In March 2011, the catastrophic accident known as "The Fukushima Daiichi nuclear disaster" took place, initiated by the Tohoku earthquake and tsunami in Japan. The only nuclear accident to receive a Level-7 classification on the International Nuclear Event Scale since the Chernobyl nuclear power plant disaster in 1986, the Fukushima event triggered global concerns and rumors regarding radiation leaks. Among the false rumors was an image, which had been described as a map of radioactive discharge emanating into the Pacific Ocean, as illustrated in the accompanying figure. In fact, this figure, depicting the wave height of the tsunami that followed, still to this date circulates on social media with the inaccurate description. Social media is ideal for spreading rumors, because it lacks censorship.
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of pitting and stress corrosion cracking, with a focus on dry storage canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion cracks via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.
Large uncertainties in many phenomena of interest have challenged the reliability of pertaining decisions. Collecting additional information to better characterize involved uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and assists with optimal allocation of resources for information collection. However, a primary challenge facing VoI analysis is the very high computational cost of the underlying Bayesian inference especially for equality-type information. This paper proposes the first surrogate-based framework for VoI analysis. Instead of modeling the limit state functions describing events of interest for decision making, which is commonly pursued in surrogate model-based reliability methods, the proposed framework models system responses. This approach affords sharing equality-type information from observations among surrogate models to update likelihoods of multiple events of interest. Moreover, two knowledge sharing schemes called model and training points sharing are proposed to most effectively take advantage of the knowledge offered by costly model evaluations. Both schemes are integrated with an error rate-based adaptive training approach to efficiently generate accurate Kriging surrogate models. The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge. While state-of-the-art methods based on importance sampling and adaptive Kriging Monte Carlo simulation are unable to solve this problem, the proposed method is shown to offer accurate and robust estimates of VoI with a limited number of model evaluations. Therefore, the proposed method facilitates the application of VoI for complex decision problems.
Japanese authorities are introducing a variety of measures to prevent the wrongful use of drones, which has been increasing due to many people being unfamiliar with regulations, especially tourists from abroad. Under the civil aeronautics law, a drone of 200 grams or more cannot be operated in airspace around airports or residential areas without permission from the government. In addition, the law regulating the use of drones bans flights in airspace near designated important places such as the Prime Minister's Office, the Imperial Palace and nuclear power plants. Foreign tourists and others unfamiliar with the laws continue to violate them. In 2019, 14 foreign nationals had their cases sent to prosecutors, as of Nov. 20.
A plan to remove fuel debris from the primary containment vessel of a reactor at the Fukushima No. 1 nuclear power plant is expected to be further pushed back after it became apparent that Tokyo Electric Power Company Holdings Ltd. will not be able to conduct an internal probe -- a key step to start removing the fuel debris -- by the end of March as planned. The internal probe would involve using remote-controlled robots to collect fuel debris inside the No. 1 reactor so Tepco can examine its composition and form. Tepco's plan is to open three holes in both the outer and inner doors of the primary containment vessel using pressurized water mixed with a polishing agent. After it succeeded in opening three holes in the outer door, Tepco started drilling a hole in the inner door in June 2019. But that procedure caused the concentration of radioactive dust to increase temporarily, prompting staff to suspend work.
The drone attack claimed by Yemeni rebels on key Saudi Arabian oil refineries that took place on September 14, 2019 has brought the powerful technology back into the news. Unfortunately, the strikes that disrupted roughly 5% of the world's oil supply has also contributed more ammunition to the overarching negative connotations the word "drone" conjures. "Drone" is a very broad term. Colloquially, drones are usually thought of as remote-piloted flying devices used by militaries for surveillance and offensive tactics or by civilians for recreational or business purposes. Merriam-Webster defines it as "an unmanned aircraft or ship guided by remote control or onboard computers."
Wildlife is flourishing in the exclusion zone around the disabled Fukushima Daichii nuclear reactor in Japan, images from remotely-operated cameras have revealed. Researchers spotted more than 20 species in areas around the reactor, including wild boar, macaques and fox-like raccoon dogs. The findings help reveal how wildlife populations respond in the wake of catastrophic nuclear disaster like those that occurred at Fukushima and Chernobyl. Humans were evacuated from certain zones around the the Fukushima reactor following radiation leaks caused by the Tōhoku earthquake and tsunami of 2011. Wildlife ecologist James Beasley of the University of Georgia, in the US, and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area around the Fukushima Daiichi power plant over a four-month period.
It had already been an eventful day in Iran: The country had just launched missiles at United States forces based in Iraq and an airliner carrying at least 176 people crashed shortly after takeoff from Tehran on Wednesday, killing everyone on board. Then just before dawn, a 4.5-magnitude earthquake struck southern Iran at a depth of about six miles, the United States Geological Survey reported, in the same region as the troubled Bushehr nuclear power plant. It struck just as Iranian leaders were trumpeting their strike on two Iraqi bases housing United States forces, in retaliation for last week's American drone strike that killed Maj. No casualties were immediately reported, but rescue teams were working at the site, Jahangir Dehqani, managing director of the Bushehr crisis management agency, told the state-run IRNA news agency. The quake was reported about 30 miles from the Russian-built Bushehr nuclear plant.
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.