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arxiv: 2605.23121 · v1 · pith:PXETRQXCnew · submitted 2026-05-22 · ⚛️ physics.plasm-ph · physics.comp-ph

NIMROD-to-IMAS workflow for extended-magnetohydrodynamic data with reusable datasets and implications for IMAS schema development

Pith reviewed 2026-05-25 02:58 UTC · model grok-4.3

classification ⚛️ physics.plasm-ph physics.comp-ph
keywords NIMRODIMASextended MHDtokamak simulationdata conversionworkflowDIII-Dedge harmonic oscillations
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The pith

A workflow converts NIMROD extended-MHD simulation data to IMAS v4 records while conserving equilibrium, profile, perturbation and grid quantities.

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

The paper presents a workflow that takes NIMROD code inputs and hierarchical HDF5 outputs and produces records compatible with version 4 of the ITER IMAS Data Dictionary. The workflow applies COCOS-consistent coordinate handling and encodes finite-element poloidal meshes plus toroidal Fourier components through the IMAS General Grid Description. Validation on the DIII-D 163518 edge harmonic oscillation simulation shows that essential equilibrium, profile, perturbation and grid data are preserved in the resulting records. The work also identifies gaps in the current IMAS framework that must be addressed to support extended MHD data at database scale, including needs for provenance and metadata.

Core claim

The NIMROD-to-IMAS workflow converts code-specific inputs and outputs into IMAS v4 records, conserving essential equilibrium, profile, perturbation, and grid data as demonstrated on the DIII-D 163518 edge harmonic oscillation case, while exposing gaps in the IMAS framework for extended MHD data.

What carries the argument

NIMROD-to-IMAS conversion workflow that applies COCOS-consistent coordinate treatment and IMAS General Grid Description to encode finite-element poloidal meshes and toroidal Fourier components from hierarchical HDF5 dumps.

Load-bearing premise

The IMAS General Grid Description and existing data dictionary structures, together with the COCOS-consistent treatment, are sufficient to represent NIMROD's finite-element poloidal meshes and toroidal Fourier components without loss of essential physics information.

What would settle it

A side-by-side comparison of the converted IMAS records against the original NIMROD data for the DIII-D 163518 case that reveals loss of perturbation amplitudes or grid fidelity would falsify the conservation claim.

Figures

Figures reproduced from arXiv: 2605.23121 by Alexei Y. Pankin, Andreas Kleiner, Eric Suchyta, Fatima Ebrahimi, Jacob King, Jesus Dominguez-Palacios, Norbert Podhorszki, Qian Gong.

Figure 1
Figure 1. Figure 1: Schematic illustration of the plasma re [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Schematic overview of the data flow from NIMROD simulations and experimental measurements [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Mapping linear and nonlinear NIMROD simulations to IMAS. In the linear case, [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of NIMROD poloidal mesh (left) and unstructured IMAS GGD mesh preserving FE [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Connectivity conventions for optional unstructured cell topologies: (a) [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: NIMROD-to-IMAS converter verification using contour reconstruction for an n = 1 tempera￾ture perturbation on the poloidal (R, Z) plane. Left: contour produced directly from the native NIMROD dump using nimpy. Right: contour produced by reading the corresponding field from IMAS using the NIMROD-to-IMAS plotting utility. Different default colormaps lead to minor background differ￾ences, while the perturbatio… view at source ↗
read the original abstract

Extended magnetohydrodynamic (MHD) simulations of tokamak plasmas regularly produce outputs in multi-dimensional, multiple-field formats; these code-specific formats make it difficult to do cross-code validation/coupling and analyze at a database scale. In this paper, a workflow that converts NIMROD code inputs and outputs to records compatible with version 4 of the ITER IMAS Data Dictionary is presented. The scope of the workflow includes preprocessing of NIMROD code inputs, conversion of hierarchical NIMROD code HDF5 dumps, COCOS-consistent treatment of the coordinate system and sign convention, and encoding finite-element poloidal meshes and toroidal Fourier components through IMAS General Grid Description. Furthermore, the workflow allows for provenance and integrity metadata to be included while providing optimal I/O operations for large array structures. An example conversion based on an NIMROD code simulation of edge harmonic oscillations performed for the DIII-D discharge 163518 [A.Y. Pankin, et al., Nuclear Fusion 60.9 (2020), p. 092004] is used to validate the conservation of essential equilibrium, profile, perturbation, and grid data in the resulting IMAS records. Finally, this implementation exposes gaps in the current IMAS framework that need to be addressed to accommodate extended MHD data, and it highlights the metadata, provenance, and governance needs of the downstream use cases in the form of dataset validation, integration, and machine learning.

