Latest news with #Argonne
Yahoo
6 days ago
- 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
6 days ago
- 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.
Business Wire
15-05-2025
- Business
- Business Wire
AI helps build smarter, more resilient power grids
LEMONT, Ill.--(BUSINESS WIRE)--As society's reliance on electricity deepens, artificial intelligence (AI) is reshaping how we manage power grids and optimize energy sources. A recent workshop hosted by the U.S. Department of Energy's Argonne National Laboratory brought together leading experts from national labs, universities, government agencies, and industry to explore the transformative potential of AI foundation models for electric grids. 'We are laying the foundation for a future where AI-driven models will be an integral part of how we manage and optimize our power grids.' — Emil M. Constantinescu Share The three-day Foundational Models for Electric Grid workshop—organized by Argonne researchers Kibaek Kim, Emil M. Constantinescu, and Adrian Maldonado—was the third event in an evolving series. In partnership with IBM, Hydro-Québec, and the National Rural Electric Cooperative Association (NRECA), the event reinforced collaboration for smarter, more adaptive grids. Kim noted the field's momentum. 'We've seen attendance grow from about 25 participants at our first workshop to well over 100 at this latest session,' he said. 'This surge in interest reflects the field's rapid advancement and the urgent need for AI solutions in electric grid management.' The workshop emphasized real-world application. Through technical sessions, panels, live demos, and structured networking, participants shared insights and best practices. Industry leaders showcased AI innovations from advanced forecasting to automated distribution systems that boost performance and resilience. Argonne's Valerie Taylor and Henry Huang delivered keynotes underscoring the lab's leadership. Huang emphasized that advanced analytics and AI are essential to modernizing power systems and making them more resilient and efficient. A major focus was the use of foundation models—AI systems pre-trained on vast datasets and tailored to grid challenges. Maldonado explained, 'Our foundation model is an AI engine trained on extensive datasets covering various power grid functions. It's designed to handle everything from forecasting to operations, making it a comprehensive solution for modern grid management.' 'These models can detect subtle signals that traditional methods often miss, helping us predict and prevent outages before they cause significant disruptions,' said Constantinescu. Privacy-preserving federated learning (PPFL) was also highlighted. This method trains models on sensitive energy data without compromising privacy. Kim said, 'As we introduce more distributed energy resources to the grid — such as natural gas generators and geothermal — these AI systems will help us manage increasingly complex operations with greater precision.' 'Our goal is not just theoretical,' added Constantinescu. 'We are actively working on integrating foundation models into operational workflows, ensuring they can be used effectively in real-world power systems.' Argonne remains at the forefront of advancing AI-driven energy resilience. Insights from this workshop will drive future research initiatives and strengthen industry collaborations, ensuring that tomorrow's power systems are secure, efficient, and adaptive. "There are two major challenges we are addressing—technological limitations in grid modeling, and broader resilience issues facing modern power systems," concluded Constantinescu. "This is only the beginning. We are laying the foundation for a future where AI-driven models will be an integral part of how we manage and optimize our power grids."
