictive TRANSP simulations of 37 high-performing NSTX discharges. Only the electron and ion temperature profiles are evolved inside ρ=0.7; density, rotation, and the edge temperature are fixed to experimental fits, and fast-ion confinement is classical. The central quantitative result is that MMM overpredicts the median Te and Ti profiles by 28% and 27% RMSE, whereas electromagnetic TGLF SAT0 overpredicts Te by 46% with larger variance and underpredicts Ti by 25%, with a pronounced β-dependent Ti flattening. Electrostatic TGLF SAT1 is substantially worse. The authors also present TGLF neural-net surrogates evaluated in the FUSE time-slice flux matcher, which achieve much better agreement with experiment (Te 13–20%, Ti 9–16% RMSE) than the TRANSP TGLF runs, and they explicitly warn that TGLF should not be judged inadequate on the TRANSP results alone. The paper closes with a summary, a discussion comparing with DIII-D, MAST-U, KSTAR, and JET studies, and a list of suggested future work.

Significance. If the central claim holds, the paper provides a practically important benchmark: within the TRANSP/PT SOLVER environment, MMM is the more reliable reduced transport model for high-β NSTX-like plasmas, at orders-of-magnitude lower computational cost. The study uses a large, well-analyzed discharge database, clear profile-error metrics, aggregate statistics, and explicit treatment of model-setting sensitivity. The inclusion of TGLF surrogate models with a different flux-matching solver is a valuable honesty check that demonstrates solver dependence. The main risk is that the headline comparison conflates the transport model with the solver and the fixed-profile setup, and that experimental/profile-fit errors are not quantified. These issues are acknowledged in the text (Sec. II C and Sec. VII), but they are load-bearing for the abstract's general claim that MMM is 'more reliable' than TGLF. With additional controlled comparisons and uncertainty quantification, the paper would be a significant contribution to reduced-model validation for spherical tokamaks.

major comments (4)
  1. [Sec. II C and Table I] The paper states explicitly that 'measurement error in the underlying experimental data and possible systematic errors introduced when creating profile fits ... is not quantified in this work.' This is load-bearing because the headline comparison (MMM Te RMSE 28% vs EM TGLF 46%, Table I) is presented as a quantitative ranking. NSTX profile fits can have substantial systematic uncertainty, especially in the core and near the boundary. Without error bars on the experimental profiles, or at least a sensitivity analysis using perturbed fits, the reader cannot tell whether the reported differences are statistically meaningful. Please add an uncertainty estimate or temper the quantitative claims.
  2. [Secs. II A, V B, VII] The central claim that MMM 'more consistently agrees' with NSTX observations than TGLF is established only within the TRANSP/PT SOLVER setup, where density, rotation, and the ρ=0.7 boundary are fixed to experimental fits. The paper's own Sec. V B shows that TGLF surrogate models in the FUSE flux-matching solver yield Te RMSE 20% and Ti RMSE 16% (TGLF-NN SAT2), much lower than the TRANSP TGLF values 46% and 25%. This demonstrates that the model ranking is solver-dependent and does not reflect an intrinsic property of TGLF. The discussion in Sec. VII correctly warns against concluding TGLF is inadequate, but the abstract and title still make a general claim. A controlled comparison of MMM and TGLF within the same flux-matching solver, or a systematic study of the PT SOLVER iteration and boundary-condition effects, is needed to support the general statement. Without it, the headline should
  3. [Sec. I and database selection] MMM's ETG submodel is stated to have been calibrated against NSTX data (Refs. 70 and 73), and the validation database in this paper is also NSTX. No statement is made about the disjointness of calibration and validation sets, or about sensitivity of the MMM predictions to the calibrated constants. This creates a fairness concern when comparing MMM against TGLF on the same device. Additionally, the chosen TGLF settings (EM SAT0 and ES SAT1) are motivated by prior studies on MAST-U and NSTX, so the comparison may implicitly tune TGLF as well. Please provide evidence that the MMM-over-TGLF ranking is not dominated by this calibration overlap, or explicitly discuss the limitation.
  4. [Sec. IV, Table II] The computational-cost comparison is presented as a major advantage of MMM ('orders of magnitude lower cost'). However, Table II reports CPU hours per simulated second in PT SOLVER, which includes the number of Newton iterations and the convergence behavior of the coupled system, not the intrinsic cost of the turbulence models. The wall-clock factor of 64 for TGLF parallelism is also not included in the abstract. The authors do give per-iteration timings, which is helpful, but the cost-benefit conclusion should separate model cost from solver-iteration cost and should acknowledge that surrogate models (Sec. V B) erase much of the cost difference. Please clarify the scope of the cost claim.
minor comments (6)
  1. [Sec. I] Typographical errors: 'sytematically' and 'signficant' should be corrected.
  2. [Fig. 9 caption] 'TRANSP simulations simulations' is duplicated.
  3. [Sec. III C] The sentence about two outlier discharges with unphysically large energy confinement time is vague; please report which discharges and how the outliers were excluded from statistics.
  4. [Sec. IX] The data archive address is given as '(placeholder)'. This must be replaced with a working DOI or repository link before publication.
  5. [Sec. VII] Typo 'DIIII-D' should be 'DIII-D'.
  6. [Eq. (3)] The metric σ in Eq. (3c) is a normalized RMS error, not an RMS error in physical units. Calling it 'RMSE' in Table I is acceptable, but the axes in Fig. 12 and Fig. 3 should make the normalization explicit to avoid confusion with absolute temperature errors.

Circularity Check

2 steps flagged · score 6.0 of 10

Surrogate-model accuracy is in-sample, and MMM's ETG submodel was calibrated to NSTX data; the MMM-over-TGLF ranking is only partly independent.

  1. fitted input called prediction [Sec. I (Introduction), MMM paragraph; cf. Table III]
    "The ETG model was calibrated against NSTX data during its development in order to compensate for simplifying assumptions made in its derivation [70, 73]. Note that after that calibration was incorporated, it has remained unchanged when applying MMM to study different NSTX discharges and different devices."

    MMM's ETG submodel, which the paper later identifies as a dominant core electron-heat transport channel, had free parameters fitted to NSTX data in Refs. [70,73]. Several discharges in the validation database (e.g., 120968 and 138536 in Table III) are cited to those same development/calibration papers. The conclusion that MMM 'more consistently agree[s] with the NSTX observations' is therefore partly an in-sample check of a model component already adjusted to NSTX data, not a clean out-of-sample prediction. This does not by itself force the full MMM-versus-TGLF ranking, since the ranking also depends on other MMM submodels and the PT-SOLVER coupling, but it removes the clean independent-prediction interpretation of the reported 28% Te RMSE.

  2. fitted input called prediction [Sec. V B (Neural Net Optimization of TGLF Settings), Fig. 12]
    "A new set of TGLF surrogate models has now been trained on the same set of high performing NSTX discharges used for the predictive TRANSP simulations in this study. ... TGLF was run with different physics settings on approximately 1000 profiles taken from time slices in these discharges, both within and outside of the identified quiescent analysis windows ... the transport solver is run on time slices taken at every 20 ms in the identified analysis windows, corresponding to over 700 unique cases where the solver converged."

