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Purdue Engineering receives $800,000 Grant to accelerate the development of the 3D Printed Nuclear Reactors

Microreactor
Above: Hany Abdel-Khalik virtually meets with John W. Sutherland (top on screen), and Xinghang Zhang (co-principal investigator and professor of materials engineering)/Image Credit: Purdue University

Purdue University‘s College of Engineering recently received a huge $800,000 grant from the U.S. Department of Energy to accelerate the introduction of the 3D printed nuclear reactors. These new type of nuclear reactors – Microreactors, have the flexibility and versatility needed for many current energy applications. 

3D Printed Nuclear Reactor

Microreactor
Above: 3D printing of nuclear reactor core in progress/Image Credit: SciTechDaily

The Nuclear Energy University Program funding will enable Purdue to be a key contributor to the Transformational Challenge Reactor Demonstration Program, in which the Department of Energy’s Oak Ridge National Laboratory is working to build the first 3D printed nuclear reactor by 2023. The 3D printed nuclear reactor also will be the first advanced reactor to operate in the U.S. in more than 40 years. To support this mission, Purdue will develop and demonstrate a novel artificial intelligence method to ensure the quality of the microreactor’s components.

This project by the Purdue University will be using additive manufacturing technology, computation materials modelling, and AI concepts in creating the nuclear reactor components. The aim of combining these technologies is to significantly reduce manufacturing costs and development time, and to realistically estimate safety risks while offering reliability and convenient access to nuclear power.

According to Hany Abdel-Khalik, technical lead for the project and associate professor of nuclear engineering, “Microreactors introduce a transformational trend to the nuclear industry – a trend that enables more streamlined construction and deployment processes to address the nation’s energy challenges that cannot be overcome solely with large-scale nuclear reactors.”

He continued, “Purdue will fill a technological gap in the nuclear industry, reflecting a broader trend of applying AI strategies to support additive manufacturing (AM). AM enables designs to be adjusted during manufacturing, greatly decreasing production cost and time. Our work is aimed at driving widespread adoption of additively manufactured reactor components by using an AI-powered software system to ensure safety and reliability.”

3D printed nuclear reactor
Above: ORNL’s 3D printed nuclear reactor core/Image Credit: ORNL

Kurt Terrani, director of the TCR program at ORNL, said, “Synchronized application of additive manufacturing and artificial intelligence techniques are key to providing the most data-rich and cost-effective nuclear component qualification process. This is one of the key goals of DOE-NE’s TCR program: using modern technology to deliver a new and better way to deploy nuclear energy.”

Terrani also added, “The program is engaging the industry, the regulator and, in this case, universities in order to ensure an optimal approach is developed and adopted in widespread fashion. The technical strength of the Purdue team will shore up our ability to deliver on these goals.”

Project leader John W. Sutherland, the Fehsenfeld Family Head of Environmental and Ecological Engineering commented, “Purdue Engineering has ideal strengths to support the mission of the Department of Energy on the development of 3D printed nuclear reactors. We bring together deep expertise and collaborative capabilities, encompassing manufacturing, nuclear engineering, materials engineering, environmental engineering, AI and data analytics, nuclear reactors modelling and simulation, and systems engineering. In this project, we’ll be blending fundamental science and engineering in a very complex and dynamic environment.”

Xinghang Zhang, co-principal investigator and professor of materials engineering said, “Purdue’s solution will apply reinforcement learning, a kind of AI that uses advanced machine learning strategies to fine-tune the selection of the optimum AM process parameters – such as printing speed and melting temperature in this case – to train the AI models and guide decision making.”

The project team also includes Steven Shade, the Ball Brothers Director of Advanced Manufacturing Initiatives, who is affiliated with Environmental and Ecological Engineering.


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