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arxiv: 2605.09720 · v1 · submitted 2026-05-10 · ⚛️ physics.plasm-ph · physics.comp-ph

Recognition: 2 theorem links

· Lean Theorem

Predictive capabilities of the integrated modeling TRANSP code for tokamak plasmas

A.Y. Pankin, B.A. Grierson, G.W. Hammett, J.B. Lestz, J. Breslau, M. Goliyad, M.V. Gorelenkova, R. Budny, S.C. Jardin, X. Yuan

Pith reviewed 2026-05-12 03:14 UTC · model grok-4.3

classification ⚛️ physics.plasm-ph physics.comp-ph
keywords TRANSPtokamakpredictive modelingplasma transportPT_SOLVERgyrokinetic modelingnumerical solverfusion simulation
0
0 comments X

The pith

The TRANSP code's PT_SOLVER module and T3D/GX integration deliver a robust numerical framework for time-dependent predictive transport simulations of tokamak plasmas.

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

This paper describes advances in the TRANSP integrated modeling code focused on its predictive capabilities. The new PT_SOLVER module solves coupled equations for particle density, electron and ion energy, and toroidal angular momentum with an implicit Newton method that manages stiff transport behavior. It also details the interface to the T3D/GX framework for incorporating high-fidelity gyrokinetic models. Verification relies on analytical benchmarks, manufactured solutions, and comparisons with other codes using TGLF/NEO transport models. The work establishes that this setup supports reliable simulations and serves as a platform for combining reduced and detailed models in future tokamak studies.

Core claim

The predictive TRANSP framework has a robust numerical implementation for time-dependent predictive transport simulations. PT_SOLVER advances the coupled transport equations using an implicit Newton method that includes source coupling, moving-geometry terms, and nonlinear stabilization based on modified Peclet numbers to handle stiffness from gradient-dependent diffusivities. The TRANSP Interface to the modular T3D/GX workflow enables coupling to high-fidelity gyrokinetic models for turbulent transport. Verification through analytical and manufactured solution benchmarks plus code-to-code comparisons confirms convergence and accuracy, providing a basis for hybrid reduced and high-fidelity预测

What carries the argument

PT_SOLVER, a modular multi-region parallel solver that uses an implicit Newton method with nonlinear stabilization based on modified Peclet numbers to advance coupled transport equations while handling source terms and stiffness.

If this is right

  • The solver handles source terms for heating, current drive, alpha-particle effects, and collisional energy exchange.
  • Nonlinear stabilization via modified Peclet numbers controls discretization in regions of steep gradients.
  • Convergence is assessed using both residual norms and profile-change measures.
  • The T3D/GX interface supports coupled simulations with high-fidelity gyrokinetic transport models.
  • The framework enables future hybrid workflows that combine reduced models with high-fidelity predictions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This numerical approach could reduce the computational cost of exploring operating scenarios for devices like ITER while maintaining accuracy.
  • The modular design may allow straightforward addition of new physics such as impurity transport or edge effects.
  • Validated predictive runs might improve the reliability of extrapolations from current experiments to future fusion facilities.
  • Such tools could eventually support near-real-time profile predictions for plasma control and optimization.

Load-bearing premise

The chosen verification benchmarks and code-to-code comparisons with TGLF/NEO are sufficient to demonstrate robustness across the full range of conditions in real tokamak experiments.

What would settle it

A significant mismatch between TRANSP-predicted profiles and measured data from a well-characterized tokamak discharge where input conditions and uncertainties are precisely known.

Figures

Figures reproduced from arXiv: 2605.09720 by A.Y. Pankin, B.A. Grierson, G.W. Hammett, J.B. Lestz, J. Breslau, M. Goliyad, M.V. Gorelenkova, R. Budny, S.C. Jardin, X. Yuan.

