Uncertainty Quantification of Concrete Utilized in Dry Cask Storage

Kelly, Ryan; Tsvetkov, Pavel V.; Chirayath, Sunil S.; Poston, J.W.; Kitcher, Evans D.

doi:10.13182/nt13-155

Cited by 6 publications

(1 citation statement)

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“…The results from their analytical modeling agreed with corresponding MCNP simulations. HI-STORM 100S (a variant of the HI-STORM cask) spent fuel cask based on variabilities in concrete composition and density [28]. The authors state that density variations in the concrete have the largest effect on radiation shielding capabilities.…”

Section: Both Simulation Tools Are Developed As Part Of the Standardimentioning

confidence: 99%

Analytic Modeling of a Deep Shielding Problem

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2020

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Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the groundwork for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than "building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of MCNP, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate

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Section: Both Simulation Tools Are Developed As Part Of the Standardimentioning

confidence: 99%