New Discovery May Explain the Heat Loss in Fusion Reactors
The lack of understanding when it comes to how the gas in a fusion reactor will cope with the intense level of pressure and heat needed to force atoms to stick together.
The lack of understanding when it comes to how the gas in a fusion reactor will cope with the intense level of pressure and heat needed to force atoms to stick together. The high temperature is the key to fusion, and it must be strong enough to stop the resistance of atoms to stick each other. Overcoming turbulence is vital, but first it must be determined how this works. Until this is sorted, it will not be possible to predict the performance of new reactors e.g. ITER being built in France.
Research between MIT and General Atomics, University of California and Princeton Plasma Physics Lab claims to have the surprising answer. Slight turbulence and high turbulence – 60 times greater – explains the difference between results and theory.
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These findings debunk the views that ions caused more turbulence than electrons and the eddies that survived would be so small as to have no effect and both types need to exist. A problem arose as it seemed it would take 3,000 years of computer time to prove but with multiple computers this was reduced.
The results could not be doubted, small eddies can withstand a temperature of 100 million degrees and will influence the amount of fusion occurring in a reactor. These findings are helping produce streamlined simulation for a regular laptop. Further research from MIT graduate Ruiz Ruiz supports the findings, and this is, by using a different type of reactor.
General Atomics researcher Gary Staebler considers both papers analysis to be first class and very significant.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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