    The TGLF-NN and GKNN surrogates are trained on profiles from the same NSTX discharges, explicitly including the same analysis windows, and then 'evaluated' on time slices from those windows (Fig. 12). The quoted RMSE values (e.g., Te 13%, Ti 9% for GKNN SAT2) are therefore in-sample fits to the validation targets, not out-of-sample predictions. Using these numbers to say that surrogate-based flux matching 'find[s] better agreement with experiment than the TRANSP simulations' compares a fitted emulator against unfitted models; the improvement is forced by the training set rather than by the surrogate's predictive skill.

full rationale

The paper is primarily an empirical comparison of two reduced transport models inside the TRANSP/PT-SOLVER predictive framework, and that comparison itself is not circular: both models are run with the same solver, neoclassical model, fixed boundary at rho=0.7, and fixed density/rotation inputs, and the paper reports full error distributions. The central MMM-over-TGLF ranking has independent content in the sense that TGLF was not fitted to NSTX and could in principle have outperformed MMM. The ranking is, however, conditional on the solver and on the fixed experimental profiles, and the paper itself shows in Sec. V B that the same TGLF physics, when represented by neural-net surrogates in a different flux-matching solver, yields much lower RMSE; the paper explicitly cautions in Sec. VII against concluding that TGLF is inadequate. The strongest circularity is in that surrogate analysis: the TGLF-NN/GKNN 'predictions' are trained on the same NSTX discharges and time slices on which their accuracy is then reported, so the improvement is an artifact of in-sample fitting. A second, partial circularity is that MMM's ETG submodel was calibrated against NSTX data during development, with some validation discharges overlapping the calibration studies, so MMM's favorable NSTX agreement is not fully first-principles. These issues do not reduce the entire paper to a tautology, but they do compromise the clean 'prediction' interpretation of the headline accuracies, warranting a score of 6.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim is a benchmark comparison, so the paper's contribution is not a derivation from first principles. The load-bearing content is the choice of calibrated transport models, simulation configuration, and discharge database. The main circularity-relevant item is that MMM's ETG submodel was calibrated on NSTX data, while the validation set is also NSTX; the paper does not establish disjointness. No new entities are invented.

free parameters (2)
  • TGLF saturation rule constants (SAT0, SAT1, and variants)
    TGLF quasilinear fluxes depend on saturation-rule parameters fitted to databases of nonlinear gyrokinetic simulations (Sec. I). These constants are not re-fit in this paper, but they are part of the model being validated.
  • MMM ETG submodel calibration constants
    Sec. I: 'The ETG model was calibrated against NSTX data during its development.' The specific numeric constants are not given; this device-specific calibration is a fitted component of the model under test.
assumptions (5)
  • domain assumption Transport is local and quasilinear, and MMM represents total diffusivity as the sum of independent instability submodels.
    Sec. I describes MMM as combining four submodels and notes the assumption that total diffusivity is a sum of independently calculated diffusivities; this is a modeling premise, not tested here.
  • domain assumption Density and rotation are prescribed from experiment; only Te and Ti are predicted.
    Sec. II A: 'The density and rotation profiles were treated as inputs... instead of being predicted alongside the temperature profiles.' This shapes the turbulence and sources evaluated by the transport models.
  • domain assumption Fast-ion confinement is classical and no anomalous energetic-particle transport is included.
    Sec. II A: 'fast ion confinement is assumed to be classical.' Analysis windows are chosen MHD quiescent, but beam ions contribute 10-60% of stored energy.
  • domain assumption Experimental profile fits are treated as ground truth for error metrics, with unquantified uncertainty.
    Sec. II C explicitly states measurement and profile-fit systematic errors 'are not quantified in this work.' All RMSE comparisons assume these fits are unbiased.
  • domain assumption The selected discharges and quiescent windows are representative of high-performing NSTX regimes relevant to NSTX-U.
    Sec. II B selects previously analyzed, mostly high-performing discharges and windows 'free of impulsive variations'; extrapolation to NSTX-U scenarios assumes this set is representative.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas." pith.science (2026). https://pith.science/paper/EKXB46E3

@misc{pith2026250904359,
  author       = {Pith},
  title        = {Pith review of: Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EKXB46E3}},
  note         = {Machine review of arXiv:2509.04359}
}
abstract

Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those using the Trapped Gyro-Landau Fluid (TGLF) model, despite TGLF requiring orders of magnitude greater computational cost. When considering all examined discharges, MMM has median overpredictions of electron temperature ($T_e$) and ion temperature ($T_i$) profiles of 28% and 27%, respectively, relative to the experiment. TGLF overpredicts $T_e$ by 46%, with much larger variance than MMM, and underpredicts $T_i$ by 25%. As $\beta$ is increased across NSTX discharges, TGLF predicts lower $T_e$ and significant flattening of the $T_i$ profile, conflicting with NSTX observations. When using an electrostatic version of TGLF, both $T_e$ and $T_i$ are substantially overpredicted, underscoring the importance of electromagnetic turbulence in the high $\beta$ spherical tokamak regime. Additionally, calculations with neural net surrogate models for TGLF were performed outside of TRANSP with a time slice flux matching transport solver, finding better agreement with experiment than the TRANSP simulations, highlighting the impact of different transport solvers and simulation techniques. Altogether, the reasonable agreement with experiment of temperature profiles predicted by MMM motivates a more detailed examination of the sensitivities of the TRANSP simulations with MMM to different NSTX plasma regimes in a companion paper, in preparation for self-consistent, time-dependent predictive modeling of NSTX-U scenarios.

Figures

Figures reproduced from arXiv: 2509.04359 by the authors.

Figure 1
Figure 1. FIG. 1. Collection of well-analyzed NSTX discharges mod [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Electron temperature profiles averaged over all examined NSTX discharges for (a) relatively low [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. (a) Histogram of relative error (RMSE over [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Ion temperature profiles averaged over all examined NSTX discharges for (a) relatively low [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. (a) Histogram of relative error (RMSE over [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. (a) On-axis [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Comparison of experimentally measured and pre [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Dependence of relative errors in electromagnetic [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. (a) Electron heat diffusivity profile predicted by [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: FIG. 10. Comparison of computational cost of different trans [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: FIG. 11. Electron and ion temperature profiles predicted by TGLF for discharge 133959 (top row) and 133964 (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Calculations of kinetic plasma profiles and thermal stored energy with neural net surrogate models based on elec [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: FIG. 13. Ratio of turbulent ion heat flux, calculated by [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

152 extracted references · 78 canonical work pages

  1. [1]

    J. B. Lestz, G. Avdeeva, S. M. Kaye, M. V. Gorelenkova, F. D. Halpern, J. McClenaghan, A. Y. Pankin, and K. E. Thome, Sensitivities of time-dependent tempera- ture profile predictions for NSTX with the Multi-Mode Model, Plasma Physics and Controlled Fusion (submit- ted)

  2. [2]

    M. Ono, S. Kaye, Y.-K. Peng, G. Barnes, W. Blan- chard, M. Carter, J. Chrzanowski, L. Dudek, R. Ewig, D. Gates, R. Hatcher, T. Jarboe, S. Jardin, D. Johnson, R. Kaita, M. Kalish, C. Kessel, H. Kugel, R. Maingi, R. Majeski, J. Manickam, B. McCormack, J. Menard, D. Mueller, B. Nelson, B. Nelson, C. Neumeyer, G. Oliaro, F. Paoletti, R. Parsells, E. Perry, N....

    2000

  3. [3]

    S. A. Sabbagh, J.-W. Ahn, J. Allain, R. Andre, A. Bal- baky, R. Bastasz, D. Battaglia, M. Bell, R. Bell, P. Beiersdorfer, E. Belova, J. Berkery, R. Betti, J. Bialek, T. Bigelow, M. Bitter, J. Boedo, P. Bonoli, A. Boozer, A. Bortolon, D. Boyle, D. Brennan, J. Bres- lau, R. Buttery, J. Canik, G. Caravelli, C. Chang, N. Crocker, D. Darrow, B. Davis, L. Delga...

    2013

  4. [4]

    J. E. Menard, S. Gerhardt, M. Bell, J. Bialek, A. Brooks, J. Canik, J. Chrzanowski, M. Denault, L. Dudek, D. A. Gates, N. Gorelenkov, W. Gut- tenfelder, R. Hatcher, J. Hosea, R. Kaita, S. Kaye, C. Kessel, E. Kolemen, H. Kugel, R. Maingi, M. Mar- denfeld, D. Mueller, B. Nelson, C. Neumeyer, M. Ono, E. Perry, R. Ramakrishnan, R. Raman, Y. Ren, S. Sabbagh, M...