Figure 1
Figure 1. Figure 1: Plot of the stabilization function F(x) versus x = Pˆ e. For x ≥ 10, F(x) = 0; for x near 0, F(x) ≈ 1; and for x < −10, F(x) = −x. Rj = U n+1 j − U n j ∆t + 1 V ′ j  A˜ j U n+1 j−1 + Bj U n+1 j + C˜ j U n+1 j+1  − Sj . (14) The L2 norm of the residual is then ∥R∥2 =   X N j=1 R2 j ∆ξ   1/2 . (15) To monitor convergence in the electron energy solver, PT SOLVER evaluates the signed discrete energy-bala… view at source ↗
Figure 2
Figure 2. Figure 2: Initial and final profiles for the constant-diffusivity coupled benchmark with the following [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: MMS benchmark with evolving H-mode-like pedestal profiles. Left and middle panels [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Profiles evolution for the coupled three [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The maximum residual norms over all transport channels as a function of New [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of standalone PT SOLVER and TGYRO results for the DIII-D H-mode discharge 125236 using TGLF for anomalous transport and NEO for neoclassical transport in both workflows. Red solid curves denote PT SOLVER, blue dashed curves denote TGYRO, and gray curves denote the experimental profiles. The dotted vertical line at ρ = 0.8 marks the outer boundary of the predictive region. The agreement is genera… view at source ↗
Figure 7
Figure 7. Figure 7: Schematic illustration of the multilevel parallelization strategy used in PT SOLVER for coupled transport calcu￾lations with TGLF. The scaling results that correspond to this im￾plementation are shown in [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Parallel scaling of the PT SOLVER with the TGLF model for three representative configurations. Left: scaling of standalone TGLF with nky = 21. Middle: scaling of PT SOLVER TGLF for nzones = 42 > Ncores. Right: scaling of PT SOLVER with parallel TGLF for nzones = 20 < Ncores. In each panel, the solid curve shows the measured model speedup, and the dashed line indicates ideal linear scaling. Standalone TGLF … view at source ↗
Figure 9
Figure 9. Figure 9: Interface-focused schematic of the TRANSP–T3D/GX coupling shows the modular or [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of predicted profiles using PT [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗
read the original abstract

This paper expands on the TRANSP description given in Computer Physics Communications 312 (2025) 109611 by describing recent progress in TRANSP's predictive functionality and emphasizing the development of the PT_SOLVER module and integration of the high-fidelity T3D/GX framework for plasma profile prediction using a high-fidelity gyrokinetic model for turbulent transport. PT_SOLVER is a modular, multi-region, parallel solver for coupled transport equations of particle density, electron and ion energy, and toroidal angular momentum that uses an implicit Newton method to advance the solution of these equations. The numerical formulation includes source coupling, moving-geometry terms, and nonlinear stabilization based on modified Peclet numbers, thereby enabling the PT_SOLVER to handle the stiffness associated with gradient-dependent transport models. Stabilization occurs via a nonlinear function controlling discretization in zones of steep gradients or rapidly changing transport coefficients. Source terms that account for heating, current drive, alpha-particle effects, and collisional energy exchange are handled thoroughly, and both residual norms and profile-change measures are used to assess convergence. Verification is carried out using analytical benchmark solutions, manufactured solution benchmarks, convergence studies of stiff gradient-dependent diffusivities, and code-to-code comparisons of TGYRO using the TGLF/NEO models for anomalous and neoclassical transport. This paper also describes the TRANSP Interface to the modular T3D/GX workflow and presents verification examples related to the interface for coupled prediction simulations. The results in this paper confirm that the predictive TRANSP framework has a robust numerical implementation for time-dependent predictive transport simulations, and it provides a basis for future hybrid reduced and high-fidelity workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper expands on the TRANSP code's predictive capabilities for tokamak plasmas by detailing the PT_SOLVER module, a modular parallel solver that uses an implicit Newton method to advance coupled transport equations for particle density, electron/ion energy, and toroidal angular momentum. It incorporates source coupling, moving-geometry terms, and nonlinear Peclet-number stabilization to handle stiffness from gradient-dependent transport models. Verification includes analytical benchmarks, manufactured solutions, stiff diffusivity convergence studies, and code-to-code comparisons with TGYRO using TGLF/NEO for anomalous and neoclassical transport. The work also describes the TRANSP interface to the T3D/GX high-fidelity gyrokinetic workflow and presents associated verification examples.

