Self-consistent core-pedestal transport simulations with neural network accelerated models

Meneghini, Orso; Smith, Sterling P.; Snyder, Philip B.; Staebler, Gary M.; Candy, Jeffrey; Belli, E.; Lao, Lang L.; Kostuk, Mark; Luce, Timothy C.; Luda, Teobaldo; Park, Jin Myung; Poli, F.

doi:10.1088/1741-4326/aa7776

Title: Self-consistent core-pedestal transport simulations with neural network accelerated models

Journal Article · Wed Jul 12 00:00:00 EDT 2017 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/aa7776· OSTI ID:1374389

Meneghini, Orso ^[1]; Smith, Sterling P. ^[1]; Snyder, Philip B. ^[1]; Staebler, Gary M. ^[1]; Candy, Jeffrey ^[1]; Belli, E. ^[1]; Lao, Lang L. ^[1]; Kostuk, Mark ^[1]; Luce, Timothy C. ^[1]; Luda, Teobaldo ^[2]; Park, Jin Myung ^[3];

^[4]

General Atomics, San Diego, CA (United States)
Politecnico di Torino, Torino (Italy)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

Fusion whole device modeling simulations require comprehensive models that are simultaneously physically accurate, fast, robust, and predictive. In this paper we describe the development of two neural-network (NN) based models as a means to perform a snon-linear multivariate regression of theory-based models for the core turbulent transport fluxes, and the pedestal structure. Specifically, we find that a NN-based approach can be used to consistently reproduce the results of the TGLF and EPED1 theory-based models over a broad range of plasma regimes, and with a computational speedup of several orders of magnitudes. These models are then integrated into a predictive workflow that allows prediction with self-consistent core-pedestal coupling of the kinetic profiles within the last closed flux surface of the plasma. Finally, the NN paradigm is capable of breaking the speed-accuracy trade-off that is expected of traditional numerical physics models, and can provide the missing link towards self-consistent coupled core-pedestal whole device modeling simulations that are physically accurate and yet take only seconds to run.

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Research Organization:: General Atomics, San Diego, CA (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: FG02-95ER54309

OSTI ID:: 1374389

Journal Information:: Nuclear Fusion, Vol. 57, Issue 8; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 58 works

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