    2012

  5. [5]

    J. E. Menard, J. P. Allain, D. J. Battaglia, F. Bedoya, R. E. Bell, E. Belova, J. W. Berkery, M. D. Boyer, N. Crocker, A. Diallo, F. Ebrahimi, N. Ferraro, E. Fredrickson, H. Frerichs, S. Gerhardt, N. Gore- lenkov, W. Guttenfelder, W. Heidbrink, R. Kaita, S. M. Kaye, D. M. Kriete, S. Kubota, B. P. LeBlanc, D. Liu, R. Lunsford, D. Mueller, C. E. Myers, M. O...

    2017

  6. [6]

    Berkery, P

    J. Berkery, P. Adebayo-Ige, H. A. Khawaldeh, G. Avdeeva, S.-G. Baek, S. Banerjee, K. Barada, D. Battaglia, R. Bell, E. Belli, E. Belova, N. Bertelli, N. Bisai, P. Bonoli, M. Boyer, J. Butt, J. Candy, C. Chang, C. Clauser, L. C. Rivera, M. Curie, P. de Vries, R. Diab, A. Diallo, J. Dominski, V. Duarte, E. Emdee, N. Ferraro, R. Fitzpatrick, E. Foley, E. Fre...

    2024

  7. [7]

    S. M. Kaye, J. W. Connor, and C. M. Roach, Thermal confinement and transport in spherical tokamaks: a re- view, Plasma Physics and Controlled Fusion 63, 123001 (2021)

    2021

  8. [8]

    Ono and R

    M. Ono and R. Kaita, Recent progress on spherical torus research, Physics of Plasmas 22, 040501 (2015)

    2015

Show all 152 references
  1. [9]

    S. M. Kaye, F. M. Levinton, D. Stutman, K. Tritz, H. Yuh, M. G. Bell, R. E. Bell, C. W. Domier, D. Gates, W. Horton, J. Kim, B. P. LeBlanc, N. C. Luhmann, R. Maingi, E. Mazzucato, J. E. Menard, D. Mikkelsen, D. Mueller, H. Park, G. Rewoldt, S. A. Sabbagh, D. R. Smith, and W. W...

    2007

  2. [10]

    S. Kaye, S. Gerhardt, W. Guttenfelder, R. Maingi, R. Bell, A. Diallo, B. LeBlanc, and M. Podesta, The de- pendence of H-mode energy confinement and transport on collisionality in NSTX, Nuclear Fusion 53, 063005 (2013)

    2013

  3. [11]

    Valoviˇ c, R

    M. Valoviˇ c, R. Akers, G. Cunningham, L. Garzotti, B. Lloyd, D. Muir, A. Patel, D. Taylor, M. Turnyanskiy, M. Walsh, and the MAST team, Scaling of H-mode en- ergy confinement with Ip and BT in the MAST spherical tokamak, Nuclear Fusion 49, 075016 (2009)

    2009

  4. [12]

    Valoviˇ c, R

    M. Valoviˇ c, R. Akers, M. de Bock, J. McCone, L. Gar- zotti, C. Michael, G. Naylor, A. Patel, C. M. Roach, R. Scannell, M. Turnyanskiy, M. Wisse, W. Gutten- felder, J. Candy, and the MAST team, Collisionality and safety factor scalings of H-mode energy transport in the MAST s...

    2011

  5. [13]

    Kurskiev, N

    G. Kurskiev, N. Bakharev, V. Bulanin, F. Cherny- shev, V. Gusev, N. Khromov, E. Kiselev, V. Minaev, I. Miroshnikov, E. Mukhin, M. Patrov, A. Petrov, Y. Petrov, N. Sakharov, P. Shchegolev, A. Slad- komedova, V. Solokha, A. Telnova, S. Tolstyakov, V. Tokarev, and A. Yashin, Ther...

    2019

  6. [14]

    Kurskiev, V

    G. Kurskiev, V. Gusev, N. Sakharov, Y. Petrov, N. Bakharev, I. Balachenkov, A. Bazhenov, F. Cherny- shev, N. Khromov, E. Kiselev, S. Krikunov, V. Mi- naev, I. Miroshnikov, A. Novokhatskii, N. Zhiltsov, E. Mukhin, M. Patrov, K. Shulyatiev, P. Shchegolev, O. Skrekel, A. Telnova,...

    2021

  7. [15]

    Rewoldt, W

    G. Rewoldt, W. M. Tang, S. Kaye, and J. Menard, Mi- croinstability properties of small-aspect-ratio tokamaks, Physics of Plasmas 3, 1667 (1996)

    1996

  8. [16]

    J. E. Kinsey, R. E. Waltz, and J. Candy, The effect of plasma shaping on turbulent transport and E ×B shear quenching in nonlinear gyrokinetic simulations, Physics of Plasmas 14, 102306 (2007)

    2007

  9. [17]

    C. M. Roach, I. G. Abel, R. J. Akers, W. Arter, M. Barnes, Y. Camenen, F. J. Casson, G. Colyer, J. W. Connor, S. C. Cowley, D. Dickinson, W. Dor- land, A. R. Field, W. Guttenfelder, G. W. Hammett, R. J. Hastie, E. Highcock, N. F. Loureiro, A. G. Peeters, M. Reshko, S. Saarelma...

    2009

  10. [18]

    Guttenfelder, J

    W. Guttenfelder, J. Candy, S. M. Kaye, W. M. Nevins, R. E. Bell, G. W. Hammett, B. P. LeBlanc, and H. Yuh, Scaling of linear microtearing stability for a high col- lisionality National Spherical Torus Experiment dis- charge, Physics of Plasmas 19, 022506 (2012)

    2012

  11. [19]

    Guttenfelder, J

    W. Guttenfelder, J. L. Peterson, J. Candy, S. M. Kaye, Y. Ren, R. E. Bell, G. W. Hammett, B. P. LeBlanc, D. R. Mikkelsen, W. M. Nevins, and H. Yuh, Progress in simulating turbulent electron thermal transport in NSTX, Nuclear Fusion 53, 093022 (2013)

    2013

  12. [20]

    S. M. Kaye, W. Guttenfelder, R. E. Bell, S. P. Gerhardt, B. P. LeBlanc, and R. Maingi, Reduced model predic- tion of electron temperature profiles in microtearing- dominated National Spherical Torus eXperiment plas- mas, Physics of Plasmas 21, 082510 (2014)

    2014

  13. [21]

    C. F. Clauser, W. Guttenfelder, T. Rafiq, and E. Schus- ter, Linear ion-scale microstability analysis of high and low-collisionality NSTX discharges and NSTX-U pro- jections, Physics of Plasmas 29, 102303 (2022)

    2022

  14. [22]

    Patel, D

    B. Patel, D. Dickinson, C. Roach, and H. Wilson, Linear gyrokinetic stability of a high β non-inductive spherical tokamak, Nuclear Fusion 62, 016009 (2021). 26

    2021

  15. [23]

    McClenaghan, T

    J. McClenaghan, T. Slendebroek, G. M. Staebler, S. P. Smith, O. M. Meneghini, B. A. Grierson, K. E. Thome, G. Avdeeva, L. L. Lao, J. Candy, and W. Guttenfelder, Transition from ITG to MTM linear instabilities near pedestals of high density plasmas, Physics of Plasmas 30, 042512 (2023)

    2023

  16. [24]

    Kennedy, M

    D. Kennedy, M. Giacomin, F. Casson, D. Dickinson, W. Hornsby, B. Patel, and C. Roach, Electromagnetic gyrokinetic instabilities in STEP, Nuclear Fusion 63, 126061 (2023)

    2023

  17. [25]

    Kennedy, C

    D. Kennedy, C. Roach, M. Giacomin, P. Ivanov, T. Ad- kins, F. Sheffield, T. G¨ orler, A. Bokshi, D. Dickinson, H. Dudding, and B. Patel, On the importance of parallel magnetic-field fluctuations for electromagnetic instabil- ities in STEP, Nuclear Fusion 64, 086049 (2024)

    2024

  18. [26]

    Giacomin, D

    M. Giacomin, D. Kennedy, F. J. Casson, A. C J, D. Dickinson, B. S. Patel, and C. M. Roach, On elec- tromagnetic turbulence and transport in STEP, Plasma Physics and Controlled Fusion 66, 055010 (2024)

    2024

  19. [27]

    Dominski, W

    J. Dominski, W. Guttenfelder, D. Hatch, T. Goerler, F. Jenko, S. Munaretto, and S. Kaye, Global micro- tearing modes in the wide pedestal of an NSTX plasma, Physics of Plasmas 31, 044501 (2024)

    2024

  20. [28]

    McClenaghan, T

    J. McClenaghan, T. F. Neiser, F. D. Halpern, K. E. Thome, A. D. Turnbull, E. W. DeShazer, O. M. Meneghini, G. Avdeeva, J. B. Lestz, and J. Candy, Role of perturbed parallel magnetic field effects in predicting turbulent transport in NSTX, Plasma Physics and Con- trolled Fusion...

