Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
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Article Number | 01005 | |
Number of page(s) | 1 | |
Section | Plenary Talks | |
DOI | https://doi.org/10.1051/epjconf/202430201005 | |
Published online | 15 October 2024 |
https://doi.org/10.1051/epjconf/202430201005
Unearthing the Chicago Piles: A Monte Carlo perspective
Université Paris-Saclay, CEA, SERMA, 91191 Gif-sur-Yvette, France
* Corresponding author: andrea.zoia@cea.fr
† Corresponding author: davide.mancusi@cea.fr
Published online: 15 October 2024
The divergence of the Chicago Pile 1 (CP-1) on December 2nd, 1942, marks the birth of the nuclear age. Built under the West Stands of the Stagg Field in Chicago in slightly over two weeks, CP-1 was the first man-made nuclear reactor, conceived as a proof-of-principle physics experiment to demonstrate the feasibility of a self-sustained neutron chain reaction. The fuel consisted in a mixture of uranium oxide and metal uranium, with lumps of several sizes and shapes stacked within bricks of graphite used as moderator to form a cubic lattice. After only three months of operation, CP-1 was dismantled due to safety reasons and rebuilt at the Argonne Site in twenty-one days, under the name of Chicago Pile 2 (CP-2): the new pile had a longer life, contributing vastly to research on material science and nuclear reactor theory, and was finally decommissioned in 1954. Prompted by the eightieth anniversary of the Chicago Pile(s), in this talk we celebrate these astonishing scientific artifacts by building fully detailed three-dimensional digital twins of CP-1 and CP-2 using TRIPOLI-4®, the Monte Carlo code developed at CEA. Technological data for the models have been retrieved from published and unpublished documents of the Manhattan Project. We compare the simulation results to the available experimental measurements, encompassing the reactivity of the piles, the control rod worth, the kinetics parameters and several reactivity coefficients. For each, we examine the associated uncertainties due to missing or incomplete technological data as well as the bias due to nuclear data libraries.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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