High energy neutron transmission analysis of dry cask storage

Greulich, Christopher; Hughes, Christopher R.; Gao, Yuan; Enqvist, Andreas; Baciak, James E.

doi:10.1016/j.nima.2017.08.014

“…A brief dimensional analysis can provide insight into how source terms are defined in the NTE. Since the neutron transport equation describes the number of neutrons in a volume at a point in time, then the units must be , (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) which confirms the previous statement. Therefore, any source term must have these same units.…”

Section: External Neutron Sourcessupporting

confidence: 74%

“…3-13 becomes the steady-state neutron transport equation, ,Ω). (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) Even after eliminating the partial derivative in time, Eqn. 3-13 is still not tractable due to the three spatial derivatives and three integrals.…”

Section: Treatment Of Time Dependencementioning

confidence: 99%

Analytic Modeling of a Deep Shielding Problem

Remedes

¹

2020

0

View full text Add to dashboard Cite

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

show abstract

“…al. state the cost to re-open a cask could be in the millions of dollars and require man-months of time [4]. The process of opening a cask to visually inspect the contents also carries an increased risk of exposing workers to radiation.…”

Section: If a Cask Inadequately Performs Any Of The Above Functions mentioning

confidence: 99%

“…Analyzing the capabilities of technology to ensure the contents of a spent fuel cask has motivated many scientific investigations, with a large reliance on computational simulations [4][5][6]. Simulation results can then be correlated to experimental observations in order to identify promising techniques to inspect the interior of a cask without opening the cask.…”

Section: If a Cask Inadequately Performs Any Of The Above Functions mentioning

confidence: 99%

See 1 more Smart Citation

Analytic Modeling of a Deep Shielding Problem

Remedes

¹

2020

0

View full text Add to dashboard Cite

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

show abstract