ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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!
Latest Magazine Issues
May 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
July 2025
Nuclear Technology
June 2025
Fusion Science and Technology
Latest News
High-temperature plumbing and advanced reactors
The use of nuclear fission power and its role in impacting climate change is hotly debated. Fission advocates argue that short-term solutions would involve the rapid deployment of Gen III+ nuclear reactors, like Vogtle-3 and -4, while long-term climate change impact would rely on the creation and implementation of Gen IV reactors, “inherently safe” reactors that use passive laws of physics and chemistry rather than active controls such as valves and pumps to operate safely. While Gen IV reactors vary in many ways, one thing unites nearly all of them: the use of exotic, high-temperature coolants. These fluids, like molten salts and liquid metals, can enable reactor engineers to design much safer nuclear reactors—ultimately because the boiling point of each fluid is extremely high. Fluids that remain liquid over large temperature ranges can provide good heat transfer through many demanding conditions, all with minimal pressurization. Although the most apparent use for these fluids is advanced fission power, they have the potential to be applied to other power generation sources such as fusion, thermal storage, solar, or high-temperature process heat.1–3
Nathan W. Porter, Vincent A. Mousseau, Maria N. Avramova
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1607-1617
Technical Paper | doi.org/10.1080/00295450.2018.1548221
Articles are hosted by Taylor and Francis Online.
This paper introduces a framework for model selection that includes parameter estimation, uncertainty propagation, and quantified validation. The framework is applied to single-phase turbulent friction modeling in CTF, which is a thermal-hydraulic code for nuclear engineering applications. The friction model is chosen because it is well understood and easy to separate from other physics, which allows focus to be on the model selection framework instead of on the particulars of the chosen model. Two different empirical models are compared: the McAdams Correlation and the Simplified McAdams Correlation. The parameter estimation is performed by calibrating each of the friction models to experimental data using the Delayed Rejection Adaptive Metropolis algorithm, which is a Markov Chain Monte Carlo method. State point uncertainties are also considered, which are determined based on measurement errors from the experiment. The input parameter distributions are propagated through CTF using a statistical method with samples. A variety of validation metrics is used to quantify which empirical model is more accurate. It is shown that model form uncertainty can be quantified using validation once all other sources of uncertainty—numerical, sampling, experimental, and parameter—have been quantitatively addressed. When multiple models are available, the one that has the smallest model form error can be selected. Though the framework is applied to a simple example here, the same process can quantify the model form uncertainty of more complicated physics, multiple models, and simulation tools in other fields. Therefore, this work is a demonstration of best practices for future assessments of model form uncertainty.