    2025

  21. [29]

    Singh, T

    T. Singh, T. Rafiq, E. Schuster, Z. Lin, and A. Ku- ley, Global gyrokinetic simulations of kinetic ballooning mode in NSTX-U plasmas, Nuclear Fusion (submitted)

  22. [30]

    J. E. Menard, L. Bromberg, T. Brown, T. Burgess, D. Dix, L. El-Guebaly, T. Gerrity, R. J. Goldston, R. J. Hawryluk, R. Kastner, C. Kessel, S. Malang, J. Minervini, G. H. Neilson, C. L. Neumeyer, S. Prager, M. Sawan, J. Sheffield, A. Sternlieb, L. Waganer, D. Whyte, and M. Zarn...

    2011

  23. [31]

    J. E. Menard, T. Brown, L. El-Guebaly, M. Boyer, J. Canik, B. Colling, R. Raman, Z. Wang, Y. Zhai, P. Buxton, B. Covele, C. D’Angelo, A. Davis, S. Ger- hardt, M. Gryaznevich, M. Harb, T. C. Hender, S. Kaye, D. Kingham, M. Kotschenreuther, S. Maha- jan, R. Maingi, E. Marriott, ...

    2016

  24. [32]

    J. E. Menard, Compact steady-state tokamak perfor- mance dependence on magnet and core physics limits, Philosophical Transactions of the Royal Society A 377, 20170440 (2019)

    2019

  25. [33]

    Menard, B

    J. Menard, B. Grierson, T. Brown, C. Rana, Y. Zhai, F. Poli, R. Maingi, W. Guttenfelder, and P. Snyder, Fu- sion pilot plant performance and the role of a sustained high power density tokamak, Nuclear Fusion62, 036026 (2022)

    2022

  26. [34]

    J. E. Menard, Next-step low-aspect-ratio tokamak de- sign studies, in 29th IAEA Fusion Energy Conference (London, United Kingdom, 2023)

    2023

  27. [35]

    Wilson, I

    H. Wilson, I. Chapman, T. Denton, W. Morris, B. Patel, G. Voss, C. Waldon, and the STEP Team, STEP—on the pathway to fusion commercialization, in Commer- cialising Fusion Energy , 2053-2563 (IOP Publishing,

  28. [36]

    Waldon, S

    C. Waldon, S. I. Muldrew, J. Keep, R. Verhoeven, T. Thompson, and M. Kisbey-Ascott, Concept de- sign overview: a question of choices and compromise, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 382, 20230414 (2024)

    2024

  29. [37]

    Meyer, Plasma burn—mind the gap, Philosophi- cal Transactions of the Royal Society A: Mathemati- cal, Physical and Engineering Sciences 382, 20230406 (2024)

    H. Meyer, Plasma burn—mind the gap, Philosophi- cal Transactions of the Royal Society A: Mathemati- cal, Physical and Engineering Sciences 382, 20230406 (2024)

    2024

  30. [38]

    P. F. Buxton, J. W. Connor, A. E. Costley, M. P. Gryaznevich, and S. McNamara, On the energy con- finement time in spherical tokamaks: implications for the design of pilot plants and fusion reactors, Plasma Physics and Controlled Fusion 61, 035006 (2019)

    2019

  31. [39]

    Kingham and M

    D. Kingham and M. Gryaznevich, The spherical toka- mak path to fusion power: Opportunities and chal- lenges for development via public–private partnerships, Physics of Plasmas 31, 042507 (2024)

    2024

  32. [40]

    Staebler, C

    G. Staebler, C. Bourdelle, J. Citrin, and R. Waltz, Quasilinear theory and modelling of gyrokinetic turbu- lent transport in tokamaks, Nuclear Fusion 64, 103001 (2024)

    2024

  33. [41]

    R. J. Hawryluk, An empirical approach to tokamak transport, in Physics of Plasmas Close to Thermonu- clear Conditions , Vol. 1, edited by B. Coppi, G. G. Leotta, D. Pfirsch, R. Pozzoli, and E. Sindoni (CEC, Brussels, 1980) pp. 19–46

    1980

  34. [42]

    R. J. Goldston, D. C. McCune, H. H. Towner, S. L. Davis, R. J. Hawryluk, and G. L. Schmidt, New tech- niques for calculating heat and particle source rates due to neutral beam injection in axisymmetric tokamaks, Journal of Computational Physics 43, 61 (1981)

    1981

  35. [43]

    F. Poli, J. Sachdev, J. Breslau, M. Gorelenkova, and X. Yuan, TRANSP v18.2, [Computer Software]https: //dx.doi.org/10.11578/dc.20180627.4 (2018)

    2018 doi

  36. [44]

    Grierson, X

    B. Grierson, X. Yuan, M. Gorelenkova, S. Kaye, N. Lo- gan, O. Meneghini, S. Haskey, J. Buchanan, M. Fitzger- ald, S. Smith, L. Cui, R. Budny, and F. Poli, Orches- trating TRANSP simulations for interpretative and pre- dictive tokamak modeling with OMFIT, Fusion Science and Tec...

    2018

  37. [45]

    Pankin, J

    A. Pankin, J. Breslau, M. Gorelenkova, R. Andre, B. Grierson, J. Sachdev, M. Goliyad, and G. Perumpilly, TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas, Computer Physics Communications 312, 109611 (2025)

    2025

  38. [46]

    G. M. Staebler, J. E. Kinsey, and R. E. Waltz, A theory- based transport model with comprehensive physics, Physics of Plasmas 14, 055909 (2007)

    2007

  39. [47]

    J. E. Kinsey, G. M. Staebler, and R. E. Waltz, The first transport code simulations using the trapped gyro- Landau-fluid model, Physics of Plasmas 15, 055908 (2008)

    2008

  40. [48]

    Staebler, J

    G. Staebler, J. Candy, R. Waltz, J. Kinsey, and W. Solomon, A new paradigm for E × B velocity shear suppression of gyro-kinetic turbulence and the momen- tum pinch, Nuclear Fusion 53, 113017 (2013)

    2013

  41. [49]

    G. M. Staebler, J. Candy, N. T. Howard, and C. Hol- land, The role of zonal flows in the saturation of multi- scale gyrokinetic turbulence, Physics of Plasmas 23, 27 062518 (2016)

    2016

  42. [50]

    G. M. Staebler, J. Candy, E. A. Belli, J. E. Kin- sey, N. Bonanomi, and B. Patel, Geometry depen- dence of the fluctuation intensity in gyrokinetic turbu- lence, Plasma Physics and Controlled Fusion63, 015013 (2020)

    2020

  43. [51]

    Staebler, E

    G. Staebler, E. A. Belli, J. Candy, J. Kinsey, H. Dud- ding, and B. Patel, Verification of a quasi-linear model for gyrokinetic turbulent transport, Nuclear Fusion 61, 116007 (2021)

    2021

  44. [52]

    Dudding, F

    H. Dudding, F. Casson, D. Dickinson, B. Patel, C. Roach, E. Belli, and G. Staebler, A new quasilinear saturation rule for tokamak turbulence with application to the isotope scaling of transport, Nuclear Fusion 62, 096005 (2022)

