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DTSTART;TZID=America/Chicago:20181114T133000
DTEND;TZID=America/Chicago:20181114T141500
UID:submissions.supercomputing.org_SC18_sess481_pec263@linklings.com
SUMMARY:Data Fusion for Nuclear Fusion – Using HPC To Put a Star in a Bott
 le
DESCRIPTION:HPC Impact Showcase\nWorkshop Reg Pass, Tutorial Reg Pass, Tec
 h Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only Reg Pa
 ss, Industry\n\nData Fusion for Nuclear Fusion – Using HPC To Put a Star i
 n a Bottle\n\nDettrick\n\nFusion energy offers the prospect of a carbon-ne
 utral, environmentally responsible, and inexhaustible energy source.  TAE 
 Technologies Inc. is trying to greatly accelerate fusion research to devel
 op the world’s first commercially viable fusion-powered generator for elec
 tricity production. \n\nTo this end, TAE has invested $100 million of capi
 tal expenditure in the construction of its latest magnetically confined fu
 sion experiment, “Norman,” an advanced beam-driven field-reversed configur
 ation (FRC) plasma device.  A central challenge of understanding the physi
 cs in a fusion plasma experiment is that most of the experimental diagnost
 ics are indirect in nature and require inverse problems, such as tomograph
 ic inversion, and that there are many interacting degrees of freedom each 
 of which requires its own diagnostic and inversion process.  To understand
  the “what” of the complete plasma state measured in this way requires dat
 a science, in particular, sensor fusion and Bayesian Inference.  To unders
 tand the “why” of the plasma state requires theory and advanced computatio
 n, which is made challenging by multiple time and space scales and multi-p
 hysics interactions.  The central problem of keeping the plasma hot enough
  for long enough to achieve fusion cannot be addressed without using HPC t
 o understand non-linear wave-particle interactions, which can bring both g
 reat benefit in terms of kinetic stabilization of modes at the macroscale,
  and detriments in the form of heat losses due to kinetic microturbulence.
  \n\nAnalysis of existing experiments, and prediction of performance in ne
 w parameter regimes, requires a fusion of the “what” and the “why” with a 
 combination of data science and HPC modeling.  TAE is partnering with data
  science heavyweights – Google and others – and big iron heavyweights such
  as the Department of Energy Leadership Computing Facilities – to bring th
 e most advanced data science algorithms and the fastest computers to bear 
 on these problems.
URL:https://sc18.supercomputing.org/presentation/?id=pec263&sess=sess481
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