Latest news with #Argonne
Miami Herald
05-07-2025
- Automotive
- Miami Herald
Hybrid vs EV vs Gas: Which Actually Saves You the Most Money?
In 2025, you can buy a Toyota Prius, skip the gas station for days, and still complain about the ride quality on potholes. That's the hybrid life: part monk, part commuter ninja. And with gas prices moonwalking toward $4 again, it's a lifestyle many Americans are still buying into-over 1.2 million hybrid sales last year alone. But the EV crowd is yelling louder every year: "Just go all electric!" So let's settle it - does the hybrid still make financial sense? Or is it a stepping stone past its prime? We did the math. Real numbers. Real assumptions. No "green halo" pricing fluff. Figure 1: 10-Year Total Cost of Ownership (TCO) Over a 10-year period, electric vehicles (EVs) can be the most cost-effective option-but only under ideal conditions like access to home charging and sufficient annual mileage. According to DOE and Argonne National Lab, hybrids remain the lowest total cost of ownership for the average American, especially for those without dedicated charging infrastructure. Assumptions: ICE: $28K purchase, $1,500/year fuel, $600/year maintenanceHybrid: $30K purchase, $1,200/year fuel, $700/year maintenanceEV: $35K purchase, $400/year electricity, $300/year maintenance The hybrid buyer still spends a little more upfront than the gas car buyer, but makes it back over time. You're saving around $300 a year at the pump, and while maintenance isn't zero (hello, regenerative braking sensors), it's generally lower than ICE over the long haul. Add in federal tax credits or state perks, and hybrids remain one of the best deals on the road, especially if you're putting in 15,000+ miles per year. Clarification: While EVs offer lower operational costs, hybrids remain the cheapest total cost option for many average-use cases, per Argonne. Here's the truth: EVs are significantly cheaper to run - when you can charge at home. Charging at home averages $400/year in electricity, compared to $1,200 in gas for a hybrid. Plus, there's no oil to change, no spark plugs to replace, and your brake pads last longer thanks to regen braking. But that advantage flips quickly if you rely on public fast-charging, which can be 3x to 5x more expensive than home charging. According to DOE/Argonne, EVs relying heavily on public charging often end up more expensive than hybrids or ICE cars over 10 years. EVs can save you about $7,000 over 10 years - but only if you charge smart and often at home. The myth that hybrids are more expensive to maintain because of "two powertrains" doesn't hold up. Most hybrid systems are built like tanks. Inverters or battery packs rarely fail under warranty, and regenerative brakes reduce wear. Meanwhile, ICE cars need regular oil changes, timing belt swaps, and eventually, catalytic converter work. EVs? Lowest upkeep by far. Scheduled EV maintenance at 6 cents per mile, compared to 10 cents for gas cars. However, battery replacement costs remain a wildcard for EVs beyond the warranty period, usually past year 8 or 10. That risk keeps hybrids competitive. So, here's what the numbers and studies actually tell us: The hybrid still offers the lowest total cost of ownership for the average driver, especially those without home charging or lower annual can be more affordable, but only with access to home chargingand consistent vehicles remain the costliest long-term, due to fuel and maintenance costs. So no, hybrids aren't a "marketing mirage." They're still the smartest move for the gas-averse, chargerless majority. And EVs? They're the future, but not everyone's present. The real question is this: Will the hybrid stay a stepping stone or become the sensible forever car? Copyright 2025 The Arena Group, Inc. All Rights Reserved.
Business Wire
01-07-2025
- Business
- Business Wire
From Waste to Watts: Argonne is Unlocking the Power in Used Nuclear Fuel
LEMONT, Ill.--(BUSINESS WIRE)--Nuclear reactors across the U.S. generate used fuel, but more than 95% of that fuel still holds valuable energy. Through advanced chemical processes and new technologies, scientists aim to recycle this material to generate more power and reduce long-term radioactive waste. Researchers are now working to make this potential a practical, scalable solution. Scientists at the U.S. Department of Energy's Argonne National Laboratory are partnering with SHINE Technologies, a Wisconsin-based company, to design a new process for separating valuable materials from used nuclear fuel. The process employs innovative equipment, including centrifugal contactors—spinning devices that efficiently separate mixed liquids—to create a safer, more efficient recycling method ready for industrial use. 'Bridging scientific research and industrial implementation is critical,' said Candido Pereira, deputy division director of Argonne's Chemical and Fuel Cycle Technologies (CFCT) division. 'Our team is committed to supporting the private sector with world-class solutions.' Recycling used nuclear fuel is complex. It's highly radioactive, generates heat, and must be safely stored and cooled. Facilities need systems to manage small amounts of leftover material. Nuclear material also requires strict safeguards to prevent unauthorized use. Scientists apply 'safeguards by design' early in development to ensure compliance and security. Economic viability is another hurdle. Recovered materials need practical applications and buyers. Some utilities may use recycled fuel in advanced reactors. Other byproducts—such as radioisotopes—can power space missions or be used in medical diagnostics. If demand exists, building recycling facilities becomes more feasible. Foresight is essential. Scientists must anticipate how future reactors will use fuel and what materials they'll require. Experts at national labs like Argonne are uniquely positioned to predict these needs and tailor recycling processes. Peter Tkac, nuclear chemist and manager of Argonne's CFCT Radiochemistry Group, leads the effort. 'It's important we develop a process that doesn't produce a pure plutonium stream,' he said, citing security risks. Tkac's team uses a Van de Graaff accelerator to simulate radiation conditions and test chemical durability in safe lab environments, speeding innovation. Tkac's group also collaborates with SHINE to test Argonne's centrifugal contactors. Using 3D printing, they build custom prototypes of different designs, allowing rapid, cost-effective testing. 'Recycling used nuclear fuel is complicated,' said Tkac. 'But with the right strategy, we can reduce waste, improve energy supply, and support the future of nuclear power.'
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.