    2022

  45. [53]

    H. G. Dudding, A new quasilnear saturation rule for tokamak turbulence , Ph.D. thesis, University of York (2022)

    2022

  46. [54]

    Q. T. Pratt, Investigating density fluctuations and rota- tion in tokamak plasmas with Doppler back-scattering , Ph.D. thesis, University of California, Los Angeles (2024)

    2024

  47. [55]

    Candy, E

    J. Candy, E. Belli, and R. Bravenec, A high-accuracy eulerian gyrokinetic solver for collisional plasmas, Jour- nal of Computational Physics 324, 73 (2016)

    2016

  48. [56]

    J. Hall, T. Neiser, O. Meneghini, S. Smith, G. Stae- bler, E. Belli, and J. Candy, Validating the linear model in TGLF with linear CGYRO simulations using ex- perimental DIII-D cases, in 64th APS DPP Meeting (Spokane, W A, 2022)

    2022

  49. [57]

    Patel, M

    B. Patel, M. Hardman, D. Kennedy, M. Giacomin, D. Dickinson, and C. Roach, The impact of E × B shear on microtearing based transport in spherical tokamaks, Nuclear Fusion 65, 026063 (2025)

    2025

  50. [58]

    Avdeeva, J

    G. Avdeeva, J. Candy, , K. Thome, E. Belli, S. Kaye, and G. Staebler, Enhanced shear-stabilization of turbu- lence in NSTX, (submitted to Physics of Plasmas)

  51. [59]

    Neiser, O

    T. Neiser, O. Meneghini, T. Slendebroek, S. Smith, J. McClenaghan, A. Ghiozzi, B. Patel, A. Nelson, G. Avdeeva, C. Roach, F. Casson, H. Dudding, G. Stae- bler, J. Hall, B. Lyons, E. Belli, J. Candy, R. Yu, B. Sammuli, R. Nazikian, and T. Osborne, Large database validation of T...

    2024

  52. [60]

    Avdeeva, K

    G. Avdeeva, K. Thome, S. Smith, D. Battaglia, C. Clauser, W. Guttenfelder, S. Kaye, J. McClenaghan, O. Meneghini, T. Odstrcil, and G. Staebler, Energy transport analysis of NSTX plasmas with the TGLF turbulent and NEO neoclassical transport models, Nu- clear Fusion 63, 126020 (2023)

    2023

  53. [61]

    G. M. Staebler, R. E. Waltz, J. Candy, and J. E. Kinsey, New paradigm for suppression of gyrokinetic turbulence by velocity shear, Phys. Rev. Lett. 110, 055003 (2013)

    2013

  54. [62]

    C. F. Clauser, T. Rafiq, J. Parisi, G. Avdeeva, W. Gut- tenfelder, E. Schuster, and C. Wilson, Electron tem- perature gradient instability and transport analysis in NSTX and NSTX-U plasmas, Physics of Plasmas 32, 022305 (2025)

    2025

  55. [63]

    M. S. Anastopoulos Tzanis, M. R. Hardman, Y. Zhang, X. Zhang, A. Sladkomedova, A. Dnestrovskii, Y. S. Na, J. H. Lee, S. J. Park, T. O’Gorman, H. Lowe, M. Ro- manelli, M. Sertoli, M. Gemmel, J. Woods, H. V. Wil- lett, and S. Team, Integrated modeling of ST40 hot ion plasmas usi...

    2025

  56. [64]

    C.-Y. Lee, S. Kim, Y.-G. Kim, Y. Kim, K. Lee, Y. S. Hwang, and Y.-S. Na, Investigation of the effect of pre- fill gas in VEST discharges by predictive transport sim- ulations, Journal of the Korean Physical Society81, 126 (2022)

    2022

  57. [65]

    Neiser, O

    T. Neiser, O. Meneghini, S. Smith, M. Fasciana, G. Staebler, and J. Candy, Big data validation of the TGLF transport model, in 62nd APS DPP Meeting (Re- mote, 2020)

    2020

  58. [66]

    Abbate, E

    J. Abbate, E. Fable, B. Grierson, A. Pankin, G. Tardini, and E. Kolemen, Large-database cross-verification and validation of tokamak transport models using baselines for comparison, Physics of Plasmas 31, 042506 (2024)

    2024

  59. [67]

    Rafiq, A

    T. Rafiq, A. H. Kritz, J. Weiland, A. Y. Pankin, and L. Luo, Physics basis of Multi-Mode anomalous trans- port module, Physics of Plasmas 20, 032506 (2013)

    2013

  60. [68]

    L. Luo, T. Rafiq, and A. Kritz, Improved Multi- Mode anomalous transport module for tokamak plas- mas, Computer Physics Communications 184, 2267 (2013)

    2013

  61. [69]

    Stability and Transport in Magnetic Confinement Sys- tems (Springer Science & Business Media, 2012)

    2012

  62. [70]

    Rafiq, C

    T. Rafiq, C. Wilson, L. Luo, J. Weiland, E. Schuster, A. Y. Pankin, W. Guttenfelder, and S. Kaye, Electron temperature gradient driven transport model for toka- mak plasmas, Physics of Plasmas 29, 092503 (2022)

    2022

  63. [71]

    Rafiq, J

    T. Rafiq, J. Weiland, A. H. Kritz, L. Luo, and A. Y. Pankin, Microtearing modes in tokamak discharges, Physics of Plasmas 23, 062507 (2016)

    2016

  64. [72]

    Rafiq, G

    T. Rafiq, G. Bateman, A. H. Kritz, and A. Y. Pankin, Development of drift-resistive-inertial ballooning trans- port model for tokamak edge plasmas, Physics of Plas- mas 17, 082511 (2010)

    2010

  65. [73]

    Rafiq, C

    T. Rafiq, C. Wilson, C. Clauser, E. Schuster, J. Wei- land, J. Anderson, S. Kaye, A. Pankin, B. LeBlanc, and R. Bell, Predictive modeling of NSTX discharges with the updated multi-mode anomalous transport module, Nuclear Fusion 64, 076024 (2024)

    2024

  66. [74]

    Rafiq, S

    T. Rafiq, S. Kaye, W. Guttenfelder, J. Weiland, E. Schuster, J. Anderson, and L. Luo, Microtearing in- stabilities and electron thermal transport in low and high collisionality NSTX discharges, Physics of Plasmas 28, 022504 (2021)

    2021

  67. [75]

    F. Poli, R. Andre, N. Bertelli, S. Gerhardt, D. Mueller, and G. Taylor, Simulations towards the achievement of non-inductive current ramp-up and sustainment in the National Spherical Torus Experiment Upgrade, Nuclear Fusion 55, 123011 (2015)

    2015

  68. [76]

    N. A. Lopez and F. M. Poli, Regarding the optimization of O1-mode ECRH and the feasibility of EBW startup on NSTX-U, Plasma Physics and Controlled Fusion 60, 065007 (2018)

    2018

  69. [77]

    Podest` a, D

    M. Podest` a, D. J. Cruz-Zabala, F. M. Poli, J. Dominguez-Palacios, J. W. Berkery, M. Garcia- Mu˜ noz, E. Viezzer, A. Mancini, J. Segado, L. Velarde, and S. M. Kaye, NBI optimization on SMART and im- plications for scenario development, Plasma Physics and Controlled Fusion 66,...

