Fusion energy involves bringing together two lighter nuclei to create a heavier one. The energy released during that process can be captured and then used to power homes, industries and entire countries.
Fusion energy will be clean, abundant and safe.
Proxima Fusion is developing quasi-isodynamic (QI) stellarators, a magnetic confinement approach in which toroidal currents cancel out to zero, resulting in uniquely robust features. W7-X is an invaluable prototype and testbed for this concept.
The scientific basis for QI stellarators has been spearheaded by our partners at the Max Planck Institute for Plasma Physics (for example, Helander & Nührenberg, PPCF 2009 and Goodman, Xanthopoulos, Plunk et al., PRX Energy 2024).
We believe that QI stellarators offer the clearest path to putting fusion on the grid.
In the absence of toroidal plasma currents, current-driven instabilities can be completely eliminated, together with the risk of disruptions that can occur in tokamaks and other stellarator concepts.
QI stellarators also offer a proven heat exhaust concept: the island divertor, which was demonstrated on W7-AS and W7-X at the Max Planck Institute for Plasma Physics.
Superconducting materials have zero electrical resistance. They have been revolutionizing magnet technology for decades.
Conventional superconductors require extremely low temperatures, near absolute zero. Modern “High-Temperature Superconductors” (HTS) can reach higher temperatures and magnetic field strengths while also having significantly wider design space, leading to less strict requirements.
HTS technology is at the forefront of leading tokamak developments. It is equally attractive for stellarators, where we are less constrained by current-driven operational limits.
It’s too expensive to build a thousand power plants and hope that one of them works.
Computational optimization is central to modern stellarators. We leverage advanced techniques to iterate faster on our designs, before building them in the real world.
We develop reduced models, using Physics-Informed Neural Networks and other machine learning techniques, to filter through potential design hypotheses and construct surrogate models for testing.
We consider stellarator optimization to be a cross-disciplinary physics, computational and engineering challenge, requiring the assessment of many scientific and technological trade-offs.
Stellarators and Tokamaks differ in their tradeoffs of design vs. operational complexity: stellarators, whilst harder to design, are easier to operate.
Stellarators can be designed to run stably in continuous operation.
Stellarators have far more degrees of freedom, meaning that we can optimize them in remarkable ways, whereas tokamak designs are relatively unflexible.
Tokamaks have so far been at the forefront of fusion science. However, stellarators are now feasible to design and build, leading us into a new era.