February 2017   |   Special Report

Cancer uncertainties

Models based on advanced statistical theory and machine-learning algorithms aim to predict a disease’s trajectory.

Exascale Science
January 2017

Upscale computing

National labs lead the push for operating systems that let applications run at exascale.

December 2016

Molecular arrangements

A University of Michigan team uses high-performance computing at Oak Ridge to predict how crystals form.

December 2016

Bake and shake

A Princeton-led team uses earthquakes and Oak Ridge’s Titan supercomputer to map the heat engine called Earth.

Science Highlights

June 2016

Predicting turbulence in fusion plasmas

Data from experiments and advanced codes combine with supercomputing muscle to help explain a half-century-old mystery.

Developing practical fusion energy has been impeded for decades by high heat loss from magnetically confined plasmas. Researchers from MIT, University of California at San Diego and General Atomics captured the dynamics of plasma turbulence linked to heat loss on unprecedented scales. Pictured here is the inside of the Alcator C-Mod tokamak, the inset depicting plasma turbulence simulations that show long wavelength blobs coexisting with short wavelength streamers – small, finger-like structures that comprise the turbulence in the core of the experimental plasmas.

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