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Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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Nuclear Science and Engineering
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Nuclear Technology
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Latest News
El Salvador: Looking to nuclear
In 2022, El Salvador’s leadership decided to expand its modest, mostly hydro- and geothermal-based electricity system, which is supported by expensive imported natural gas and diesel generation. They chose to use advanced nuclear reactors, preferably fueled by thorium-based fuels, to power their civilian efforts. The choice of thorium was made to inform the world that the reactor program was for civilian purposes only, and so they chose a fuel that was plentiful, easy to source and work with, and not a proliferation risk.
Paul R. Miles, Jared A. Cook, Zoey V. Angers, Christopher J. Swenson, Brian C. Kiedrowski, John Mattingly, Ralph C. Smith
Nuclear Technology | Volume 207 | Number 1 | January 2021 | Pages 37-53
Technical Paper | doi.org/10.1080/00295450.2020.1738796
Articles are hosted by Taylor and Francis Online.
Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location () as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source’s location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.