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

1 major / 2 minor

Summary. The manuscript presents a workflow that converts NIMROD extended-MHD simulation inputs and outputs (including hierarchical HDF5 dumps) into ITER IMAS v4 Data Dictionary records. The workflow incorporates preprocessing steps, COCOS-consistent coordinate and sign-convention handling, encoding of finite-element poloidal meshes and toroidal Fourier components via the IMAS General Grid Description, and support for provenance/integrity metadata with optimized I/O. Validation is performed on a single NIMROD simulation of edge harmonic oscillations for DIII-D discharge 163518, demonstrating conservation of equilibrium, profile, perturbation, and grid quantities; the work also identifies gaps in the current IMAS schema for extended-MHD data.

Significance. If the described conversion steps prove robust and reusable, the workflow would directly support cross-code validation, database-scale analysis, and machine-learning applications in fusion plasma physics by producing standardized, provenance-tracked datasets. Explicit treatment of COCOS conventions and the General Grid Description for NIMROD's finite-element/Fourier representation is a concrete technical contribution. The identification of IMAS schema gaps provides actionable input for framework development.

major comments (1)
  1. [Validation / example conversion] Validation section (example conversion for DIII-D 163518): conservation of equilibrium, profiles, perturbations, and grids is asserted qualitatively, but no quantitative error metrics (e.g., relative L2 norms or pointwise differences on key quantities such as safety factor or magnetic field components) or results from additional test cases are reported. This limits the strength of the claim that the workflow conserves essential physics information without loss.
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction would benefit from an explicit statement of the IMAS version targeted (v4) and a brief comparison table of NIMROD vs. IMAS data structures for the main fields converted.
  2. [Figures] Figure captions describing the converted IMAS records should include the specific IMAS IDS names and GGD structures used (e.g., equilibrium, core_profiles, or perturbations).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment regarding the validation section. We address the point below and will revise the manuscript accordingly.

read point-by-point responses
  1. Referee: [Validation / example conversion] Validation section (example conversion for DIII-D 163518): conservation of equilibrium, profiles, perturbations, and grids is asserted qualitatively, but no quantitative error metrics (e.g., relative L2 norms or pointwise differences on key quantities such as safety factor or magnetic field components) or results from additional test cases are reported. This limits the strength of the claim that the workflow conserves essential physics information without loss.

    Authors: We agree that quantitative metrics would strengthen the validation claim. In the revised manuscript we will add relative L2 norms and maximum pointwise differences (on a common grid) for key quantities including the safety factor q, electron density and temperature profiles, and the three components of the magnetic field, comparing the original NIMROD data directly to the converted IMAS records. We note that the manuscript presents a single, representative EHO case (DIII-D 163518) chosen for its relevance to existing literature; while the workflow itself is general, performing and documenting additional test cases would require substantial extra simulation and analysis effort that lies outside the scope of the present work. We therefore plan a partial revision that incorporates the requested error metrics for the existing case. revision: partial

Circularity Check

0 steps flagged

No significant circularity; workflow paper with external validation

full rationale

This is a methods paper describing an explicit data-conversion workflow (preprocessing, HDF5 conversion, COCOS handling, IMAS General Grid Description encoding) with no mathematical derivations, fitted parameters, predictions, or self-referential claims. The validation uses an external simulation (DIII-D 163518 EHO from a 2020 Nuclear Fusion paper) as a concrete benchmark to check conservation of equilibrium, profiles, perturbations, and grids. No load-bearing self-citations, uniqueness theorems, or ansatzes reduce the central claim to its own inputs; the argument is self-contained against the described steps and the cited external dataset.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The workflow depends on pre-existing standards rather than new fitted quantities or postulated entities.

axioms (2)
  • domain assumption COCOS conventions correctly map NIMROD coordinate systems and sign conventions.
    The workflow description states COCOS-consistent treatment is applied.
  • domain assumption IMAS Data Dictionary v4 and General Grid Description can represent the required NIMROD data fields.
    Conversion target is defined as IMAS v4 records.

pith-pipeline@v0.9.0 · 5827 in / 1353 out tokens · 25145 ms · 2026-05-25T02:58:24.440568+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages

  1. [1]

