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Category: nuclear energy – Page 37

The nuclear fusion industry witnessed tremendous developments in 2023. The year 2022 drew its curtains with the National Ignition Facility (NIF) at Lawrence Livermore National Lab producing a fusion reaction in the laboratory that yielded more energy than was absorbed by the fuel to initiate it. The reaction yielded 1.3 megajoules of energy, about five times the 250 kilojoules that were absorbed by the capsule. This scientific breakthrough sparked an increase in investments in 2023 with new companies joining the race.

The Fusion Industry Association, or FIA, compiled the “Global Fusion Industry Report” of 2023. Pressing for fusion energy to take over as a cleaner source of energy, FIA presented a comprehensive overview of the advancements made in the second quarter of the year in this report. It highlights the effect of a successful ignition or net energy gain in nuclear fusion and its economic consequences.

FIA observed a net increase in investments in the fusion power industry. With $1.4 billion more than the previous year, 27 companies in fusion were able to draw $46 billion in investment. The ignition inspired the emergence of newer and smaller companies which contributed the majority share of the surge in investments. There are two reported big chequeholders securing funding over $100 million in the 2nd quarter—TAE Technologies in California and ENN in China.

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“There is a whole new discussion at least posing the question of the carbon footprint of particle physics.”

A particle collider, sometimes referred to as an atom smasher, is a type of high-energy physics apparatus used to investigate the fundamental particles and forces that exist in the cosmos. Subatomic particles, such as protons, electrons, or other charged particles, are accelerated to extremely high speeds and collide at extremely high energies in particle colliders.

Scientists use them to study the core components of matter and the fundamental forces of existence such as the nature of dark matter, the properties of quarks and leptons as well as the strong nuclear force, the weak nuclear… More.

Emilio Nanni/SLAC National Accelerator Laboratory.

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Scientists showcased the application of machine learning in the sodium-cooled fast reactor (SFR).

Machine learning technology has the potential to transform nuclear reactor operations, according to a team of experts from the US Department of Energy’s Argonne National Laboratory, who demonstrated how it may improve security and efficiency.

They showcased the application of machine learning in the sodium-cooled fast reactor (SFR), a specialized cutting-edge nuclear reactor.

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Nuclear fusion holds the promise to generate energy in a clean, safe, and nearly inexhaustible way. The physical idea of fusion involves confining fuels at unearthly temperatures of approximately 150,000,000 degree Celsius which fusion reactions between atomic nuclei can happen. The fuels of interest, deuterium and tritium (isotopes of hydrogen), exist in the state of plasma. Clearly, containing these extremely hot plasmas with solid walls is unfeasible.

A plasma is an ionised gas comprising charged particles, both ions and electrons. Fortunately, the dynamics of charge particles are subject to constraints along magnetic field lines. This insight forms the basis of our current approach: constructing a magnetic bottle using powerful magnetic fields that effectively trap the plasma along these intangible field lines.

One of the most iconic magnetic confinement machine designs is the tokamak — a toroidally-shaped device, often likened to a doughnut. The name ‘tokamak’ is derived from the Russian acronym for ‘to roidal cha mber with ma gnetic c oils.’

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The neutrino, one of nature’s most elusive and least understood subatomic particles, rarely interacts with matter. That makes precision studies of the neutrino and its antimatter partner, the antineutrino, a challenge. The strongest emitters of neutrinos on Earth—nuclear reactors—play a key role in studying these particles. Researchers have designed the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT) for detailed studies of electron antineutrinos coming from the core of the High Flux Isotope Reactor (HFIR).

Now the PROSPECT research collaboration has reported the most precise measurement ever of the energy spectrum of antineutrinos emitted from the fission of uranium-235 (U-235). These results provide scientists with new information about the nature of these particles.

PROSPECT’s collaborators represent more than 60 participants from 13 universities and four national laboratories. They built a novel detector system and installed it with extensive, tailored shielding against background at the HFIR research , a Department of Energy (DOE) Office of Science user facility at Oak Ridge National Laboratory. The research focuses on antineutrinos emerging from the fission of U-235. Produced by nuclear beta decay, antineutrinos are antimatter-particle counterparts to neutrinos.

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Chinese scientists claim they’ve had unexpected success in developing a high-powered microwave (HPM) weapon, according to The Diplomat. The magazine notes that in January, Huang Wenhua, deputy director of China’s Northwest Institute of Nuclear Technology, was awarded for his research on directed energy, which HPM weapons use.

HPM systems are able to destroy electronic equipment, and in an age when most combat systems—from tanks to planes, radios to satellites—rely on electronics, the weapons could change the way wars are fought. Warships will be fitted with HPM weapons to intercept incoming missiles.

The HPM project, alongside other projects involving lasers and electromagnetic pulses, is part of the Chinese regime’s “Assassin’s Mace” (or “Trump Card”) program designed to defeat a technologically superior opponent by disabling or destroying the technology that makes the opponent superior.

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Ceramic nanowires could essentially be used even for car tires reducing even hazardous rubber waste.

A team of MIT-led engineers found a simple, inexpensive way to strengthen Inconel 718 with ceramic nanowires to be used in metal PBF AM processes. The team believes that their general approach could be used to improve many other materials. “There is always a significant need for the development of more capable materials for extreme environments. We believe that this method has great potential for other materials in the future,” said Ju Li, the Battelle Energy Alliance Professor in Nuclear Engineering and a professor in MIT’s Department of Materials Science and Engineering (DMSE).

Li, who is also affiliated with the Materials Research Laboratory (MRL), is one of three corresponding authors of a paper on the work that appeared in the April 5 issue of Additive Manufacturing. The other corresponding authors are Professor Wen Chen of the University of Massachusetts at Amherst and Professor A. John Hart of the MIT Department of Mechanical Engineering.

Co-first authors of the paper are Emre Tekoğlu, an MIT postdoc in the Department of Nuclear Science and Engineering (NSE); Alexander D. O’Brien, an NSE graduate student; and Jian Liu of UMass Amherst. Additional authors are Baoming Wang, an MIT postdoc in DMSE; Sina Kavak of Istanbul Technical University; Yong Zhang, a research specialist at the MRL; So Yeon Kim, a DMSE graduate student; Shitong Wang, an NSE graduate student; and Duygu Agaogullari of Istanbul Technical University. The study was supported by Eni S.p. A. through the MIT Energy Initiative, the National Science Foundation, and ARPA-E.

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The supercomputer which is under construction is 50 times more powerful that existing supercomputer at the facility.

The world’s most powerful supercomputer, Aurora, is being set up in the US to help scientists at the Argonne National Laboratory (ANL) simulate new nuclear reactors that are more efficient and safer than their predecessors, a press release said.

The US is already home to some of the world’s fastest supercomputers, as measured by TOP500. These supercomputers can be tasked with a variety of computational roles. Last month, Interesting Engineering reported how the Los Alamos National Laboratory (LANL) planned to use a supercomputer to check nuclear stockpiles for the US military.

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