Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International

Wikipedia Affiliate Button

Home > Press > Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Fast-camera photo of a plasma produced by the first NSTX-U operations campaign.

CREDIT
NSTX-U experiment
Fast-camera photo of a plasma produced by the first NSTX-U operations campaign. CREDIT NSTX-U experiment

Abstract:
Machine learning (ML), a form of artificial intelligence that recognizes faces, understands language and navigates self-driving cars, can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma -- the state of matter composed of free electrons and atomic nuclei, or ions -- that fuels fusion reactions.

Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth

Princeton, NJ | Posted on May 17th, 2019

The sun and most stars are giant balls of plasma that undergo constant fusion reactions. Here on Earth, scientists must heat and control the plasma to cause the particles to fuse and release their energy. PPPL research shows that ML can facilitate such control.

Neural Networks

Researchers led by PPPL physicist Dan Boyer have trained neural networks -- the core of ML software -- on data produced in the first operational campaign of the National Spherical Torus Experiment-Upgrade (NSTX-U), the flagship fusion facility, or tokamak, at PPPL. The trained model accurately reproduces predictions of the behavior of the energetic particles produced by powerful neutral beam injection (NBI) that is used to fuel NSTX-U plasmas and heat them to million-degree, fusion-relevant temperatures.

These predictions are normally generated by a complex computer code called NUBEAM, which incorporates information about the impact of the beam on the plasma. Such complex calculations must be made hundreds of times per second to analyze the behavior of the plasma during an experiment. But each calculation can take several minutes to run, making the results available to physicists only after an experiment that typically lasts a few seconds is completed.

The new ML software reduces the time needed to accurately predict the behavior of energetic particles to under 150 microseconds -- enabling the calculations to be done online during the experiment.

Initial application of the model demonstrated a technique for estimating characteristics of the plasma behavior not directly measured. This technique combines ML predictions with the limited measurements of plasma conditions available in real-time. The combined results will help the real-time plasma control system make more informed decisions about how to adjust beam injection to optimize performance and maintain stability of the plasma -- a critical quality for fusion reactions.

Rapid evaluations

The rapid evaluations will also help operators make better-informed adjustments between experiments that are executed every 15-20 minutes during operations. "Accelerated modeling capabilities could show operators how to adjust NBI settings to improve the next experiment," said Boyer, lead author of a paper in Nuclear Fusion that reports the new model.

Boyer, working with PPPL physicist Stan Kaye, generated a database of NUBEAM calculations for a range of plasma conditions similar to those achieved in experiments during the initial NSTX-U run. Researchers used the database to train a neural network to predict effects of neutral beams on the plasma, such as heating and profiles of the current. Software engineer Keith Erickson then implemented software for evaluating the model on computers used to actively control the experiment to test the calculation time.

New work will include development of neural network models tailored to the planned conditions of future NSTX-U campaigns and other fusion facilities. In addition, researchers plan to expand the present modeling approach to enable accelerated predictions of other fusion plasma phenomena. Support for this work comes from the DOE Office of Science.

####

About Princeton Plasma Physics Laboratory
PPPL, on Princeton University's Forrestal Campus in Plainsboro, N.J., is devoted to creating new knowledge about the physics of plasmas -- ultra-hot, charged gases -- and to developing practical solutions for the creation of fusion energy. The Laboratory is managed by the University for the U.S. Department of Energy's Office of Science, which is the largest single supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

For more information, please click here

Contacts:
John Greenwald

609-243-2672

Copyright © Princeton Plasma Physics Laboratory

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

RELATED JOURNAL ARTICLE:

Related News Press

News and information

'Hot spots' increase efficiency of solar desalination: Rice University engineers boost output of solar desalination system by 50% June 19th, 2019

New record: 3D-printed optical-electronic integration June 18th, 2019

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

Small currents for big gains in spintronics: A new low-power magnetic switching component could aid spintronic devices June 14th, 2019