    Nonlinear magnetohydrodynamics simulation using high-order finite elements

    C. R. Sovinec et al. “Nonlinear magnetohydrodynamics simulation using high-order finite elements”. In:Journal of Computational Physics195.1 (2004), pp. 355–386.doi:10.1016/j.jcp.2003.10.004

  2. [2]

    Multiple time-scale calculations of global plasmas with the M3D-C1 code

    S. C. Jardin et al. “Multiple time-scale calculations of global plasmas with the M3D-C1 code”. In: Computational Science & Discovery5.1 (2012), p. 014002.doi:10.1088/1749-4699/5/1/014002

  3. [3]

    Princeton University

    Princeton Plasma Physics Laboratory.The M3D-C1 User’s Guide. Princeton University. Princeton, NJ, n.d.url:https://m3dc1.pppl.gov/M3DC1_new.pdf

  4. [4]

    The JOREK non-linear extended MHD code and applications to large-scale insta- bilities and their control in magnetically confined fusion plasmas

    M. Hoelzl et al. “The JOREK non-linear extended MHD code and applications to large-scale insta- bilities and their control in magnetically confined fusion plasmas”. In:Nuclear Fusion61.6 (2021), p. 065001.doi:10.1088/1741-4326/abf99f

  5. [5]

    DIII-D National Fusion Facility

    General Atomics.The GATO Manual (Version 4). DIII-D National Fusion Facility. San Diego, CA, n.d.url:https://fusion.gat.com/THEORY/gato/Gato_manual_Version4.pdf

  6. [6]

    GATO: An ideal MHD stability code for a general magnetic configuration

    L. C. Bernard, F. J. Helton, and R. W. Moore. “GATO: An ideal MHD stability code for a general magnetic configuration”. In:Computer Physics Communications24.3-4 (1981), pp. 377–380.doi:10. 1016/0010-4655(81)90160-0

  7. [7]

    Reconstruction of current profile parameters and plasma shapes in tokamaks

    L. L. Lao et al. “Reconstruction of current profile parameters and plasma shapes in tokamaks”. In: Nuclear Fusion25.11 (1985), p. 1611.doi:10.1088/0029-5515/25/11/007

  8. [8]

    Design and first applications of the ITER integrated modelling & analysis suite

    F. Imbeaux, S. D. Pinches, J. B. Lister, et al. “Design and first applications of the ITER integrated modelling & analysis suite”. In:Nuclear Fusion55.12 (2015), p. 123006.doi:10.1088/0029-5515/ 55/12/123006

  9. [9]

    Integrated modeling applications for tokamak experiments with OMFIT

    O. Meneghini et al. “Integrated modeling applications for tokamak experiments with OMFIT”. In: Nuclear Fusion55.8 (2015), p. 083008.doi:10.1088/0029-5515/55/8/083008

  10. [10]

    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

  11. [11]

    Towards validated MHD modeling of edge harmonic oscillation in DIII-D QH-mode discharges

    A. Y. Pankin et al. “Towards validated MHD modeling of edge harmonic oscillation in DIII-D QH-mode discharges”. In:Nuclear Fusion60.9 (2020), p. 092004.doi:10.1088/1741-4326/ab9afe

  12. [12]

    The FAIR Guiding Principles for scientific data management and steward- ship

    Mark D. Wilkinson et al. “The FAIR Guiding Principles for scientific data management and steward- ship”. In:Scientific Data3 (2016), p. 160018.doi:10.1038/sdata.2016.18

  13. [13]

    Pankin et al.An IMAS representation of NIMROD magnetohydrodynamic data for DIII-D discharge 163518

    Alexei Y. Pankin et al.An IMAS representation of NIMROD magnetohydrodynamic data for DIII-D discharge 163518. Manuscript submitted to Scientific Data. 2026

  14. [14]

    Studies of Plasma Equilibrium and Transport in a Tokamak Fu- sion Device with the Inverse-Variable Technique

    R.R. Khayrutdinov and V.E. Lukash. “Studies of Plasma Equilibrium and Transport in a Tokamak Fu- sion Device with the Inverse-Variable Technique”. In:Journal of Computational Physics109.2 (1993), pp. 193–201.issn: 0021-9991.doi:https : / / doi . org / 10 . 1006 / jcph . 1993 . 1211.url:https : //www.sciencedirect.com/science/article/pii/S0021999183712118

  15. [15]

    Optimising the ITER 15MA DT Baseline Scenario by Exploiting a Self-Consistent Free-Boundary Core-Edge-SOL Workflow in IMAS

