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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.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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
BREAKING NEWS: Trump issues executive orders to overhaul nuclear industry
The Trump administration issued four executive orders today aimed at boosting domestic nuclear deployment ahead of significant growth in projected energy demand in the coming decades.
During a live signing in the Oval Office, President Donald Trump called nuclear “a hot industry,” adding, “It’s a brilliant industry. [But] you’ve got to do it right. It’s become very safe and environmental.”
Federico Di Rocco, Dan G. Cacuci, Madalina C. Badea
Nuclear Science and Engineering | Volume 185 | Number 3 | March 2017 | Pages 549-603
Technical Paper | doi.org/10.1080/00295639.2017.1279943
Articles are hosted by Taylor and Francis Online.
This paper provides the results of the adjoint sensitivity model developed in the accompanying Part I for a natural draft counter-flow cooling tower. The selected responses are (1) outlet air temperature, (2) outlet water temperature, (3) outlet water mass flow rate, (4) air outlet relative humidity, and (5) air mass flow rate. Explicit expressions for the best-estimate nominal values of the model parameters and responses are also provided, together with the best-estimate reduced standard deviations of the predicted model parameters and responses. The results stemming from this work show that the PM_CMPS procedure reduces the predicted standard deviations of all responses and model parameters.