Significance. If the reported verification results hold, the paper establishes a robust numerical foundation for time-dependent predictive transport simulations in TRANSP. The use of multiple independent verification methods (analytical solutions, manufactured solutions, dedicated stiff-diffusivity tests, and external code comparisons) is a clear strength that supports reliability for stiff, coupled systems and provides a credible basis for hybrid reduced-order and high-fidelity modeling workflows in fusion plasma research.

minor comments (1)
  1. The description of convergence assessment (residual norms versus profile-change measures) in the PT_SOLVER section would benefit from a brief quantitative example showing how the two criteria are balanced in a stiff test case.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive review, accurate summary of the PT_SOLVER and T3D/GX contributions, and recommendation to accept the manuscript. There are no major comments requiring a point-by-point response.

Circularity Check

0 steps flagged

No significant circularity in verification chain

full rationale

The paper's central claim is that the PT_SOLVER implicit Newton scheme provides robust numerical implementation for stiff time-dependent transport equations, confirmed via external verification. This rests on analytical benchmark solutions, manufactured solution tests, dedicated convergence studies for gradient-dependent diffusivities, and independent code-to-code comparisons against TGLF/NEO and TGYRO. The single reference to a prior TRANSP description (CPC 2025) supplies only background context and does not carry any uniqueness theorem, ansatz, or fitted parameter that the present results reduce to by construction. No self-definitional loops, renamed empirical patterns, or load-bearing self-citations appear in the derivation or verification steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on standard numerical methods for PDE solving and established transport models from prior literature, with no new free parameters, ad-hoc axioms, or invented physical entities introduced.

axioms (2)
  • standard math Implicit Newton methods with nonlinear stabilization via modified Peclet numbers are stable for stiff gradient-dependent transport equations.
    Invoked in the description of PT_SOLVER numerical formulation.
  • domain assumption TGLF and NEO models provide adequate representations of anomalous and neoclassical transport for verification purposes.
    Used in code-to-code comparison benchmarks.

pith-pipeline@v0.9.0 · 5644 in / 1231 out tokens · 37102 ms · 2026-05-12T03:14:27.446767+00:00 · methodology

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Works this paper leans on

55 extracted references · 55 canonical work pages

  1. [1]

    An Empirical Approach to Tokamak Transport

    R.J. Hawryluk. “An Empirical Approach to Tokamak Transport”. In:Physics of Plasmas Close to Thermonuclear Conditions, ed. by B. Coppi, et al.Vol. 1 (1980), pp. 19–46

    work page 1980

  2. [2]

    June 2018.doi:10.11578/dc.20180627.4.url:https: //www.osti.gov/biblio/code-12542

    Joshua Breslau et al.TRANSP. June 2018.doi:10.11578/dc.20180627.4.url:https: //www.osti.gov/biblio/code-12542

    work page doi:10.11578/dc.20180627.4.url:https: 2018

  3. [3]

    TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas

    A.Y. Pankin et al. “TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas”. In:Computer Physics Communications312 (2025), p. 109611.issn: 0010-4655.doi:https://doi.org/10.1016/j.cpc.2025.109611.url:https://www. sciencedirect.com/science/article/pii/S0010465525001134

    work page doi:10.1016/j.cpc.2025.109611.url:https://www 2025

  4. [4]

    Alpha heating in ITER L-mode and H-mode plasmas

    R.V. Budny. “Alpha heating in ITER L-mode and H-mode plasmas”. In:Nuclear Fusion52.1 (Nov. 2011), p. 013001.doi:10.1088/0029-5515/52/1/013001.url:https://doi.org/ 10.1088/0029-5515/52/1/013001

    work page doi:10.1088/0029-5515/52/1/013001.url:https://doi.org/ 2011

  5. [5]

    Validation of D–T fusion power prediction capability against 2021 JET D–T experiments

    Hyun-Tae Kim et al. “Validation of D–T fusion power prediction capability against 2021 JET D–T experiments”. In:Nuclear Fusion63.11 (Oct. 2023), p. 112004.doi:10.1088/1741- 4326/ace26d.url:https://doi.org/10.1088/1741-4326/ace26d

    work page doi:10.1088/1741- 2021

  6. [6]

