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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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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July 2025
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Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
Arsen S. Iskhakov, Cheng-Kai Tai, Igor A. Bolotnov, Tri Nguyen, Elia Merzari, Dillon R. Shaver, Nam T. Dinh
Nuclear Technology | Volume 210 | Number 7 | July 2024 | Pages 1167-1184
Research Article | doi.org/10.1080/00295450.2023.2185056
Articles are hosted by Taylor and Francis Online.
Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. This work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.