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Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
Terrestrial Energy, Schneider partner on molten salt reactor
Terrestrial Energy and Schneider Electric are teaming to deploy Terrestrial Energy's integral molten salt reactor (IMSR) to provide zero-emission power to industrial facilities and large data centers.
The companies signed a memorandum of understanding in April to jointly develop commercial opportunities with high-energy users looking for reliable, affordable, and zero-carbon baseload supply. Terrestrial Energy said that working with Schneider “offers solutions to the major energy challenges faced by data center operators and many heavy industries operating a wide range of industrial processes such as hydrogen, ammonia, aluminum, and steel production.”
Lei Jin, Kaushik Banerjee
Nuclear Science and Engineering | Volume 194 | Number 3 | March 2020 | Pages 190-206
Technical Paper | doi.org/10.1080/00295639.2019.1678104
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. These new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.