    Core fusion power gain and alpha heating in JET, TFTR, and ITER

    R. V. Budny and J. G. Cordey. “Core fusion power gain and alpha heating in JET, TFTR, and ITER”. In:Nuclear Fusion56 (2016), p. 056002

    work page 2016

  7. [7]

    Alpha heating, isotopic mass, and fast ion effects in deuterium–tritium experiments

    R. V. Budny and JET Contributors. “Alpha heating, isotopic mass, and fast ion effects in deuterium–tritium experiments”. In:Nuclear Fusion58 (2018), p. 096011. 1https://creativecommons.org/licenses/by/4.0/ 30

    work page 2018

  8. [8]

    Large-database cross-verification and validation of tokamak transport models using baselines for comparison

    J. Abbate et al. “Large-database cross-verification and validation of tokamak transport models using baselines for comparison”. In:Physics of Plasmas31.4 (2024), p. 042506.doi:10.1063/ 5.0190908

    work page 2024

  9. [9]

    Energy transport analysis of NSTX plasmas with the TGLF turbulent and NEO neoclassical transport models

    G. Avdeeva et al. “Energy transport analysis of NSTX plasmas with the TGLF turbulent and NEO neoclassical transport models”. In:Nuclear Fusion63.12 (Oct. 2023), p. 126020. doi:10.1088/1741-4326/acfc56.url:https://doi.org/10.1088/1741-4326/acfc56

    work page doi:10.1088/1741-4326/acfc56.url:https://doi.org/10.1088/1741-4326/acfc56 2023

  10. [10]

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

    J. B. Lestz et al. “Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas”. In:Plasma Physics and Controlled Fusion67.10 (2025), p. 105029.doi:10.1088/1361-6587/ae0c35

    work page doi:10.1088/1361-6587/ae0c35 2025

  11. [11]

    Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model

    J. B. Lestz et al. “Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model”. In:Plasma Physics and Controlled Fusion67.10 (2025), p. 105030. doi:10.1088/1361-6587/ae0c34

    work page doi:10.1088/1361-6587/ae0c34 2025

  12. [12]

    TRANSP-TGLF core predictive modeling of the JET DT baseline sce- nario

    F. Auriemma et al. “TRANSP-TGLF core predictive modeling of the JET DT baseline sce- nario”. In:Nuclear Fusion66.5 (Apr. 2026), p. 056043.doi:10.1088/1741-4326/ae5de2. url:https://doi.org/10.1088/1741-4326/ae5de2

    work page doi:10.1088/1741-4326/ae5de2 2026

  13. [13]

    Pereverzev and P

    G. Pereverzev and P. Yushmanov.ASTRA automated system for TRansport analysis in a tokamak. Tech. rep. IPP 5/98. Max-Planck-Institut f¨ ur Plasmaphysik, 2002

    work page 2002

  14. [14]

    Cenacchi and A

    G. Cenacchi and A. Taroni.JETTO: A Free Boundary Plasma Transport Code (Basic Ver- sion). Tech. rep. JET-IR(88)03. JET / ENEA, 1988

    work page 1988

  15. [15]

    JINTRAC: A system of codes for integrated simulation of Tokamak scenarios

    M. Romanelli et al. “JINTRAC: A system of codes for integrated simulation of Tokamak scenarios”. In:Plasma and Fusion Research9 (2014), p. 3403023.doi:10 . 1585 / pfr . 9 . 3403023

    work page 2014

  16. [16]

    An efficient transport solver for tokamak plasmas

    J. M. Park et al. “An efficient transport solver for tokamak plasmas”. In:Computer Physics Communications214 (2017), pp. 1–5.doi:10.1016/j.cpc.2016.12.018

    work page doi:10.1016/j.cpc.2016.12.018 2017

  17. [17]

    Integrated modeling of highβ N steady state scenario on DIII-D

    J. M. Park et al. “Integrated modeling of highβ N steady state scenario on DIII-D”. In: Physics of Plasmas25 (2018), p. 012506

    work page 2018

  18. [18]