    F. Koechl et al. “Optimising the ITER 15MA DT Baseline Scenario by Exploiting a Self-Consistent Free-Boundary Core-Edge-SOL Workflow in IMAS”. In:27th IAEA Fusion Energy Conference (FEC 2018). IAEA. Gandhinagar, India, 2018, EX/P7–25.url:https : / / scientific - publications . ukaea.uk/wp-content/uploads/FEC2018_PAPER_EX_P7-25_DINA-JINTRAC-IMAS_V1.PDF. 37

  16. [16]

    Expediting Higher Fidelity Plasma State Reconstructions for the DIII-D Na- tional Fusion Facility Using Leadership Class Computing Resources

    Sterling Smith et al. “Expediting Higher Fidelity Plasma State Reconstructions for the DIII-D Na- tional Fusion Facility Using Leadership Class Computing Resources”. In:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2024, pp. 2118–2126.doi:10.1109/SCW63240.2024.00265.url:https://conferen...

  17. [17]

    Figueroa

    L L Lao et al. “Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction”. In:Plasma Physics and Controlled Fusion64.7 (2022), p. 074001.doi:10.1088/1361- 6587/ac6fff.url:https://doi.org/10.1088/1361-6587/ac6fff

  18. [18]

    Bechtel et al.Production of a FAIR Tokamak Equilibria Database for EFIT-AI

    T. Bechtel et al.Production of a FAIR Tokamak Equilibria Database for EFIT-AI. Bulletin of the American Physical Society, APS Division of Plasma Physics Meeting (abstract JO9012B). 2023.url: https://ui.adsabs.harvard.edu/abs/2023APS..DPPJO9012B/abstract

  19. [19]

    Optimization of computational MHD normal-mode analysis for tokamaks

    AB Mikhailovskii et al. “Optimization of computational MHD normal-mode analysis for tokamaks”. In:Plasma Physics Reports23 (1997)

  20. [20]

    The initial step towards JOREK integration in IMAS

    D. Penko et al. “The initial step towards JOREK integration in IMAS”. In:Proceedings of the In- ternational Conference Nuclear Energy for New Europe (NENE 2019). Paper 707. 2019.url:https: //arhiv.djs.si/nene2019/proceedings-html/PDF/NENE2019_707.pdf

  21. [21]

    Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities

    O. Meneghini et al. “Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities”. In:27th IAEA Fusion Energy Conference (FEC 2018). Preprint 0012. Gand- hinagar, India, 2018, TH/P6–16.url:https : / / nucleus - new . iaea . org / sites / fusionportal / Shared%20Documents/FEC%202018/fec2018-preprints/preprint0012.pdf

  22. [22]

    Smith, et al.Interfacing OMFIT with ITER IMAS via OMAS

    Orso Meneghini, Sterling P. Smith, et al.Interfacing OMFIT with ITER IMAS via OMAS. 3rd IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis. Presentation Slides. Vienna, Austria, 2019.url:https : / / nucleus . iaea . org / sites / fusionportal / Shared % 20Documents / TM % 20on % 20Fusion % 20Data % 20Processing % 20Validation % 20an...

  23. [23]

    Theory-based model for the pedestal, edge stability and ELMs in tokamaks

    A.Y. Pankin et al. “Theory-based model for the pedestal, edge stability and ELMs in tokamaks”. In: Nuclear Fusion46.4 (2006), p. 403.doi:10.1088/0029-5515/46/4/001.url:https://doi.org/10. 1088/0029-5515/46/4/001

  24. [24]

    Effects beyond ideal MHD on stability of wide and enhanced pedestal regimes in NSTX

    Alexei Pankin et al. “Effects beyond ideal MHD on stability of wide and enhanced pedestal regimes in NSTX”. In:Plasma Phys. Control. Fusion67.9 (2025), p. 095023.doi:10.1088/1361-6587/ae049c. url:https://doi.org/10.1088/1361-6587/ae049c

  25. [25]

    MHD modeling of a DIII-D low-torque QH-mode discharge and comparison to observations

    J. R. King et al. “MHD modeling of a DIII-D low-torque QH-mode discharge and comparison to observations”. In:Physics of Plasmas24.5 (2017), p. 055902.doi:10.1063/1.4977467

  26. [26]

    Linear simulations of a wide pedestal quiescent H-mode plasma with the extended-MHD code NIMROD

    J. J. Dominguez-Palacios Duran et al. “Linear simulations of a wide pedestal quiescent H-mode plasma with the extended-MHD code NIMROD”. In:Nuclear Fusion66.3 (2026), p. 036003.doi:10.1088/ 1741-4326/ae3844

