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Jefferson Lab awarded $8M for accelerator technology to enable transmutation
The Thomas Jefferson National Accelerator Facility is leading research supported by two Department of Energy Advanced Research Projects Agency–Energy (ARPA-E) grants aimed at developing accelerator technology to enable nuclear waste recycling, decreasing the half-life of spent nuclear fuel.
Both grants, totaling $8.17 million in combined funding, were awarded through the Nuclear Energy Waste Transmutation Optimized Now (NEWTON) program, which aims to enable the transmutation of nuclear fuels by funding novel technologies for improving the performance of particle generation systems.
Meenu Sethu, Bhavya Kotla, Darrell Russell, Mahboubeh Madadi, Nesar Ahmed Titu, Jamie Baalis Coble, Ronald L. Boring, Klaus Blache, Vivek Agarwal, Vaibhav Yadav, Anahita Khojandi
Nuclear Technology | Volume 209 | Number 3 | March 2023 | Pages 276-294
Critical Review—Human-Machine Interface Technologies | doi.org/10.1080/00295450.2022.2067461
Articles are hosted by Taylor and Francis Online.
Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions.