Farmland in Fukushima that was rendered unusable after the disastrous 2011 nuclear meltdown is getting a second chance at productivity. A group of Japanese investors have created a new plan to use the abandoned land to build wind and solar power plants, to be used to send electricity to Tokyo. The plan calls for the construction of eleven solar power plants and ten wind power plants, at an estimated cost of $2.75 billion. Fukushima has been aggressively converting land damaged by the 2011 meltdown, such as this golf course (pictured above) into a source of renewable energy. A new $2.75 billion plan will add eleven new solar plants and ten wind power plants to former farmland The project is expected to be completed in March of 2024 and is backed by a group of investors, including Development Bank of Japan and Mizuho Bank.
Recent developments in SCADA (Supervisory Control and Data Acquisition) systems for physical infrastructure, such as high pressure gas pipeline systems and electric grids, have generated enormous amounts of time series data. This data brings great opportunities for advanced knowledge discovery and data mining methods to identify system failures faster and earlier than operation experts. This paper presents our effort in collaboration with a utility company to solve a grand challenge; namely, to use advanced data mining methods to detect leaks on a high pressure gas transmission system. Leak detection models with unsupervised learning tasks were developed analyzing billions of data records to identify leaks of different sizes and impacts, with very low false positive rates. In particular, our solution was able to identify small leaks leading to rupture events. The model also identified small leaks not identifiable with current detection systems. Such high-fidelity early identification enables operation personnel to take preventive measures against possible catastrophic events. We then formulate several generic detection methods with models derived from time series anomaly detection methods. We show that our leak detection models are superior to the SCADA alarm system, a mass balance model and other generic time series anomaly detection models in terms of both detection accuracy and computation time.
DUBAI, UNITED ARAB EMIRATES - A Yemen rebel drone strike this week on a critical Saudi oil pipeline shows that the otherwise-peaceful sandy reaches of the Arabian Peninsula now are at risk of similar assault, including an under-construction nuclear power plant and Dubai International Airport, among the world's busiest. U.N. investigators said the Houthis' new UAV-X drone, found in recent months during the Saudi-led coalition's war in Yemen, likely has a range of up to 1,500 km (930 miles). That puts the far reaches of both Saudi Arabia and the United Arab Emirates, the two main opponents of the Iranian-allied Houthi rebels in Yemen, within reach of drones difficult to detect and track. Their relatively simple design, coupled with readily available information online, makes targeting even easier, analysts say. "These installations are easily findable, like on Google Earth," said Tim Michetti, an expert on illicit weapons technology with experience in Yemen.
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.
U.S. stocks were slightly higher Wednesday morning as utility companies climbed. Energy companies were trading lower as the price of oil continued to slip. Stocks are at their lowest levels in two months after large losses in two of the last three days. The Dow Jones industrial average advanced 31 points, or 0.2%, to 18,097 as of 10:05 a.m. The Standard & Poor's 500 index rose 5 points, or 0.2%, to 2,132.