A little bit of fuel, a much bigger fire: The Department of Energy’s Fusion Energy Sciences program has awarded a second round of funding to a spin-polarized fusion (SPF) project centered on the DIII-D tokamak.
The basis of SPF is the idea that polarized fusion fuel, where the isotopes’ spin is aligned parallel to the magnetic field, has a higher likelihood of the collisions that lead to fusion than unpolarized fuel. Theoretically, this method has the potential to boost the system’s energy output by 70 percent–80 percent.
“The ultimate goal is being able to harvest energy using the minimum amount of material,” said Xiangdong Wei, a physicist at Thomas Jefferson National Accelerator Facility who is helping lead the study. “With the right alignment, a little bit of fuel can produce a much bigger fire, and you can use that energy for the next round of fusion.”
This idea has been around since the 1980s, but it has been technically complicated to develop a system that sufficiently polarizes the fuel and injects it in such a way where it stays polarized for long enough to increase the fusion cross section, boosting the number of reactions per time at a given density.
The team has already designed equipment that can polarize, store, and inject deuterium and helium-3 into the DIII-D tokamak. Now they are beginning phase II, aiming to prototype and construct the devices.
Seeing the plasma: As recently published in Review of Scientific Instruments, researchers have developed a multichannel X-ray imager that measures fusion-plasma temperatures with a large field of view and a high resolution.
Called the toroidal X-ray imager, the device works by using aperiodic multilayer coatings with different bandwidths on the mirrors in its detectors. These nanoscale layers are optimized to capture three distinct X-ray energy bands centered at 8.7, 13, and 17.5 keV for determining the plasma temperature.
According to the American Institute of Physics, the device is the first diagnostic tool capable of measuring all three energy bands at a single location. It will be headed to NIF for inertial confinement fusion experiments.
AI design neutralizes effect of tiny material defects: Inertial confinement fusion can be impacted by Richtmyer-Meshkov (RM) instabilities, resulting in unstable jetting at points of tiny material defects when the fuel fill tube targets are hit.
As described in a Physical Review Letters paper, researchers used a machine-learning design optimization algorithm to determine where to add microscopic voids to a gelatine, which dispersed energy from a shock wave before it could reach the interface that typically produces jets.
The idea would need to be extended to the design of spherical fill tubes to be applied to fusion, but the concept could also be used across a range of other materials research applications.
“The challenge is that while these designs look promising in simulations, they are often extremely difficult to manufacture and experimentally test,” said Jergus Strucka, an instrument scientist at European XFEL and one of the study authors. “Our work is one of the first demonstrations that such AI-optimized structures can actually be built and studied in real experiments.”
