Latest news with #RuiHu
Sustainability Times
06-06-2025
- Science
- Sustainability Times
'Reactor Has a Mind Now': U.S. Nuclear Plants Given Digital Twins That Predict Failures Before They Even Exist
IN A NUTSHELL 🚀 Scientists at Argonne National Laboratory have developed advanced digital twins for nuclear reactors, enhancing safety and efficiency. for nuclear reactors, enhancing safety and efficiency. 🔍 Built upon graph neural networks , these digital twins offer rapid and accurate predictions of reactor behavior under various conditions. , these digital twins offer rapid and accurate predictions of reactor behavior under various conditions. 💡 The technology has been successfully applied to both the Experimental Breeder Reactor II and the new generic Fluoride-salt-cooled High-temperature Reactor. 🔧 Digital twins enable continuous monitoring and proactive maintenance, potentially leading to lower operating costs and paving the way for autonomous operations. In a groundbreaking development, scientists at the US Department of Energy's Argonne National Laboratory have introduced advanced digital twins for nuclear reactors—a transformative technology that promises to enhance reactor efficiency, predictive maintenance, and overall safety. Built upon the latest advancements in artificial intelligence, these dynamic virtual replicas simulate physical reactors, enabling unprecedented improvements in operational capabilities. With these digital twins, scientists can now monitor and predict the behavior of reactors under various conditions, paving the way for more efficient and safer nuclear energy production. This article delves into the technology's intricate details and its potential to revolutionize the nuclear energy landscape. Harnessing the Power of Graph Neural Networks The digital twin technology developed at Argonne is underpinned by graph neural networks (GNNs), a state-of-the-art AI framework adept at processing complex, interconnected data. These networks are uniquely suited to replicate the intricate systems within a nuclear reactor. By preserving the layout of reactor systems and embedding fundamental physics laws, GNN-based digital twins offer a robust and accurate replica of real systems. This capability allows for rapid predictions of reactor behavior under various conditions, significantly outperforming traditional simulation methods. Rui Hu, Argonne principal nuclear engineer and a key figure in the project, emphasizes that this technology marks a significant step towards understanding and managing advanced nuclear reactors. 'Our digital twin technology enables us to predict and respond to changes with the required speed and accuracy,' he states. The ability to swiftly simulate different scenarios enhances the reactor's operational readiness, ensuring that safety protocols are always one step ahead of potential issues. 'Ukraine to Restart Nuclear Power in Chernobyl': This Shocking Mini-Reactor Plan Sends Global Shockwaves Through Energy and Safety Circles Proven Success with Experimental and New Reactor Designs The Argonne team has successfully applied their digital twin methodology to both historical and new reactor designs. A notable application was the creation of digital twins for the now-inactive Experimental Breeder Reactor II (EBR-II), which served as a crucial test case for validating their simulation models. Furthermore, they have extended this approach to a new design, the generic Fluoride-salt-cooled High-temperature Reactor (gFHR). This successful application highlights the versatility and reliability of their technology. By leveraging vast datasets from Argonne's System Analysis Module (SAM), the AI models are trained to predict reactor behavior swiftly. The trained model can make accurate predictions based on limited real-time sensor data, supporting better planning and decision-making. The speed and accuracy of GNN-based digital twins are remarkable, significantly reducing the time required for simulations and potentially lowering maintenance and operating costs. 'Russia Deploys Floating Nuclear Beast': New 75-Megawatt Reactor Powers World's Largest Icebreaker Through Arctic Fury Ensuring Safety and Operational Efficiency The implications of digital twin technology for nuclear reactor safety and efficiency are profound. These digital replicas can continuously monitor reactors, detecting anomalies and suggesting changes to maintain optimal safety and operation. This proactive capability is expected to lead to significant reductions in maintenance and operating costs, providing more reliable predictions by understanding how all reactor parts work together. Argonne's digital twin technology offers numerous advantages over traditional methods, fostering a deeper understanding of reactor dynamics. By simulating various operational scenarios, the system can recommend adjustments to prevent potential issues before they arise. This level of foresight is crucial in ensuring the smooth operation of nuclear reactors, ultimately contributing to a safer and more sustainable energy future. 'China Moves Decades Ahead': World's First Fusion-Fission Hybrid Reactor Set to Eclipse U.S. Efforts by 2030 The Future: Autonomous Reactor Operations The potential future applications of digital twin technology are vast and exciting. Beyond immediate safety and efficiency improvements, this technology could enhance emergency planning and enable more informed real-time decision-making by operators. Perhaps most intriguingly, it could pave the way for autonomous reactor operations. The development of such capabilities utilized the processing power of the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility, underscoring the collaborative effort required to advance nuclear technology. As the nuclear energy sector continues to evolve, this innovation represents a significant step forward in the development and deployment of advanced reactors. By ensuring they operate safely, reliably, and efficiently, while reducing costs and extending component life, digital twins hold the promise of transforming how we harness nuclear energy. What does the future hold for the integration of AI-driven technologies in other critical sectors? Our author used artificial intelligence to enhance this article. Did you like it? 4.4/5 (20)
Yahoo
28-05-2025
- Business
- Yahoo
Argonne's Virtual Models Pave the Way for Advanced Nuclear Reactors