Physics

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

Mysterious Majorana quasiparticle is now closer to being controlled for quantum computing: Princeton researchers detect a robust Majorana quasiparticle and show how it can be turned on and off June 14th, 2019

Breaking the symmetry in the quantum realm May 31st, 2019

Laboratories

2D crystals conforming to 3D curves create strain for engineering quantum devices June 7th, 2019

Quantum information gets a boost from thin-film breakthrough: Method opens new path to all-optical quantum computers, other technologies May 31st, 2019

NIST physicists 'teleport' logic operation between separated ions May 30th, 2019

Govt.-Legislation/Regulation/Funding/Policy

'Hot spots' increase efficiency of solar desalination: Rice University engineers boost output of solar desalination system by 50% June 19th, 2019

New record: 3D-printed optical-electronic integration June 18th, 2019

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

Mysterious Majorana quasiparticle is now closer to being controlled for quantum computing: Princeton researchers detect a robust Majorana quasiparticle and show how it can be turned on and off June 14th, 2019

Possible Futures

New record: 3D-printed optical-electronic integration June 18th, 2019

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

Mysterious Majorana quasiparticle is now closer to being controlled for quantum computing: Princeton researchers detect a robust Majorana quasiparticle and show how it can be turned on and off June 14th, 2019

University of Konstanz researchers create uniform-shape polymer nanocrystals: Researchers from the University of Konstanz's CRC 1214 'Anisotropic Particles as Building Blocks: Tailoring Shape, Interactions and Structures' generate uniform-shape nanocrystals using direct polymeriz June 14th, 2019

Announcements

'Hot spots' increase efficiency of solar desalination: Rice University engineers boost output of solar desalination system by 50% June 19th, 2019

New record: 3D-printed optical-electronic integration June 18th, 2019

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

Small currents for big gains in spintronics: A new low-power magnetic switching component could aid spintronic devices June 14th, 2019

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers

'Hot spots' increase efficiency of solar desalination: Rice University engineers boost output of solar desalination system by 50% June 19th, 2019

New record: 3D-printed optical-electronic integration June 18th, 2019

Can break junction techniques still offer quantitative information at single-molecule level June 18th, 2019

University of Konstanz researchers create uniform-shape polymer nanocrystals: Researchers from the University of Konstanz's CRC 1214 'Anisotropic Particles as Building Blocks: Tailoring Shape, Interactions and Structures' generate uniform-shape nanocrystals using direct polymeriz June 14th, 2019

Energy

'Hot spots' increase efficiency of solar desalination: Rice University engineers boost output of solar desalination system by 50% June 19th, 2019

UCI scientists create new class of two-dimensional materials: Fabrication could help unlock new quantum computing and energy technologies June 6th, 2019

Quantum rebar: Quantum dots enhance stability of solar-harvesting perovskite crystals: Researchers demonstrate that perovskite crystals and quantum dots working together can increase stability of solar materials May 24th, 2019

Big energy savings for tiny machines May 24th, 2019

Artificial Intelligence

New Video Highlights Specific Topics Sought in Call for Papers for the 2019 IEEE International Electron Devices Meeting (IEDM) June 13th, 2019

Analog Bits and GLOBALFOUNDRIES Deliver Differentiated Analog and Mixed Signal IP for High-Performance Mobile and Compute Applications: Analog Bits’ Analog and Mixed Signal IPs Including Various PLLs, PCIe Reference Clock, Sensors and Power Circuits with GLOBALFOUNDRIES 12nm Fin June 5th, 2019

CEA-Leti & Stanford Target Edge-AI Apps with Breakthrough Memory Cell: Paper at ISSCC 2019 Presents Proof-of-Concept Multi-Bit Chip That Overcomes NVM’s Read/Write, Latency and Integration Challenges February 20th, 2019

Using artificial intelligence to engineer materials' properties: New system of 'strain engineering' can change a material's optical, electrical, and thermal properties February 11th, 2019

NanoNews-Digest
The latest news from around the world, FREE



  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project