Orchestrating TRANSP Simulations for Interpretative and Predictive Tokamak Modeling with OMFIT

Abstract

TRANSP simulations are being used in the OMFIT workflow manager to enable a machine-independent means of experimental analysis, postdictive validation, and predictive time-dependent simulations on the DIII-D, NSTX, JET, and C-MOD tokamaks. The procedures for preparing input data from plasma profile diagnostics and equilibrium reconstruction, as well as processing of the time-dependent heating and current drive sources and assumptions about the neutral recycling, vary across machines, but are streamlined by using a common workflow manager. Settings for TRANSP simulation fidelity are incorporated into the OMFIT framework, contrasting between-shot analysis, power balance, and fast-particle simulations. A previously established series of data consistency metrics are computed such as comparison of experimental versus calculated neutron rate, equilibrium stored energy versus total stored energy from profile and fast-ion pressure, and experimental versus computed surface loop voltage. Discrepancies between data consistency metrics can indicate errors in input quantities such as electron density profile or , or indicate anomalous fast-particle transport. Measures to assess the sensitivity of the verification metrics to input quantities are provided by OMFIT, including scans of the input profiles and standardized postprocessing visualizations. For predictive simulations, TRANSP uses GLF23 or TGLF to predict core plasma profiles, with user-defined boundary conditions in the outer region of the plasma. International Tokamak Physics Activity (ITPA) validation metrics are provided in postprocessing to assess the transport model validity. By using OMFIT to orchestrate the steps for experimental data preparation, selection of operating mode, submission, postprocessing, and visualization, we have streamlined and standardized the usage of TRANSP.

Keywords:

• TRANSP
• integrated simulations
• plasma transport

Acknowledgments

This material is based upon work supported by the DOE, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility under awards DE-AC02-09CH11466 and DE-FC02-04ER54698. The DIII-D data shown in this paper can be obtained in digital format by following the links at https://fusion.gat.com/global/D3D_DMP. B. A. Grierson gratefully acknowledges discussions with R. Andre, C. Bourdelle, C. Greenfield, R. J. Hawryluk, W. W. Heidbrink, R. M. Nazikian, C. C. Petty, W. M. Solomon, G. M. Staebler, M. A. Van Zeeland, and Y. Zhu.

Notes

^a https://science.energy.gov/fes/community-resources/workshop-reports/.

^b http://w3.pppl.gov/pshare/help/transp.htm.