A comparison of dose rate calculations for a spent fuel storage cask by using MCNP and SAS4

Chen, A.Y.; Chen, Y.F.; Wang, J.N.; Sheu, Rong-Jiun; Liu, Y.-W.H.; Jiang, Shiang‐Huei

doi:10.1016/j.anucene.2008.08.009

Cited by 8 publications

(2 citation statements)

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“…MCNP has also undergone general validation in multiple disciplines within nuclear engineering; including but not limited to radiation shielding [19], criticality [20], and intermediate and high-energy physics [21] where MCNP results were compared to simple experiments. In order to validate computational tools as applied to spent fuel casks, scientist have turned to a comparative method where results from other radiation transport codes are compared with MCNP [22,23]. However, discrepancies between results from different simulation tools are attributed to different physics being included in each tool.…”

Section: State Of Current Used Fuel Cask Researchmentioning

confidence: 99%

Analytic Modeling of a Deep Shielding Problem

Remedes

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: State Of Current Used Fuel Cask Researchmentioning

confidence: 99%

Analytic Modeling of a Deep Shielding Problem

Remedes

2020

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“…Chen et al [12] made an accuracy and computational efficiency comparison of dose rate calculations for a spent fuel storage cask using the MCNP and SAS4 (Shielding Analysis Sequence 4) computer simulation codes, whose main differences are the cross-section libraries and the imbedded variance reduction techniques MCNP has. In this comparison, MCNP's overall performance was better, although SAS4 was more efficient when the only concern was the dose rate measured at the side of the cask.…”

Section: Modeling Of Storage Costsmentioning

confidence: 99%

A Methodology for Optimizing the Management of Spent Fuel of Nuclear Power Plants Using Dry Storage Casks

Gomes

Saldanha

Alvim

2019

Science and Technology of Nuclear Installations

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The management of spent nuclear fuel assemblies of nuclear reactors is a priority subject among member states of the International Atomic Energy Agency. For the majority of these countries, the destination of such fuel assemblies is a decision that is yet to be made and the “wait-and-see” policy is thus adopted by them. In this case, the irradiated fuel is stored in on-site spent fuel pools until the power plant is decommissioned or, when there is no more racking space in the pool, they are stored in intermediate storage facilities, which can be another pool or dry storage systems, until the final decision is made. The objective of this study is to propose a methodology that, using optimization algorithms, determines the ideal time for removal of the fuel assemblies from the spent fuel pool and to place them into dry casks for intermediate storage. In this scenario, the methodology allows for the optimal dimensioning of the designed spent fuel pools and the casks’ characteristics, thus reducing the final costs for purchasing new Nuclear Power Plants (NPP), as the size and safety features of the pool could be reduced and dry casks, that would be needed anyway after the decommissioning of the plant, could be purchased with optimal costs. To demonstrate the steps involved in the proposed methodology, an example is given, one which uses the Monte Carlo N-Particle code (MCNP) to calculate the shielding requirements for a simplified model of a concrete dry cask. From the given example, it is possible to see that, using real-life data, the proposed methodology can become a valuable tool to help making nuclear energy a more attractive choice costwise.

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