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Division Spotlight
Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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.”
Richard Sanchez, Simone Santandrea
Nuclear Science and Engineering | Volume 183 | Number 2 | June 2016 | Pages 196-213
Technical Paper | doi.org/10.13182/NSE15-78
Articles are hosted by Taylor and Francis Online.
A conservative linear surface approximation (CLS) has been recently introduced to speed up the method of characteristics in unstructured meshes. In this work, we present an analysis of the convergence of the CLS in unstructured geometries, which shows that, under optimal conditions, the method converges quadratically with the size of the regions, while the classical step characteristics approximation converges linearly. The predicted convergence rates apply only to a homogeneous convex domain with a regular boundary and regular sources and can be viewed as upper bounds for realistic heterogeneous cases. We also analyze the errors induced by the numerical implementation of the step and CLS approximations and show their impact in the final error. Numerical calculations illustrate the convergence rates.