    FUSE (Fusion Synthesis Engine): A next generation framework for integrated design of fusion pilot plants

    O. Meneghini et al. “FUSE (Fusion Synthesis Engine): A Next Generation Framework for Integrated Design of Fusion Pilot Plants”. In:arXiv preprint arXiv:2409.05894(2024).doi: 10.48550/arXiv.2409.05894. arXiv:2409.05894 [physics.plasm-ph]

    work page doi:10.48550/arxiv.2409.05894 2024

  19. [19]

    TORAX: A Fast and Differentiable Tokamak Transport Simulator in JAX

    Jonathan Citrin et al. “TORAX: A Fast and Differentiable Tokamak Transport Simulator in JAX”. In:arXiv preprint arXiv:2406.06718(2024).doi:10.48550/arXiv.2406.06718. arXiv:2406.06718 [physics.plasm-ph]

    work page doi:10.48550/arxiv.2406.06718 2024

  20. [20]

    On 1D diffusion problems with a gradient-dependent diffusion coefficient

    S.C. Jardin et al. “On 1D diffusion problems with a gradient-dependent diffusion coefficient”. In:Journal of Computational Physics227.20 (2008), pp. 8769–8775.issn: 0021-9991.doi: https://doi.org/10.1016/j.jcp.2008.06.032.url:https://www.sciencedirect.com/ science/article/pii/S0021999108003616

    work page doi:10.1016/j.jcp.2008.06.032.url:https://www.sciencedirect.com/ 2008

  21. [21]

    Stellarator profile predictions using Trinity3D and GX

    Tony Qian et al. “Stellarator profile predictions using Trinity3D and GX”. In:APS Division of Plasma Physics Meeting Abstracts. Vol. 2022. APS Meeting Abstracts. Jan. 2022, BO03.006

    work page 2022

  22. [22]

    TRINITY: A unified treatment of turbulence, transport, and heating in magnetized plasmas

    Michael Barnes. “TRINITY: A unified treatment of turbulence, transport, and heating in magnetized plasmas”. eprint arXiv:0901.2868. Ph.D. thesis. University of Maryland, 2008. arXiv:0901.2868 [physics.plasm-ph]

    work page arXiv 2008

  23. [23]

    Laguerre-Hermite pseudo-spectral velocity formulation of gyrokinetics

    N. R. Mandell, W. Dorland, and M. Landreman. “Laguerre-Hermite pseudo-spectral velocity formulation of gyrokinetics”. In:Journal of Plasma Physics84.1 (2018), p. 905840108.doi: 10.1017/S0022377818000041. 31

    work page doi:10.1017/s0022377818000041 2018

  24. [24]

    GX: a GPU-native gyrokinetic turbulence code for tokamak and stel- larator design

    N. R. Mandell et al. “GX: a GPU-native gyrokinetic turbulence code for tokamak and stel- larator design”. In:Journal of Plasma Physics90.4 (2024), p. 905900402.doi:10 . 1017 / S0022377824000631

    work page 2024

  25. [25]

    Dynamic modeling of transport and positional control of tokamaks

    S. C. Jardin, N. Pomphrey, and J. DeLucia. “Dynamic modeling of transport and positional control of tokamaks”. In:Journal of Computational Physics66.2 (1986), pp. 481–507.doi: 10.1016/0021-9991(86)90077-X

    work page doi:10.1016/0021-9991(86)90077-x 1986

  26. [26]

    Boca Raton: CRC Press, 2010

    Stephen Jardin.Computational Methods in Plasma Physics. Boca Raton: CRC Press, 2010. isbn: 9780429075537.doi:10.1201/EBK1439810958.url:https://www.taylorfrancis. com/books/mono/10.1201/EBK1439810958/computational- methods- plasma- physics- stephen-jardin

    work page doi:10.1201/ebk1439810958.url:https://www.taylorfrancis 2010

  27. [27]

