Leveraging advances from ORNL in manufacturing, materials, nuclear science, nuclear engineering, high-performance computing, data analytics, and related fields, the Transformational Challenge Reactor Demonstration Program looks to build and operate an additively manufactured microreactor by 2023. The researchers say they are now scaling up the additive manufacturing process necessary to build it, and developing methods to confirm the consistency and reliability of its printed components.
"The nuclear industry is still constrained in thinking about the way we design, build, and deploy nuclear energy technology," says ORNL Director Thomas Zacharia. "DOE launched this program to seek a new approach to rapidly and economically develop transformational energy solutions that deliver reliable, clean energy."
Traditionally, reactor development and deployment has relied on materials, fuels, and technology pioneered in the 1950s and '60s, and been slowed by high costs and decades-long construction times. The TCR program, say the scientists, will introduce new, advanced materials and use integrated sensors and controls, providing a highly optimized, efficient system that reduces cost, relying on scientific advances with potential to shape a new path in reactor design, manufacturing, licensing, and operation.
The researchers say they have completed several foundational experiments including selection of a core design, and a three-month "sprint" that demonstrated the ability of the additive manufacturing technology to quickly produce a prototype reactor core, which took 40 hours to build with temperatures reaching over 1,400 degrees Celsius around the melt pool where a laser heats and melts while adding a new layer. They will now focus on refining the selected design and the processes that will ensure an optimal and reliable energy system.
The researchers are using monitoring technologies such as thermal imaging to continually assess the manufacturing process, and which provide live data streams that enable real-time qualification of the printed material and performance analysis through artificial intelligence. Extensive post-build testing is also conducted to assess component performance and establish links between the behavior of each