Norman fusion device reaches ‘long enough’ milestone

WNN 09 February 2018

TAE Technologies, the California, USA-based fusion energy technology company, has announced that its proprietary beam-driven field-reversed configuration (FRC) plasma generator has set a new company record for plasma temperature. After more than 4000 experiments to date, the generator ‘Norman’ has now exceeded the capabilities and performance of the company’s previous FRC plasma generator, C-2U.

The Norman fusion device (Image: TAE Technologies)

Norman – a $100 million National Laboratory-scale device named after company founder Norman Rostoke – was unveiled in May last year and reached first plasma in June.

For a successful fusion reaction, plasma must be hot enough to enable forceful enough collisions to cause fusion and sustain itself long enough to harness the power at will. These are known as the ‘hot enough’ and ‘long enough’ milestone. TAE said it had proved the ‘long enough’ component in 2015, after more than 100,000 experiments. A year later, the company began building Norman, its fifth-generation device, to further test plasma temperature increases in pursuit of ‘hot enough’.

“This announcement is an important milestone on our quest to deliver world-changing clean fusion energy to help combat climate change and improve the quality of life for people globally,” TAE President and CTO Michl Binderbauer said in the 6 February statement.

TAE Technologies’ approach to fusion combines advanced accelerator and plasma physics, and uses abundant, non-radioactive hydrogenboron (pB-11) as a fuel source. The proprietary magnetic beam-driven FRC technology injects high-energy hydrogen atoms into the plasma to make the system more stable and better confined. This solution is compact and energy efficient, TAE says, “yielding a practical commercial power plant that is economically competitive with other energy technologies and provides continuous baseload power generation”.

TAE says it has a longstanding collaboration with Google to apply machine learning to advance plasma physics.