    Stable numeric scheme for diffusion equation with a stiff transport

    G.V. Pereverzev and G. Corrigan. “Stable numeric scheme for diffusion equation with a stiff transport”. In:Computer Physics Communications179.8 (2008), pp. 579–585.issn: 0010- 4655.doi:10 . 1016 / j . cpc . 2008 . 05 . 006.url:https : / / www . sciencedirect . com / science/article/pii/S001046550800218X

    work page 2008

  28. [28]

    High-order implicit solver in con- servative formulation for tokamak plasma transport equations

    Andrei Ludvig-Osipov, Dmytro Yadykin, and P¨ ar Strand. “High-order implicit solver in con- servative formulation for tokamak plasma transport equations”. In:Computer Physics Com- munications311 (2025), p. 109570.doi:10.1016/j.cpc.2025.109570

    work page doi:10.1016/j.cpc.2025.109570 2025

  29. [29]

    Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

    Federico Felici et al. “Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model”. In:Nuclear Fusion 58.9 (2018), p. 096006.doi:10.1088/1741-4326/aac8f0

    work page doi:10.1088/1741-4326/aac8f0 2018

  30. [30]

    Rapid optimization of stationary tokamak plasmas in RAPTOR: demonstration for the ITER hybrid scenario with neural network surrogate transport model QLKNN

    Simon Van Mulders et al. “Rapid optimization of stationary tokamak plasmas in RAPTOR: demonstration for the ITER hybrid scenario with neural network surrogate transport model QLKNN”. In:Nuclear Fusion61.8 (2021), p. 086019.doi:10.1088/1741-4326/ac0d12

    work page doi:10.1088/1741-4326/ac0d12 2021

  31. [31]

    Tokamak profile prediction using direct gyrokinetic and neoclassical simula- tion

    J. Candy et al. “Tokamak profile prediction using direct gyrokinetic and neoclassical simula- tion”. In:Physics of Plasmas16.6 (2009), p. 060704.doi:10.1063/1.3167820

    work page doi:10.1063/1.3167820 2009

  32. [32]

    Direct multiscale coupling of a transport code to gyrokinetic turbulence codes

    M. Barnes et al. “Direct multiscale coupling of a transport code to gyrokinetic turbulence codes”. In:Physics of Plasmas17.5 (2010), p. 056109.doi:10.1063/1.3323082

    work page doi:10.1063/1.3323082 2010

  33. [33]

    A gyro-Landau-fluid transport model

    R. E. Waltz et al. “A gyro-Landau-fluid transport model”. In:Physics of Plasmas4.7 (1997), pp. 2482–2496.doi:10.1063/1.872228.url:https://doi.org/10.1063/1.872228

    work page doi:10.1063/1.872228.url:https://doi.org/10.1063/1.872228 1997

  34. [34]

    Burning Plasma Confinement Projections and Renormalization of the GLF23 Drift-Wave Transport Model

    Jonathan E. Kinsey, Gary M. Staebler, and Ronald E. Waltz. “Burning Plasma Confinement Projections and Renormalization of the GLF23 Drift-Wave Transport Model”. In:Fusion Science and Technology44.4 (2003), pp. 763–775.doi:10.13182/FST03-A414.url:https: //doi.org/10.13182/FST03-A414

    work page doi:10.13182/fst03-a414.url:https: 2003

  35. [35]

    The first transport code simulations using the trapped gyro-Landau-fluid model

    J. E. Kinsey, G. M. Staebler, and R. E. Waltz. “The first transport code simulations using the trapped gyro-Landau-fluid model”. In:Physics of Plasmas15.5 (2008), p. 055908.doi: 10.1063/1.2889008

    work page doi:10.1063/1.2889008 2008

  36. [37]

    Physics basis of Multi-Mode anomalous transport module

    T. Rafiq et al. “Physics basis of Multi-Mode anomalous transport module”. In:Physics of Plasmas20.3 (2013), p. 032506.doi:10.1063/1.4794288

    work page doi:10.1063/1.4794288 2013

  37. [38]

    Microtearing modes in tokamak discharges

    T. Rafiq et al. “Microtearing modes in tokamak discharges”. In:Physics of Plasmas23.6 (2016), p. 062507.doi:10.1063/1.4953609. 32

    work page doi:10.1063/1.4953609 2016

  38. [39]

