United States Project Notice - Longer Wavelength Lasers For Inertial Fusion Energy With Laser-Plasma Instability Control: Machine Learning Optimum Spike Trains Of Uneven Duration And Delay (STUD Pulses)


Project Notice

PNR 61477
Project Name Longer Wavelength Lasers for Inertial Fusion Energy with Laser-Plasma Instability Control: Machine Learning Optimum Spike Trains of Uneven Duration and Delay (STUD Pulses)
Project Detail PingThings will develop a national infrastructure for analytics and artificial intelligence (AI) on the power grid using a three-pronged approach. First, a scalable, cloud-based platform will store, process, analyze, and visualize grid sensor data. Second, massive open and accessible datasets will be created through (a) deploying grid sensors to capture wide-scale and localized grid behavior, (b) simulating and executing grid models to generate virtual sensor data, and (c) establishing a secure data exchange mechanism. Third, a diverse research community will be developed through focused educational content, online code sharing, and data and AI competitions. The project’s goal is to accelerate the development of data-driven use cases to improve grid operation and analysis. Potential Impact: This project will make research on DOE target impact areas easier to conduct by providing state-of-the-art tools and open access to necessary sensor datasets for analytic development and training of ML and Deep Learning (DL) models. Polymath Research will enable the use of longer-wavelength lasers for IFE. This project seeks to control LPI using pulses composed of Spike Trains of Uneven duration and Delay (STUD), a sequence of precisely timed laser pulses designed to disrupt LPI growth and memory build up in the plasma due to persistent self-organization of the plasma undergoing continuous and undisrupted laser energy deposition. The challenge is that with rather limited knowledge of the dynamic (micro-) state of the plasma, laser pulses composed of STUD must be devised to combat memory build up and exponential reamplification. The team will use data from simulation models and high-repetition-rate lasers to train a multitude of machine-learning algorithms to select optimal spike trains and define conditions where longer-wavelength, laser-triggered LPI can be successfully tamed. These predictions will then be tested in follow-on work on a laser facility operating at high energy. Potential Impact: Controlling LPI on the scale of instability growth times is a game changer for all laser-based IFE schemes.
Funded By Self-Funded
Sector Energy & Power
Country United States , Northern America
Project Value USD 1,147,032

Contact Information

Company Name Polymath Research
Web Site https://arpa-e.energy.gov/technologies/projects/longer-wavelength-lasers-inertial-fusion-energy-laser-plasma-instability

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