A comparative analysis of text-to-image generative AI models in scientific contexts: a case study on nuclear power.

In this work, we propose and assess the potential of generative artificial intelligence (AI) as a tool for facilitating public engagement around potential clean energy sources. Such an application could increase energy literacy-an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy.

Operational data for fault prognosis in particle accelerators with machine learning.

This paper presents real operational data collected from the power systems of the Spallation Neutron Source facility, which provides the most intense neutron beam in the world. The authors have used a radio-frequency test facility (RFTF) and simulated system failures in the lab without causing a catastrophic system failure. Waveform signals have been collected from the RFTF normal operation as well as during fault induction efforts.

Real electronic signal data from particle accelerator power systems for machine learning anomaly detection.

This article describes real time series datasets collected from the high voltage converter modulators (HVCM) of the Spallation Neutron Source facility. HVCMs are used to power the linear accelerator klystrons, which in turn produce the high-power radio frequency to accelerate the negative hydrogen ions (H). Waveform signals have been collected from the operation of more than 15 HVCM systems categorized into four major subsystems during the years 2020-2022.