LEMONT, Ill., May 28, 2025--(BUSINESS WIRE)--Digital twins are virtual replicas of real-world systems, offering transformative potential across various fields. At the U.S. Department of Energy's (DOE) Argonne National Laboratory, researchers have developed digital twin technology to enhance the efficiency, reliability, and safety of nuclear reactors. This technology leverages advanced computer models and artificial intelligence (AI) to predict reactor behavior, aiding operators in making real-time decisions. According to Rui Hu, an Argonne principal nuclear engineer, this digital twin technology marks a significant advancement in understanding and managing advanced nuclear reactors. It enables rapid and accurate predictions and responses to changes in reactor conditions. Digital twins allow scientists to monitor and predict the behavior of small modular reactors and microreactors under different conditions. The Argonne team applied their methodology to create digital twins for two types of nuclear reactors: the now-inactive Experimental Breeder Reactor II (EBR-II) and a new type, the generic Fluoride-salt-cooled High-temperature Reactor (gFHR). The EBR-II digital twin served as a test case to validate the simulation models. The core of this digital twin technology is graph neural networks (GNNs), a type of AI that processes data structured as graphs, representing interconnected components. GNNs excel at recognizing complex patterns and connections, offering powerful insights into systems where relationships are crucial. By preserving the layout of reactor systems and embedding fundamental physics laws, GNN-based digital twins provide a robust and accurate replica of real systems. The researchers utilized the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility, to train the GNN and perform uncertainty quantification, which involves identifying and reducing uncertainty in models. GNN-based digital twins are significantly faster than traditional simulations, quickly predicting reactor behavior during various scenarios, such as changes in power output or cooling system performance. They achieve this by training on simulation data from Argonne's System Analysis Module (SAM), a tool for analyzing advanced nuclear reactors. The trained model can make accurate predictions based on limited real-time sensor data, supporting better planning and decision-making, and potentially reducing maintenance and operating costs. Additionally, digital twins can continuously monitor reactors to detect anomalies. If unusual behavior is detected, the system can suggest changes to maintain safety and smooth operation. Argonne's digital twin technology offers numerous advantages over traditional methods, providing more reliable predictions by understanding how all reactor parts work together. It can be used for emergency planning, informed decision-making, and potentially autonomous reactor operation in the future. This innovation represents a significant step forward in the development and deployment of advanced nuclear reactors, ensuring they operate safely, reliably, and efficiently while reducing costs and extending component life. View source version on Contacts Christopher J. KramerHead of Media RelationsArgonne National LaboratoryOffice: 630.252.5580Email: media@ Sign in to access your portfolio
Business Wire
28-05-2025
- Science
- Business Wire
Argonne's Virtual Models Pave the Way for Advanced Nuclear Reactors
LEMONT, Ill.--(BUSINESS WIRE)--Digital twins are virtual replicas of real-world systems, offering transformative potential across various fields. At the U.S. Department of Energy's (DOE) Argonne National Laboratory, researchers have developed digital twin technology to enhance the efficiency, reliability, and safety of nuclear reactors. This technology leverages advanced computer models and artificial intelligence (AI) to predict reactor behavior, aiding operators in making real-time decisions. 'Our digital twin technology introduces a significant step toward understanding and managing advanced nuclear reactors, enabling us to predict and respond to changes with the required speed and accuracy.' — Rui Hu, Argonne principal nuclear engineer Share According to Rui Hu, an Argonne principal nuclear engineer, this digital twin technology marks a significant advancement in understanding and managing advanced nuclear reactors. It enables rapid and accurate predictions and responses to changes in reactor conditions. Digital twins allow scientists to monitor and predict the behavior of small modular reactors and microreactors under different conditions. The Argonne team applied their methodology to create digital twins for two types of nuclear reactors: the now-inactive Experimental Breeder Reactor II (EBR-II) and a new type, the generic Fluoride-salt-cooled High-temperature Reactor (gFHR). The EBR-II digital twin served as a test case to validate the simulation models. The core of this digital twin technology is graph neural networks (GNNs), a type of AI that processes data structured as graphs, representing interconnected components. GNNs excel at recognizing complex patterns and connections, offering powerful insights into systems where relationships are crucial. By preserving the layout of reactor systems and embedding fundamental physics laws, GNN-based digital twins provide a robust and accurate replica of real systems. The researchers utilized the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility, to train the GNN and perform uncertainty quantification, which involves identifying and reducing uncertainty in models. GNN-based digital twins are significantly faster than traditional simulations, quickly predicting reactor behavior during various scenarios, such as changes in power output or cooling system performance. They achieve this by training on simulation data from Argonne's System Analysis Module (SAM), a tool for analyzing advanced nuclear reactors. The trained model can make accurate predictions based on limited real-time sensor data, supporting better planning and decision-making, and potentially reducing maintenance and operating costs. Additionally, digital twins can continuously monitor reactors to detect anomalies. If unusual behavior is detected, the system can suggest changes to maintain safety and smooth operation. Argonne's digital twin technology offers numerous advantages over traditional methods, providing more reliable predictions by understanding how all reactor parts work together. It can be used for emergency planning, informed decision-making, and potentially autonomous reactor operation in the future. This innovation represents a significant step forward in the development and deployment of advanced nuclear reactors, ensuring they operate safely, reliably, and efficiently while reducing costs and extending component life.