    The critical temperature gradient model of plasma transport: Applications to JET and future tokamaks

    P-H. Rebut, P.P. Lallia, and M.L. Watkins. “The critical temperature gradient model of plasma transport: Applications to JET and future tokamaks”. In:Plasma Physics and Con- trolled Nuclear Fusion Research (Proc. 12th IAEA Conf., Nice, France). Vol. 2. IAEA, 1989, p. 191

    work page 1989

  39. [40]

    TSC simulation of Ohmic discharges in TFTR

    S.C. Jardin, M.G. Bell, and N. Pomphrey. “TSC simulation of Ohmic discharges in TFTR”. In:Nuclear Fusion33.3 (Mar. 1993), p. 371.doi:10 . 1088 / 0029 - 5515 / 33 / 3 / I01.url: https://dx.doi.org/10.1088/0029-5515/33/3/I01

    work page doi:10.1088/0029-5515/33/3/i01 1993

  40. [41]

    Transport simulation on L-mode and improved confinement associated with current profile modification

    A Fukuyama et al. “Transport simulation on L-mode and improved confinement associated with current profile modification”. In:Plasma Physics and Controlled Fusion37.6 (June 1995), p. 611.doi:10.1088/0741-3335/37/6/002.url:https://dx.doi.org/10.1088/ 0741-3335/37/6/002

    work page doi:10.1088/0741-3335/37/6/002.url:https://dx.doi.org/10.1088/ 1995

  41. [42]

    Intermittentβcollapse after NBCD turn-off in JT-60U fully non-inductive reversed shear discharges

    N Takei et al. “Intermittentβcollapse after NBCD turn-off in JT-60U fully non-inductive reversed shear discharges”. In:Plasma Physics and Controlled Fusion49.3 (Feb. 2007), p. 335. doi:10 . 1088 / 0741 - 3335 / 49 / 3 / 011.url:https : / / dx . doi . org / 10 . 1088 / 0741 - 3335/49/3/011

    work page 2007

  42. [43]

    Paleoclassical electron heat transport

    J.D. Callen. “Paleoclassical electron heat transport”. In:Nuclear Fusion45.9 (Aug. 2005), p. 1120.doi:10.1088/0029-5515/45/9/012.url:https://dx.doi.org/10.1088/0029- 5515/45/9/012

    work page doi:10.1088/0029-5515/45/9/012.url:https://dx.doi.org/10.1088/0029- 2005

  43. [44]

    Effect of impurity particles on the finite-aspect ratio neoclas- sical ion thermal conductivity in a tokamak

    C. S. Chang and F. L. Hinton. “Effect of impurity particles on the finite-aspect ratio neoclas- sical ion thermal conductivity in a tokamak”. In:The Physics of Fluids29.10 (Oct. 1986), pp. 3314–3316.issn: 0031-9171.doi:10.1063/1.865847. eprint:https://pubs.aip.org/ aip / pfl / article - pdf / 29 / 10 / 3314 / 12413384 / 3314 \ _1 \ _online . pdf.url:https...

    work page doi:10.1063/1.865847 1986

  44. [45]

    Kinetic calculation of neoclassical transport including self-consistent electron and impurity dynamics

    E A Belli and J Candy. “Kinetic calculation of neoclassical transport including self-consistent electron and impurity dynamics”. In:Plasma Physics and Controlled Fusion50.9 (July 2008), p. 095010.doi:10.1088/0741-3335/50/9/095010.url:https://dx.doi.org/10.1088/ 0741-3335/50/9/095010

    work page doi:10.1088/0741-3335/50/9/095010.url:https://dx.doi.org/10.1088/ 2008

  45. [46]

    Full linearized Fokker–Planck collisions in neoclassical transport simulations

    E A Belli and J Candy. “Full linearized Fokker–Planck collisions in neoclassical transport simulations”. In:Plasma Physics and Controlled Fusion54.1 (Dec. 2011), p. 015015.doi: 10.1088/0741-3335/54/1/015015.url:https://dx.doi.org/10.1088/0741-3335/54/1/ 015015

    work page doi:10.1088/0741-3335/54/1/015015.url:https://dx.doi.org/10.1088/0741-3335/54/1/ 2011

