Data-driven profile prediction for DIII-D

Abbate, Joseph; Conlin, R.; Kolemen, E.

doi:10.1088/1741-4326/abe08d

Data-driven profile prediction for DIII-D

Journal Article · Fri Mar 19 00:00:00 UTC 2021 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/abe08d· OSTI ID:1779436

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Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States); General Atomics, Energy & Advanced Concepts, DIII-D
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States)

A new, fully data-driven algorithm has been developed that uses neural networks to predict plasma pro les on a scale of τ_E into the future given actuators and the present plasma state. The model was trained and tested on DIII-D data from the 2013-2018 experimental campaigns. The model is accurate on average, with q predictions the worst and pressure predictions the best. The model can run in milliseconds and is very simple to use. This makes it a potentially useful tool for operators and physicists when planning plasma scenarios. It also is a candidate for doing phase-space exploration without going through the DIIID database or complicated and computationally expensive simulation codes. Here, a reduced model using only realtime diagnostics has also been developed and formed the basis for a model-predictive control algorithm implemented and successfully tested on DIII-D.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC02-09CH11466; AR0001166; FC02-04ER54698; SC0015480; SC0015878

OSTI ID:: 1779436

Alternate ID(s):: OSTI ID: 23129696

Journal Information:: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 4 Vol. 61; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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70 PLASMA PHYSICS AND FUSION TECHNOLOGY
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machine learning
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transport

Data-driven profile prediction for DIII-D

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