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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Max Planck’s ELISE reaches record values for ITER plasma heating
The Max Planck Institute for Plasma Physics (IPP) announced that it recently has achieved a new record for ion current density for neutral particle heating at its ELISE (Extraction from a Large Ion Source Experiment) experimental testing facility in Garching, Germany. ELISE is being used to test neutral beam injection (NBI) systems that will be used to heat the plasma of the ITER fusion experiment in France.
M. Marseguerra, M. E. Ricotti, E. Zio
Nuclear Science and Engineering | Volume 124 | Number 2 | October 1996 | Pages 339-348
Techniacl Paper | doi.org/10.13182/NSE96-A28583
Articles are hosted by Taylor and Francis Online.
The early detection of incipient failures is of paramount importance for the safety and reliability of nuclear power plants. The feasibility of using artificial neural networks as process simulators in a fault detection device is explored. Two neural networks are trained to follow the dynamic evolution of the system pressure in a nonfaulty pressurizer of a pressurized water reactor. During an accident, the discrepancy between the plant’s signals and the neural networks’predictions can be used to rapidly detect the faulty condition. In reality, the signals will be unavoidably affected by a certain level of noise. The robustness of neural networks to noisy patterns assures a satisfactory degree of accuracy in the process predictions and, therefore, a high efficiency in the detection as well.