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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
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
Argonne researching “climate-ready” nuclear plant design
Scientists at Argonne National Laboratory have partnered with Washington state–based Energy Northwest to look at alternative ways to cool nuclear reactors as climate change impacts relied-upon water sources.
Jan Dufek, Waclaw Gudowski
Nuclear Science and Engineering | Volume 152 | Number 3 | March 2006 | Pages 274-283
Technical Paper | doi.org/10.13182/NSE06-2
Articles are hosted by Taylor and Francis Online.
A new adaptive stochastic approximation method for an efficient Monte Carlo calculation of steady-state conditions in thermal reactor cores is described. The core conditions that we consider are spatial distributions of power, neutron flux, coolant density, and strongly absorbing fission products like 135Xe. These distributions relate to each other; thus, the steady-state conditions are described by a system of nonlinear equations. When a Monte Carlo method is used to evaluate the power or neutron flux, then the task turns to a nonlinear stochastic root-finding problem that is usually solved in the iterative manner by stochastic optimization methods. One of those methods is stochastic approximation where efficiency depends on a sequence of stepsize and sample size parameters. The stepsize generation is often based on the well-known Robbins-Monro algorithm; however, the efficient generation of the sample size (number of neutrons simulated at each iteration step) was not published yet. The proposed method controls both the stepsize and the sample size in an efficient way; according to the results, the method reaches the highest possible convergence rate.