    2024

  70. [78]

    D. J. Cruz-Zabala, M. Podesta, F. M. Poli, S. M. Kaye, M. Garcia-Munoz, E. Viezzer, and J. W. Berkery, Per- formance prediction applying different reduced turbu- lence models to the SMART tokamak, Nuclear Fusion 28 (2024)

    2024

  71. [79]

    Rafiq, Z

    T. Rafiq, Z. Wang, S. Morosohk, E. Schuster, J. Wei- land, W. Choi, and H.-T. Kim, Validating the Multi- Mode Model’s ability to reproduce diverse tokamak sce- narios, Plasma 6, 435 (2023)

    2023

  72. [80]

    A. Y. Pankin, A. H. Kritz, T. Rafiq, A. M. Garofalo, I. Holod, and J. Weiland, Extending the validation of multi-mode model for anomalous transport to high beta poloidal tokamak scenario in DIII-D, Physics of Plasmas 25, 052505 (2018)

    2018

  73. [81]

    Bourdelle, J

    C. Bourdelle, J. Citrin, B. Baiocchi, A. Casati, P. Cot- tier, X. Garbet, F. Imbeaux, and J. Contributors, Core turbulent transport in tokamak plasmas: bridging the- ory and experiment with QuaLiKiz, Plasma Physics and Controlled Fusion 58, 014036 (2015)

    2015

  74. [82]

    Citrin, S

    J. Citrin, S. Maeyama, C. Angioni, N. Bonanomi, C. Bourdelle, F. Casson, E. Fable, T. G¨ orler, P. Man- tica, A. Mariani, M. Sertoli, G. Staebler, T. Watanabe, and J. Contributors, Integrated modelling and multi- scale gyrokinetic validation study of ETG turbulence in a JET hy...

    2022

  75. [83]

    X. Yuan, D. McCune, S. Jardin, R. Budny, and G. Ham- mett, A modular, parallel, multi-region, predictive transport equation solver, installed and available in PTRANSP, in 53rd APS DPP Meeting (Salt Lake City, UT, 2011)

    2011

  76. [84]

    X. Yuan, S. Jardin, R. Budny, G. Staebler, and G. Ham- mett, New predictive capabilities in PTRANSP with PTSOL VER, in 54th APS DPP Meeting (Providence, RI, 2012)

    2012

  77. [85]

    X. Yuan, S. Jardin, G. Hammett, R. Budny, and G. Staebler, Parallel computing aspect in TRANSP with PT-SOL VER, in55th APS DPP Meeting (Denver, CO, 2013)

    2013

  78. [86]

    Avdeeva, K

    G. Avdeeva, K. E. Thome, J. W. Berkery, S. M. Kaye, J. McClenaghan, O. Meneghini, T. Odstrcil, S. A. Sab- bagh, S. P. Smith, and A. D. Turnbull, Accuracy of kinetic equilibrium reconstruction of NSTX and NSTX- U plasmas and its impact on the transport and stabil- ity analysis,...

    2024

  79. [87]

    S. C. Jardin, G. Bateman, G. W. Hammett, and L. P. Ku, Short note: On 1D diffusion problems with a gradient-dependent diffusion coefficient, J. Comput. Phys. 227, 8769–8775 (2008)

    2008

  80. [88]

    L. Lao, H. St. John, R. Stambaugh, A. Kellman, and W. Pfeiffer, Reconstruction of current profile parame- ters and plasma shapes in tokamaks, Nuclear Fusion25, 1611 (1985)

    1985

  81. [89]

    L. L. Lao, H. E. St. John, Q. Peng, J. R. Ferron, E. J. Strait, T. S. Taylor, W. H. Meyer, C. Zhang, and K. I. Y. and, MHD equilibrium reconstruction in the DIII-D tokamak, Fusion Science and Technology48, 968 (2005)

    2005

  82. [90]

    J. E. Menard, R. E. Bell, D. A. Gates, S. M. Kaye, B. P. LeBlanc, F. M. Levinton, S. S. Medley, S. A. Sab- bagh, D. Stutman, K. Tritz, and H. Yuh, Observation of instability-induced current redistribution in a spherical- torus plasma, Phys. Rev. Lett. 97, 095002 (2006)

    2006

  83. [91]

    W. A. Houlberg, K. C. Shaing, S. P. Hirshman, and M. C. Zarnstorff, Bootstrap current and neoclassical transport in tokamaks of arbitrary collisionality and as- pect ratio, Physics of Plasmas 4, 3230 (1997)

    1997

  84. [92]

    Sheng, R

    H. Sheng, R. E. Waltz, and G. M. Staebler, Alfv´ en eigen- mode stability and critical gradient energetic particle transport using the Trapped-Gyro-Landau-Fluid model, Physics of Plasmas 24, 072305 (2017)

    2017

  85. [93]

    Bass and R

    E. Bass and R. Waltz, Prediction of Alfv´ en eigenmode energetic particle transport in ITER scenarios with a critical gradient model, Nuclear Fusion 60, 016032 (2019)

    2019

  86. [94]

    Weiland, T

    J. Weiland, T. Rafiq, and E. Schuster, Fast particles in drift wave turbulence, Physics of Plasmas 30, 042517 (2023)

    2023

  87. [95]

    Rafiq, C

    T. Rafiq, C. Wilson, J. Weiland, L. Whitall, and E. Schuster, Towards efficient and practical models for energetic particle transport in tokamaks, in 63rd APS DPP Meeting (Denver, CO, 2023)

    2023

  88. [96]

    Candy, C

    J. Candy, C. Holland, R. E. Waltz, M. R. Fahey, and E. Belli, Tokamak profile prediction using direct gyroki- netic and neoclassical simulation, Physics of Plasmas 16, 060704 (2009)

    2009

  89. [97]

    Gerhardt, D

    S. Gerhardt, D. Gates, S. Kaye, R. Maingi, J. Menard, S. Sabbagh, V. Soukhanovskii, M. Bell, R. Bell, J. Canik, E. Fredrickson, R. Kaita, E. Kolemen, H. Kugel, B. L. Blanc, D. Mastrovito, D. Mueller, and H. Yuh, Recent progress towards an advanced spheri- cal torus operating p...

    2011

  90. [98]

    Gerhardt, E

    S. Gerhardt, E. Fredrickson, D. Gates, S. Kaye, J. Menard, M. Bell, R. Bell, B. L. Blanc, H. Kugel, S. Sabbagh, and H. Yuh, Calculation of the non- inductive current profile in high-performance NSTX plasmas, Nuclear Fusion 51, 033004 (2011)

    2011

  91. [99]

    Y. Ren, W. Guttenfelder, S. M. Kaye, E. Mazzucato, R. E. Bell, A. Diallo, C. W. Domier, B. P. LeBlanc, K. C. Lee, D. R. Smith, and H. Yuh, Experimental study of parametric dependence of electron-scale turbulence in a spherical tokamak, Physics of Plasmas 19, 056125 (2012)

    2012

  92. [100]

    Y. Ren, W. Guttenfelder, S. Kaye, E. Mazzucato, R. Bell, A. Diallo, C. Domier, B. LeBlanc, K. Lee, M. Podesta, D. Smith, and H. Yuh, Electron-scale tur- bulence spectra and plasma thermal transport respond- ing to continuous E × B shear ramp-up in a spherical tokamak, Nuclear ...

    2013

  93. [101]

    Gerhardt, J

    S. Gerhardt, J. Canik, R. Maingi, D. Battaglia, R. Bell, W. Guttenfelder, B. LeBlanc, D. Smith, H. Yuh, and S. Sabbagh, Progress in understanding the enhanced pedestal H-mode in NSTX, Nuclear Fusion 54, 083021 (2014)

    2014

  94. [102]

    Ruiz Ruiz, W

    J. Ruiz Ruiz, W. Guttenfelder, A. E. White, N. T. Howard, J. Candy, Y. Ren, D. R. Smith, N. F. Loureiro, C. Holland, and C. W. Domier, Validation of gyrokinetic simulations of a National Spherical Torus eXperiment H-mode plasma and comparisons with a high-k scatter- ing synthe...

