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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Nuclear Science and Engineering
September 2025
Nuclear Technology
Fusion Science and Technology
August 2025
Latest News
Startup looks to commercialize inertial fusion energy
Another startup hoping to capitalize on progress the Department of Energy’s Lawrence Livermore National Laboratory has made in realizing inertial fusion energy has been launched. On August 27, San Francisco–based Inertia Enterprises, a private fusion power start-up, announced the formation of the company with the goal of commercializing fusion energy.
Akash Tiwari, Shilan Jin, Shashank Galla, Bhaskar Botcha, Sean Hayes, Monika Biener, Kshitij Bhardwaj, Satish Bukkapatnam, Yu Ding, Alexos Antonios, Pierre Baldi, Suhas Bhandarkar
Fusion Science and Technology | Volume 81 | Number 3 | April 2025 | Pages 219-231
Research Article | doi.org/10.1080/15361055.2024.2385224
Articles are hosted by Taylor and Francis Online.
In inertial confinement fusion (ICF) experiments seeking output gains of unity and beyond, the quality of the ablator capsule is paramount for minimizing the hydrodynamic mix that quenches the central hot spot. Defects in the form of foreign particles or missing mass on the surface and within the wall of the capsule are primary offenders. High-density carbon capsules made for ICF experiments at the National Ignition Facility are precision polished to achieve surface smoothness on the order of a few nanometers as well as to minimize isolated defects in the form of pits. Given the critical role of this process, we are developing smart manufacturing techniques with the goal of elevating the efficiency of this process.
Our approach is to use MEMS (micro-electromechanical systems)–based sensors to capture the fine vibration signals generated during the polishing process and combine them with synchronized visual feedback as needed. Beyond using these sensors for process monitoring, we use specific deep learning methods to analyze the data and extract correlations with both the process parameters and the final performance of the polishing run. Here, we describe the multiple fronts we have explored in this regard and the results we have gotten so far. This approach promises to have the potential to ultimately provide real-time feedback that can be used to ensure the progress of the run as well as a means for faster optimization.