AWS to acquire nuclear-powered data center in Pennsylvania. Find out how this move will impact cloud services and energy consumption.
Category: nuclear energy – Page 29
If two points were ripped apart faster than light, they would no longer interact through any force of physics. Whereas a constant dark energy would leave behind already-intact objects, like clusters of galaxies, phantom energy could tear them apart. In a finite amount of time, billions of years from now, clusters would tear apart, followed by ever-smaller objects. Even atomic and nuclear bonds would not withstand the onslaught.
Eventually, space itself would dissolve in an event known as the Big Rip. Any two points, no matter how close, would be ripped infinitely far away from each other. The very structure of space-time, the causal foundations that make our universe work, would no longer behave. The universe would just break down.
However, luckily, most physicists do not believe this scenario can actually happen. For one, it’s unclear how this process of ripping interacts with the other laws of physics. For example, quarks cannot be torn apart — when you attempt to do so, you need so much energy that new quarks materialize out of the vacuum. So ripping apart quarks just might lead to other, interesting interactions.
Most people know about solids, liquids, and gases as the main three states of matter, but a fourth state of matter exists as well. Plasma—also known as ionized gas—is the most abundant, observable form of matter in our universe, found in the sun and other celestial bodies.
Creating the hot mix of freely moving electrons and ions that compose a plasma often requires extreme pressures or temperatures. In these extreme conditions, researchers continue to uncover the unexpected ways that plasma can move and evolve. By better understanding the motion of plasma, scientists gain valuable insights into solar physics, astrophysics, and fusion.
In a paper published in Physical Review Letters, researchers from the University of Rochester, along with colleagues at the University of California, San Diego, discovered a new class of plasma oscillations—the back-and-forth, wave-like movement of electrons and ions. The findings have implications for improving the performance of miniature particle accelerators and the reactors used to create fusion energy.
Artificial intelligence is pivotal in advancing biotechnology and medical procedures, ranging from cancer diagnostics to the creation of new antibiotics. However, the ecological footprint of large-scale AI systems is substantial. For instance, training extensive language models like ChatGPT-3 requires several gigawatt-hours of energy—enough to power an average nuclear power plant at full capacity for several hours.
Prof. Mario Chemnitz and Dr. Bennet Fischer from Leibniz IPHT in Jena, in collaboration with their international team, have devised an innovative method to develop potentially energy-efficient computing systems that forego the need for extensive electronic infrastructure.
They harness the unique interactions of light waves within optical fibers to forge an advanced artificial learning system. Unlike traditional systems that rely on computer chips containing thousands of electronic components, their system uses a single optical fiber.
The Voyager 1 was launched in 1977. Almost 50 years later, it’s still going and sending back information, penetrating ever deeper into space. It can do that because it’s powered by nuclear energy.
Long a controversial energy source, nuclear has been experiencing renewed interest on Earth to power our fight against climate change. But behind the scenes, nuclear has also been facing a renaissance in space.
In July, the US National Aeronautics and Space Administration (NASA) and Defense Advanced Research Projects Agency (DARPA) jointly announced that they plan to launch a nuclear-propelled spacecraft by 2025 or 2026. The European Space Agency (ESA) in turn is funding a range of studies on the use of nuclear engines for space exploration. And last year, NASA awarded a contract to Westinghouse to develop a concept for a nuclear reactor to power a future moon base.
Princeton researchers report that a new AI model has solved one of the major roadblocks to generating fusion energy.
Fusion powers the Sun, and, by extension, makes life on Earth possible.
Researchers use AI to predict and prevent plasma instabilities in fusion reactors, averting reaction disruptions. Experiments show AI forecasts issues 300 milliseconds early, allowing real-time adjustments for stability.
Fashioned from the same element found in sand and covered by intricate patterns, microchips power smartphones, augment appliances and aid the operation of cars and airplanes.
Now, scientists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) are developing computer simulation codes that will outperform current simulation techniques and aid the production of microchips using plasma, the electrically charged state of matter also used in fusion research.
These codes could help increase the efficiency of the manufacturing process and potentially stimulate the renaissance of the chip industry in the United States.
India will invite private firms to invest about $26 billion in its nuclear energy sector to increase the amount of electricity from sources that don’t produce carbon dioxide emissions, two government sources told Reuters.
In this study, a novel rapid diagnostic method was developed for optimizing the production of transplutonium isotope through high flux reactor irradiation. The proposed method was based on the concept of “Single Energy Interval Value (SEIV)” and “Energy Spectrum Total Value (ESTV)”, which significantly improved the production efficiency of isotopes such as 252Cf (by 15.08 times), 244Cm (by 65.20 times), 242Cm (by 11.98 times), and 238Pu (by 7.41 times). As a promising alternative to the traditional Monte Carlo burnup calculation method, this method offers a more efficient approach to evaluate radiation schemes and optimize the design parameters. The research discovery provides a theoretical basis for further refining the analysis of transplutonium isotope production, leading to more efficient and sustainable production methods. Future studies could focus on the implementation of energy spectrum conversion technology to further improve the optimal energy spectrum.
The production of transplutonium isotope, which are essential in numerous fields such as military and space technology, remains inefficient despite being produced through irradiation in a high flux reactor. Past studies on the optimization of transplutonium isotope production through irradiation in a high flux reactor have been limited by the computational complexity of traditional methods such as Monte Carlo burnup calculation. These limitations have hindered the refinement of the evaluation, screening, and optimization of the irradiation schemes. Hence, this research aimed to develop a rapid diagnostic method for evaluating radiation schemes that can improve the production efficiency of isotopes such as 252Cf, 244Cm, 242Cm, and 238Pu. The outcome of the study showed great potential in advancing the production of transplutonium isotope, which have numerous applications in fields such as military, energy, and space technology.
The limited production rate of transplutonium isotopes poses a significant challenge in meeting the growing demand for sustaining the nuclear industry (i.e. energy and military). This research provides a sustainable solution to improve the efficiency of transplutonium isotope production through a novel rapid diagnostic method. Thus, it fulfils UNSDG 7 (Affordable and Clean Energy) by providing a sustainable source of energy, as well as UNSDG 9 (Industry, Innovation and Infrastructure) by promoting technological innovation in the nuclear industry, especially for military use.