    2019

  95. [103]

    Y. Ren, W. Wang, W. Guttenfelder, S. Kaye, J. Ruiz- Ruiz, S. Ethier, R. Bell, B. LeBlanc, E. Mazzucato, D. Smith, C. Domier, and H. Yuh, Exploring the regime of validity of global gyrokinetic simulations with spheri- cal tokamak plasmas, Nuclear Fusion60, 026005 (2019)

    2019

  96. [104]

    D. J. Battaglia, W. Guttenfelder, R. E. Bell, A. Di- allo, N. Ferraro, E. Fredrickson, S. P. Gerhardt, S. M. Kaye, R. Maingi, and D. R. Smith, Enhanced pedestal H-mode at low edge ion collisionality on NSTX, Physics 29 of Plasmas 27, 072511 (2020)

    2020

  97. [105]

    ITER Physics Expert Group on Confinement and Transport, ITER Physics Expert Group on Confine- ment Modelling and Database, and ITER Physics Basis Editors, Chapter 2: Plasma confinement and transport, Nuclear Fusion 39, 2175 (1999)

    1999

  98. [106]

    Pankin, D

    A. Pankin, D. McCune, R. Andre, G. Bateman, and A. Kritz, The tokamak Monte Carlo fast ion module nubeam in the National Transport Code Collaboration library, Computer Physics Communications 159, 157 (2004)

    2004

  99. [107]

    E. D. Fredrickson, N. N. Gorelenkov, M. Podest` a, A. Bortolon, S. P. Gerhardt, R. E. Bell, A. Diallo, and B. LeBlanc, Parametric dependence of fast-ion trans- port events on the National Spherical Torus Experi- ment, Nuclear Fusion 54, 093007 (2014)

    2014

  100. [108]

    S. M. Kaye, D. J. Battaglia, D. Baver, E. Belova, J. W. Berkery, V. N. Duarte, N. Ferraro, E. Fredrickson, N. Gorelenkov, W. Guttenfelder, G. Z. Hao, W. Heid- brink, O. Izacard, D. Kim, I. Krebs, R. L. Haye, J. Lestz, D. Liu, L. A. Morton, J. Myra, D. Pfefferle, M. Podest` a, ...

    2019

  101. [109]

    Patel, Confinement physics for a steady state net electric burning spherical tokamak , Ph.D

    B. Patel, Confinement physics for a steady state net electric burning spherical tokamak , Ph.D. thesis, Uni- versity of York (2021)

    2021

  102. [110]

    Meneghini, S

    O. Meneghini, S. Smith, P. Snyder, G. Staebler, J. Candy, E. Belli, L. Lao, M. Kostuk, T. Luce, T. Luda, J. Park, and F. Poli, Self-consistent core-pedestal trans- port simulations with neural network accelerated mod- els, Nuclear Fusion 57, 086034 (2017)

    2017

  103. [111]

    Neiser, O

    T. Neiser, O. Meneghini, S. Smith, J. McClenaghan, D. Orozco, J. Hall, G. Staebler, E. Belli, and J. Candy, Database generation for validation of TGLF and re- training of neural network accelerated TGLF-NN, in 64th APS DPP Meeting (Spokane, W A, 2022)

    2022

  104. [112]

    S. P. Smith, Z. Anthony Xing, T. B. Amara, S. Sebas- tian Denk, E. W. DeShazer, O. Meneghini, T. Neiser, L. Stephey, O. Antepara, C. M. Clark, E. Dart, P. Ding, S. Flanagan, R. Nazikian, D. Schissel, C. Simpson, N. S. Tyler, T. D. Uram, and S. W. Williams, Expedit- ing higher ...

    2024

  105. [113]

    Meneghini, T

    O. Meneghini, T. Slendebroek, B. C. Lyons, K. McLaughlin, J. McClenaghan, L. Stagner, J. Har- vey, T. F. Neiser, A. Ghiozzi, G. Dose, J. Guterl, A. Zalzali, T. Cote, N. Shi, D. Weisberg, S. P. Smith, B. A. Grierson, and J. Candy, FUSE (fusion synthesis engine): A next generati...

    2024 arXiv

  106. [114]

    Slendebroek, A

    T. Slendebroek, A. O. Nelson, O. M. Meneghini, G. Dose, A. G. Ghiozzi, J. Harvey, B. C. Lyons, J. Mc- Clenaghan, T. F. Neiser, D. B. Weisberg, M. G. Yoo, E. Bursch, and C. Holland, Exploring the fusion power plant design space: comparative analysis of positive and negative tri...

    2025

  107. [115]

    E. A. Belli and J. Candy, Kinetic calculation of neoclas- sical transport including self-consistent electron and im- purity dynamics, Plasma Physics and Controlled Fusion 50, 095010 (2008)

    2008

  108. [116]

    E. A. Belli and J. Candy, Full linearized fokker–planck collisions in neoclassical transport simulations, Plasma Physics and Controlled Fusion 54, 015015 (2011)

    2011

  109. [117]

    Neiser, O

    T. Neiser, O. Meneghini, S. Smith, J. McClenaghan, T. Slendebroek, D. Orozco, B. Sammuli, G. Staebler, J. Hall, E. Belli, and J. Candy, Multi-fidelity neural net- work representation of gyrokinetic turbulence, in 63rd APS DPP Meeting (Denver, CO, 2023)

    2023

  110. [118]

    Morosohk, A

    S. Morosohk, A. Pajares, T. Rafiq, and E. Schuster, Neural network model of the multi-mode anomalous transport module for accelerated transport simulations, Nuclear Fusion 61, 106040 (2021)

    2021

  111. [119]

    Morosohk, B

    S. Morosohk, B. Leard, T. Rafiq, and E. Schuster, Ma- chine learning-enhanced model-based scenario optimiza- tion for DIII-D, Nuclear Fusion 64, 056018 (2024)

    2024

  112. [120]

    Leard, Z

    B. Leard, Z. Wang, S. Morosohk, T. Rafiq, and E. Schus- ter, Fast neural-network surrogate model of the updated Multi-Mode anomalous transport module for NSTX-U, IEEE Transactions on Plasma Science 52, 4126 (2024)

    2024

  113. [121]

    Chung, C

    H. Chung, C. Lee, G. Choi, S. Kaye, B. LeBlanc, J. Berkery, and Y.-S. Na, Development of a data-driven neural network model for electron thermal transport in nstx, Nuclear Fusion 65, 086028 (2025)

    2025

  114. [122]

    Leard, T

    B. Leard, T. Rafiq, I. Ward, F. Galfrascoli, E. Schuster, A. Pankin, and M. Gorelenkova, Self-consistent equilib- rium and transport simulations for nstx-u plasmas en- hanced via machine learning surrogate models, Fusion Engineering and Design 219, 115201 (2025)

    2025

  115. [123]

    Pereverzev and P

    G. Pereverzev and P. Yushmanov, ASTRA Automated System for TRansport Analysis in a tokamak (2002)

    2002

  116. [124]

    Fable, C

    E. Fable, C. Angioni, A. A. Ivanov, K. Lackner, O. Maj, S. Y. Medvedev, G. Pautasso, G. V. Pereverzev, W. Treutterer, and the ASDEX Upgrade Team, Dynam- ical coupling between magnetic equilibrium and trans- port in tokamak scenario modelling, with application to current ramps,...

    2013

  117. [125]

    C. Lee, J. Seo, S. Park, J. Lee, S. Kim, B. Kim, C. Byun, Y. Lee, J. Gwak, J. Kang, L. Jung, H.-S. Kim, S.-H. Hong, and Y.-S. Na, Development of integrated suite of codes and its validation on KSTAR, Nuclear Fusion 61, 096020 (2021)

    2021

  118. [126]

    Lee, private communication

    C. Lee, private communication

  119. [127]

    H.-T. Kim, M. Romanelli, X. Yuan, S. Kaye, A. Sips, L. Frassinetti, J. Buchanan, and JET Contributors, Sta- 30 tistical validation of predictive TRANSP simulations of baseline discharges in preparation for extrapolation to JET D–T, Nuclear Fusion 57, 066032 (2017)

    2017

  120. [128]

    Parisi, W

    J. Parisi, W. Guttenfelder, A. Nelson, R. Gaur, A. Kleiner, M. Lampert, G. Avdeeva, J. Berkery, C. Clauser, M. Curie, A. Diallo, W. Dorland, S. Kaye, J. McClenaghan, and F. Parra, Kinetic-ballooning- limited pedestals in spherical tokamak plasmas, Nuclear Fusion 64, 054002 (2024)