Business Wire
22-04-2025
- Health
- Business Wire
Argonne Leverages AI and Supercomputing to Revolutionize Cancer Research
LEMONT, Ill.--(BUSINESS WIRE)--Discovering new drugs to treat cancer and predicting how tumors will respond to them remain key challenges in the fight against the disease. At the U.S. Department of Energy's (DOE) Argonne National Laboratory, researchers are using the power of artificial intelligence (AI) and high performance computing (HPC) to pioneer new methods that speed up drug discovery and enhance drug response prediction. Over the past decade, their efforts have evolved from creating AI tools for cancer research to evaluating the growing number of AI models, ultimately leading to their latest work aimed at drug-resistant cancer targets. Using the new Aurora exascale system, researchers led an early science project that focused on AI-driven drug discovery for cancer. Their work demonstrated how the system's immense processing power can help accelerate the discovery of promising new drug molecules. A decade of AI-driven innovation The origins of Argonne's AI-driven cancer research date back to 2016, when DOE forged a partnership with National Cancer Institute (NCI) to employ advanced computing technologies in the fight against cancer. Argonne has been a key player in this collaboration, developing software and AI tools to accelerate progress in cancer research. A cornerstone of this effort was the CANcer Distributed Learning Environment (CANDLE) project. Led by Argonne's Rick Stevens and supported by DOE's Exascale Computing Project, CANDLE's goal was to develop a scalable deep learning software stack for the nation's exascale supercomputers. With that foundation, the lab's focus extended from building AI models to developing a rigorous method to assess the growing number of models emerging from the broader cancer research community. This shift led to the launch of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project in 2021. Led by Argonne in collaboration with Frederick National Laboratory for Cancer Research, the IMPROVE team set out to develop a standardized way to analyze and compare the performance of various drug response prediction models. While the team's efforts are laying the groundwork for more reliable and effective AI models, the project continues to evolve to meet new challenges as they emerge. The next frontier: 'undruggable' targets Expanding on the DOE-NCI efforts, researchers are now setting their sights on a longstanding challenge in cancer research: 'undruggable' targets (proteins that are known to resist chemical treatments). With a focus on proteins that play a key role in cancer progression, the team's work begins with a list of targets identified through lab experiments. The researchers then retrieve the protein sequences from public databases. If a protein's 3D structure is unknown, they work with scientists at the Advanced Photon Source (APS), a DOE Office of Science user facility, to determine it. The APS was recently upgraded to deliver significantly brighter X-ray beams, giving scientists a powerful tool to advance research across different fields. After determining the protein's structure, the team turns to Aurora to simulate the behavior and interactions of the protein at the atomic level. The simulations combined with experimental data help identify areas where small molecules might bind to inhibit the protein's activity. The computational results are then relayed to experimental collaborators to validate the findings. Adding to the challenge is the team's focus on undruggable targets. Inhibiting these proteins has eluded researchers for decades, earning them a reputation as one of the most difficult problems in cancer biology.
Morocco World
03-04-2025
- Business
- Morocco World
Moroccan Scientist Khalil Amine Elected to US Academy of Engineering
Rabat– Khalil Amine, a Moroccan materials scientist, has been elected to the National Academy of Engineering of the United States for his contributions to battery and energy storage technologies. The recognition comes for his leadership in the field of materials science, specifically in the development of batteries and energy storage devices. Amine, who also serves as a professor at the University of Chicago, is among 128 members and 22 international members inducted into the NAE class of 2025. 'I am very delighted to be selected as a member of the National Academy of Engineering,' said Amine. 'This is a recognition not only for me, but also for all my co-workers and collaborators around the world, as well as Argonne, which has provided an unmatched, state-of-the-art capability to do excellent work.' Amine leads the Advanced Battery Technology team at Argonne, where his research focuses on the development of advanced chemistries, materials, and battery systems. His team's work spans several industries, including automotive, power grids, satellites, military, and medical applications. A key focus of Amine's research is the creation of new cathodes, anodes, solid-state electrolytes, and additives for lithium-ion batteries, as well as exploring 'beyond-lithium' batteries that use alternative chemistries for energy storage. Read also: Morocco Trains 11,000 Engineers a Year to Ensure Future-Ready Workforce Amine's significant contributions to the field of battery technology have made him a leading figure in materials science. He holds more than 200 patents or patent applications in the field, and he was for 23 years the most cited scientist in battery technology globally. His accomplishments have earned him numerous accolades, including the prestigious Global Energy Prize in 2019. Amine is also a member of several prestigious scientific organizations, including the National Academy of Inventors, the European Academy of Sciences, and the Electrochemical Society, among others. Born in Morocco, Amine earned degrees in chemistry and materials science from the University of Bordeaux. After his academic training, he joined Argonne in 1998, bringing with him experience gained from research positions in Belgium and Japan. His innovative work has played a pivotal role in advancing energy storage technologies that have far-reaching applications in today's technological landscape. The National Academy of Engineering, founded in 1964, provides independent analysis and advice on engineering matters, offering leadership and insight into complex global challenges. Amine, along with other members of the NAE class of 2025, will be formally inducted at the Academy's annual meeting in October. Tags: khalil AmineMoroccan scientistUS Academy of engineering