  27. [27]

    Tokamak coordinate conventions: COCOS

    O. Sauter and S. Yu. Medvedev. “Tokamak coordinate conventions: COCOS”. In:Computer Physics Communications184.2 (2013), pp. 293–302.doi:10.1016/j.cpc.2012.09.010

  28. [28]

    Sparsified time-dependent Fourier neural operators for fusion simula- tions

    Mustafa Mutiur Rahman et al. “Sparsified time-dependent Fourier neural operators for fusion simula- tions”. In:Physics of Plasmas31.12 (Dec. 2024), p. 123902

  29. [29]

    Gmsh: a three-dimensional finite element mesh generator with built-in pre- and post-processing facilities

    Christophe Geuzaine and Jean-Fran¸ cois Remacle. “Gmsh: a three-dimensional finite element mesh generator with built-in pre- and post-processing facilities”. In:International Journal for Numerical Methods in Engineering79.11 (2009), pp. 1309–1331

  30. [30]

    Harris, K

    Charles R. Harris et al. “Array programming with NumPy”. In:Nature585.7825 (2020), pp. 357–362. doi:10.1038/s41586-020-2649-2

  31. [31]

    Thompson, Z

    Joe F. Thompson, Z. U. A. Warsi, and C. Wayne Mastin.Numerical Grid Generation: Foundations and Applications. New York: North-Holland, 1985

  32. [32]

    Frey and Paul-Louis George.Mesh Generation: Application to Finite Elements

    Pascal J. Frey and Paul-Louis George.Mesh Generation: Application to Finite Elements. Chichester: Wiley, 2000. 38

  33. [33]

    Paris: Herm` es, 1998

    Paul-Louis George and Houman Borouchaki.Delaunay Triangulation and Meshing: Application to Finite Elements. Paris: Herm` es, 1998

  34. [34]

    Delaunay Refinement Algorithms for Triangular Mesh Generation

    Jonathan Richard Shewchuk. “Delaunay Refinement Algorithms for Triangular Mesh Generation”. In: Computational Geometry22.1–3 (2002), pp. 21–74.doi:10.1016/S0925-7721(01)00047-5

  35. [35]

    Thomas J. R. Hughes.The Finite Element Method: Linear Static and Dynamic Finite Element Anal- ysis. Mineola, NY: Dover Publications, 2000

  36. [36]

    Presentation at the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis

    Mireille Schneider.Development of Synthetic Diagnostics for ITER. Presentation at the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis. 3 December 2021. ITER Or- ganization, Dec. 2021.url:https://conferences.iaea.org/event/251/contributions/20707/ attachments/11091/16423/Schneider%20- %20211203%20- %20IAEA%20TM%20FDPVA%20Decem...

  37. [37]

    Pinches.ITER Integrated Modelling Programme

    Simon D. Pinches.ITER Integrated Modelling Programme. Presentation at the 14th ITER International School on Integrated Modelling. 30 June 2025. ITER Organization, June 2025.url:https://www. iter.org/sites/default/files/media/2025-07/i-2_pinches.pdf

  38. [38]

    HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

    Norbert Podhorszki et al. “HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP”. In:Proceedings of the 2025 Supercomputing Asia Conference. 2025, pp. 91–95. [39]Campaign Management Toolkit.https : / / adios2 . readthedocs . io / en / v2 . 11 . 0 / advanced / campaign_management.html

  39. [39]

    Adios 2: The adaptable input output system. a framework for high-performance data management

    William F Godoy et al. “Adios 2: The adaptable input output system. a framework for high-performance data management”. In:SoftwareX12 (2020), p. 100561

  40. [40]

    Model Cards for Model Reporting,

    Margaret Mitchell et al. “Model Cards for Model Reporting”. In:FAT* ’19: Proceedings of the Confer- ence on Fairness, Accountability, and Transparency (Atlanta, GA, USA). New York, NY, USA: Associ- ation for Computing Machinery, 2019, 220–229.isbn: 9781450361255.doi:10.1145/3287560.3287596. url:https://doi.org/10.1145/3287560.3287596

  41. [41]

    Datasheets for datasets

    Timnit Gebru et al. “Datasheets for datasets”. In:Commun. ACM64.12 (Nov. 2021), 86–92.issn: 0001-0782.doi:10.1145/3458723.url:https://doi.org/10.1145/3458723. 39