    2024

  121. [129]

    Parisi, A

    J. Parisi, A. Nelson, W. Guttenfelder, R. Gaur, J. Berk- ery, S. Kaye, K. Barada, C. Clauser, A. Diallo, D. Hatch, A. Kleiner, M. Lampert, T. Macwan, and J. Menard, Stability and transport of gyrokinetic critical pedestals, Nuclear Fusion 64, 086034 (2024)

    2024

  122. [130]

    J. E. Kinsey, GLF23 modeling of turbulent transport in DIII-D, Fusion Science and Technology48, 1060 (2005)

    2005

  123. [131]

    Zhang, N

    X. Zhang, N. A. Lopez, E. D. Emdee, F. M. Poli, T. O’Gorman, P. F. Buxton, C. Marsden, M. Moscheni, H. F. Lowe, and A. Rengle, Core-edge integrated predic- tive studies of ST40 and NSTX plasmas with the scrape- off layer box model, Physics of Plasmas 32, 032513 (2025)

    2025

  124. [132]

    Podest` a, M

    M. Podest` a, M. Gorelenkova, and R. B. White, A re- duced fast ion transport model for the tokamak trans- port code TRANSP, Plasma Physics and Controlled Fu- sion 56, 055003 (2014)

    2014

  125. [133]

    N. N. Gorelenkov, V. N. Duarte, C. S. Collins, M. Podest` a, and R. B. White, Verification and applica- tion of resonance broadened quasi-linear (RBQ) model with multiple Alfv´ enic instabilities, Physics of Plasmas 26, 072507 (2019)

    2019

  126. [134]

    D. Kim, M. Podest` a, D. Liu, and F. Poli, Orbit model- ing of fast particle redistribution induced by sawtooth instability, Nuclear Fusion 58, 082029 (2018)

    2018

  127. [135]

    Bard´ oczi, M

    L. Bard´ oczi, M. Podest` a, W. W. Heidbrink, and M. A. Van Zeeland, Quantitative modeling of neoclassical tearing mode driven fast ion transport in integrated TRANSP simulations, Plasma Physics and Controlled Fusion 61, 055012 (2019)

    2019

  128. [136]

    Taylor, R

    G. Taylor, R. E. Bell, J. C. Hosea, B. P. LeBlanc, C. K. Phillips, M. Podest` a, E. J. Valeo, J. R. Wilson, J.-W. Ahn, G. Chen, D. L. Green, E. F. Jaeger, R. Maingi, P. M. Ryan, J. B. Wilgen, W. W. Heidbrink, D. Liu, P. T. Bonoli, T. Brecht, M. Choi, and R. W. Harvey, Advances...

    2010

  129. [137]

    H. Y. Yuh, F. M. Levinton, R. E. Bell, J. C. Hosea, S. M. Kaye, B. P. LeBlanc, E. Mazzucato, J. L. Peter- son, D. R. Smith, J. Candy, R. E. Waltz, C. W. Domier, J. Luhmann, N. C., W. Lee, and H. K. Park, Internal transport barriers in the National Spherical Torus Ex- periment,...

    2009

  130. [138]

    Van Compernolle, X

    B. Van Compernolle, X. Jian, N. Bertelli, S. Desai, J. B. Lestz, J. McClenaghan, M. Ono, R. I. Pinsker, and K. Thome, Parametric raytracing modeling for NSTX- U scenario development with high harmonic fast waves and neutral beam injection, Plasma Physics and Con- trolled Fusio...

    2025

  131. [139]

    G. M. Staebler, E. A. Belli, and J. Candy, A flexible gyro-fluid system of equations, Physics of Plasmas 30, 102501 (2023)

    2023

  132. [140]

    Staebler, J

    G. Staebler, J. Kinsey, E. Belli, and J. Candy, Im- proved fidelity to gyrokinetic linear stability with the GFS Gyro-Fluid System for NSTX-U plasmas, in Sher- wood Fusion Theory Conference (Missoula, MT, 2024)

    2024

  133. [141]

    J. E. Kinsey, G. M. Staebler, E. A. Belli, and J. Candy, Analysis of the impact of parallel magnetic fluctuations on linear gyrokinetic stability in NSTX-U and verifica- tion of gyro-fluid models, Physics of Plasmas 32, 072301 (2025)

    2025

  134. [142]

    Stutman, L

    D. Stutman, L. Delgado-Aparicio, N. Gorelenkov, M. Finkenthal, E. Fredrickson, S. Kaye, E. Mazzu- cato, and K. Tritz, Correlation between electron trans- port and shear Alfv´ en activity in the National Spherical Torus Experiment, Phys. Rev. Lett.102, 115002 (2009)

    2009

  135. [143]

    Y. Ren, E. Belova, N. Gorelenkov, W. Guttenfelder, S. M. Kaye, E. Mazzucato, J. L. Peterson, D. R. Smith, D. Stutman, K. Tritz, W. X. Wang, H. Yuh, R. E. Bell, C. W. Domier, and B. P. LeBlanc, Recent progress in understanding electron thermal transport in NSTX, Nu- clear Fusio...

    2017

  136. [144]

    N. N. Gorelenkov, D. Stutman, K. Tritz, A. Boozer, L. Delgado-Aparicio, E. Fredrickson, S. Kaye, and R. White, Anomalous electron transport due to multi- ple high frequency beam ion driven Alfv´ en eigenmodes, Nuclear Fusion 50, 084012 (2010)

    2010

  137. [145]

    Y. I. Kolesnichenko, Y. V. Yakovenko, and V. V. Lut- senko, Channeling of the energy and momentum dur- ing energetic-ion-driven instabilities in fusion plasmas, Phys. Rev. Lett. 104, 075001 (2010)

    2010

  138. [146]

    Kolesnichenko, Y

    Y. Kolesnichenko, Y. Yakovenko, V. V. Lutsenko, R. B. White, and A. Weller, Effects of energetic-ion-driven in- stabilities on plasma heating, transport and rotation in toroidal systems, Nuclear Fusion 50, 084011 (2010)

    2010

  139. [147]

    E. V. Belova, N. N. Gorelenkov, E. D. Fredrickson, K. Tritz, and N. A. Crocker, Coupling of neutral- beam-driven compressional Alfv´ en eigenmodes to ki- netic Alfv´ en waves in NSTX tokamak and energy chan- neling, Phys. Rev. Lett. 115, 015001 (2015)

    2015

  140. [148]

    E. V. Belova, N. N. Gorelenkov, N. A. Crocker, J. B. Lestz, E. D. Fredrickson, S. Tang, and K. Tritz, Non- linear simulations of beam-driven compressional Alfv´ en eigenmodes in NSTX, Physics of Plasmas 24, 042505 (2017)

    2017

  141. [149]

    J. B. Lestz, E. V. Belova, and N. N. Gorelenkov, Hybrid simulations of sub-cyclotron compressional and global Alfv´ en eigenmode stability in spherical tokamaks, Nu- clear Fusion 61, 086016 (2021)

    2021

  142. [150]

    S. C. Jardin, N. M. Ferraro, W. Guttenfelder, S. M. Kaye, and S. Munaretto, Ideal MHD limited electron temperature in spherical tokamaks, Phys. Rev. Lett. 128, 245001 (2022)

    2022

  143. [151]

    S. C. Jardin, N. M. Ferraro, W. Guttenfelder, S. M. Kaye, and S. Munaretto, Ideal MHD induced tempera- ture flattening in spherical tokamaks, Physics of Plas- mas 30, 042507 (2023)

    2023

  144. [152]

    Meneghini, S

    O. Meneghini, S. Smith, L. Lao, O. Izacard, Q. Ren, J. Park, J. Candy, Z. Wang, C. Luna, V. Izzo, B. Grier- son, P. Snyder, C. Holland, J. Penna, G. Lu, P. Raum, A. McCubbin, D. Orlov, E. Belli, N. Ferraro, R. Prater, T. Osborne, A. Turnbull, and G. Staebler, Integrated modeli...

    2015

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.