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使用表面复制模具对乏核燃料罐表面粗糙度进行表征。

Characterization of spent nuclear fuel canister surface roughness using surface replicating molds.

作者信息

Nation B L, Faubel J L, Vice G T, Ohlhausen J A, Durbin S, Bryan C R, Knight A W

机构信息

Nuclear Energy Fuel Cycle Storage and Transportation Technology, Sandia National Laboratories, 1515 Eubank Blvd SE, Albuquerque, NM, 87123, USA.

Materials Characterization and Performance, Sandia National Laboratories, 1515 Eubank Blvd SE, Albuquerque, NM, 87123, USA.

出版信息

Sci Rep. 2024 Oct 1;14(1):22845. doi: 10.1038/s41598-024-71992-1.

DOI:10.1038/s41598-024-71992-1
PMID:39354084
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11445515/
Abstract

In this study we present a replication method to determine surface roughness and to identify surface features when a sample cannot be directly analyzed by conventional techniques. As a demonstration, this method was applied to an unused spent nuclear fuel dry storage canister to determine variation across different surface features. In this study, an initial material down-selection was performed to determine the best molding agent and determined that non-modified Polytek PlatSil23-75 provided the most accurate representation of the surface while providing good usability. Other materials that were considered include Polygel Brush-On 35 polyurethane rubber (with and without Pol-ease 2300 release agent), Polytek PlatSil73-25 silicone rubber (with and without PlatThix thickening agent and Pol-ease 2300 release agent), and Express STD vinylpolysiloxane impression putty. The ability of PlatSil73-25 to create an accurate surface replica was evaluated by creating surface molds of several locations on surface roughness standards representing ISO grade surfaces N, N, N, and N. Overall, the molds were able to accurately reproduce the expected roughness average (R) values, but systematically over-estimated the peak-valley maximum roughness (R) values. Using a 3D printed sample cell, several locations across the stainless steel spent nuclear fuel canister were sampled to determine the surface roughness. These measurements provided information regarding variability in normal surface roughness across the canister as well as a detailed evaluation on specific surface features (e.g., welds, grind marks, etc.). The results of these measurements can support development of dry storage canister ageing management programs, as surface roughness is an important factor for surface dust deposition and accumulation. This method can be applied more broadly to different surfaces beyond stainless steel to provide rapid, accurate surface replications for analytical evaluation by profilometry.

摘要

在本研究中,我们提出了一种复制方法,用于在无法通过传统技术直接分析样品时确定表面粗糙度并识别表面特征。作为演示,该方法应用于一个未使用过的乏核燃料干式储存罐,以确定不同表面特征的变化情况。在本研究中,首先进行了材料初选,以确定最佳成型剂,并确定未改性的Polytek PlatSil23 - 75在提供良好可用性的同时,能最准确地呈现表面情况。考虑的其他材料包括Polygel Brush - On 35聚氨酯橡胶(使用和不使用Pol - ease 2300脱模剂)、Polytek PlatSil73 - 25硅橡胶(使用和不使用PlatThix增稠剂和Pol - ease 2300脱模剂)以及Express STD乙烯基聚硅氧烷印模腻子。通过在代表ISO等级表面N、N、N和N的表面粗糙度标准上的几个位置制作表面模具,评估了PlatSil73 - 25制作精确表面复制品的能力。总体而言,这些模具能够准确再现预期的粗糙度平均值(R),但系统地高估了峰谷最大粗糙度(R)值。使用3D打印的样品池,对不锈钢乏核燃料罐上的几个位置进行了采样,以确定表面粗糙度。这些测量提供了有关整个罐体正常表面粗糙度变化的信息,以及对特定表面特征(如焊缝、磨削痕迹等)的详细评估。这些测量结果可为干式储存罐老化管理程序的开发提供支持,因为表面粗糙度是表面灰尘沉积和积累的一个重要因素。该方法可更广泛地应用于不锈钢以外的不同表面,以通过轮廓仪提供快速、准确的表面复制用于分析评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/62938d282e76/41598_2024_71992_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/ad732c77eb93/41598_2024_71992_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/3d4255c58fb6/41598_2024_71992_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/dcb6d8313767/41598_2024_71992_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/85c1dd8b6e74/41598_2024_71992_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/40edd952de7d/41598_2024_71992_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/62938d282e76/41598_2024_71992_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/ad732c77eb93/41598_2024_71992_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/3d4255c58fb6/41598_2024_71992_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/dcb6d8313767/41598_2024_71992_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/85c1dd8b6e74/41598_2024_71992_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/40edd952de7d/41598_2024_71992_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad9/11445515/62938d282e76/41598_2024_71992_Fig6_HTML.jpg

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