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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 Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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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Fusion Science and Technology
Latest News
Demolition work continues near former Hanford processing facility
Workers with the Department of Energy Office of Environmental Management’s contractor Central Plateau Cleanup Company recently demolished the Reduction Oxidation Plant, one of five former plutonium production facilities at the Hanford Site in Washington state.
Glen R. Longhurst, James Cleaver
Fusion Science and Technology | Volume 54 | Number 2 | August 2008 | Pages 565-568
Technical Paper | Materials Interactions | doi.org/10.13182/FST08-A1879
Articles are hosted by Taylor and Francis Online.
Understanding and accounting for interaction of hydrogen isotopes and their oxides with metal surfaces is important for persons working with tritium systems. Reported data from several investigators have shown that the processes of oxidation, adsorption, absorption, and permeation are all coupled and interactive. A computer model has been developed for predicting the interaction of hydrogen isotopes and their corresponding oxides in a flowing carrier gas stream with the walls of a metallic tube, particularly at low hydrogen concentrations. An experiment has been constructed to validate the predictive model. Predictions from modeling lead to unexpected experiment results.