  46. [47]

    Bootstrap current and neoclassical transport in tokamaks of arbitrary collisionality and aspect ratio

    W. A. Houlberg et al. “Bootstrap current and neoclassical transport in tokamaks of arbitrary collisionality and aspect ratio”. In:Physics of Plasmas4.9 (Sept. 1997), pp. 3230–3242.issn: 1070-664X.doi:10.1063/1.872465. eprint:https://pubs.aip.org/aip/pop/article- pdf/4/9/3230/19108363/3230\_1\_online.pdf.url:https://doi.org/10.1063/1. 872465

    work page doi:10.1063/1.872465 1997

  47. [48]

    A new quasilinear saturation rule for tokamak turbulence with appli- cation to the isotope scaling of transport

    H.G. Dudding et al. “A new quasilinear saturation rule for tokamak turbulence with appli- cation to the isotope scaling of transport”. In:Nuclear Fusion62.9 (July 2022), p. 096005. doi:10.1088/1741-4326/ac7a4d.url:https://doi.org/10.1088/1741-4326/ac7a4d

    work page doi:10.1088/1741-4326/ac7a4d.url:https://doi.org/10.1088/1741-4326/ac7a4d 2022

  48. [49]

    Database generation for validation of TGLF and retraining of neural network accelerated TGLF-NN

    Tom F. Neiser et al. “Database generation for validation of TGLF and retraining of neural network accelerated TGLF-NN”. In:APS Division of Plasma Physics Meeting Abstracts. Abstract GP11.00010. 2022, GP11.00010

    work page 2022

  49. [50]

    Multi-fidelity neural network representation of gyrokinetic turbu- lence

    Tom F. Neiser et al. “Multi-fidelity neural network representation of gyrokinetic turbu- lence”. In:APS Division of Plasma Physics Meeting Abstracts. Abstract PP11.00039. 2023, PP11.00039. 33

    work page 2023

  50. [51]

    Gyro-Landau fluid equations for trapped and passing particles

    G. M. Staebler, J. E. Kinsey, and R. E. Waltz. “Gyro-Landau fluid equations for trapped and passing particles”. In:Physics of Plasmas12.10 (2005), p. 102508.doi:10.1063/1.2044587

    work page doi:10.1063/1.2044587 2005

  51. [52]

    A theory-based transport model with com- prehensive physics

    G. M. Staebler, J. E. Kinsey, and R. E. Waltz. “A theory-based transport model with com- prehensive physics”. In:Physics of Plasmas14.5 (2007), p. 055909.doi:10.1063/1.2436852

    work page doi:10.1063/1.2436852 2007

  52. [53]

    Kinetic calculation of neoclassical transport including self- consistent electron and impurity dynamics

    Emily Ann Belli and Jeff Candy. “Kinetic calculation of neoclassical transport including self- consistent electron and impurity dynamics”. In:Plasma Physics and Controlled Fusion50 (2008), p. 095010

    work page 2008

  53. [54]

    Large Database Validation of TGLF on DIII-D and MAST-U Plasmas

    T.F. Neiser. “Large Database Validation of TGLF on DIII-D and MAST-U Plasmas”. In:US- EU Transport Task Force. Asheville, NC, Apr. 2024.url:https://conferences.union. wisc.edu/ttf/conference-agenda/

    work page 2024

  54. [55]

    A quasi-linear model of electromagnetic turbulent transport and its application to flux-driven transport predictions for STEP

    M. Giacomin et al. “A quasi-linear model of electromagnetic turbulent transport and its application to flux-driven transport predictions for STEP”. In:Journal of Plasma Physics 91.1 (2025), E16.doi:10.1017/S0022377824001107

    work page doi:10.1017/s0022377824001107 2025

  55. [56]

    TRANSP documentation page

    Princeton Plasma Physics Laboratory.Guide to Running TRANSP/T3D/GX Coupled Sim- ulations. TRANSP documentation page. 2026.url:https://transp.pppl.gov/modules/ t3d.html. 34

    work page 2026