Accommodation processes in irradiated steel 12Х18Н10Т during plastic deformation in the temperature range of “dry” SNF storage

住宿 辐照 材料科学 大气温度范围 航程(航空) 复合材料 气象学 光学 地理 核物理学 物理
作者
Tamara Aldabergenova,Serik Akayev,А S Dikov,A. S. Larionov,Lyubov Dikova
出处
期刊:Habaršysy - A̋l-Farabi atyndaġy K̦azak̦ memlekettik u̇lttyk̦ universitetì. Fizika seriâsy [al-Farabi Kazakh National University]
卷期号:91 (4)
标识
DOI:10.26577/rcph.2024.v91.i4.a5
摘要

This paper presents a detailed study of the fracture surfaces of 12Kh18N10T austenitic stainless-steel specimens subjected to short-term mechanical testing at various temperatures: 24°C, 350°C, and 450°C. The specimens for the study were fabricated from spent nuclear fuel of the BN-350 reactor. The research was conducted within the framework of a multi-level approach to physical mesomechanics, which allowed for a deeper understanding of the complex processes occurring in the material under different temperature regimes. In particular, a thorough analysis of the changes in the steel's plasticity with increasing test temperature was carried out, revealing a significant dependence of this parameter on the conditions of deformation localization. It was shown that the reduction in plasticity with increasing test temperature is associated with a quasi-uniform distribution of stresses in the zones of deformation localization. These zones of local stress concentration were caused by processes that led to an increase in the material's porosity, which, in turn, was due to accommodation processes of the rotational type. Thus, the study demonstrated the importance of considering local changes in the material's structure, which can significantly affect its mechanical properties under varying operating conditions. The obtained results may contribute to a deeper understanding of the processes occurring in austenitic steels used in extreme conditions and aid in the development of more reliable materials for use in nuclear and other high-stress systems.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
乐乐应助ZJL采纳,获得10
1秒前
sui瓶完成签到,获得积分10
1秒前
才疏学浅完成签到 ,获得积分10
1秒前
清新的向日葵完成签到,获得积分20
1秒前
3秒前
3秒前
tt发布了新的文献求助10
3秒前
浅浅完成签到,获得积分10
4秒前
4秒前
机智的雁荷完成签到 ,获得积分10
4秒前
5秒前
6秒前
tingi发布了新的文献求助10
6秒前
由于发布了新的文献求助10
6秒前
7秒前
7秒前
LlLly发布了新的文献求助10
7秒前
7秒前
WSYang完成签到,获得积分0
7秒前
憨憨完成签到,获得积分10
8秒前
Rowe完成签到,获得积分10
8秒前
8秒前
8秒前
orixero应助111采纳,获得10
8秒前
8秒前
浅浅发布了新的文献求助50
9秒前
9秒前
KRYSTAL发布了新的文献求助10
10秒前
憨憨发布了新的文献求助10
10秒前
10秒前
11秒前
Garden发布了新的文献求助10
11秒前
Shayulajiao发布了新的文献求助10
11秒前
烟花应助胡宇轩采纳,获得10
11秒前
隐形曼青应助小磊采纳,获得10
12秒前
yuan完成签到,获得积分10
12秒前
地球发布了新的文献求助10
12秒前
亮亮发布了新的文献求助30
12秒前
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443241
求助须知:如何正确求助?哪些是违规求助? 8257113
关于积分的说明 17585207
捐赠科研通 5501710
什么是DOI,文献DOI怎么找? 2900830
邀请新用户注册赠送积分活动 1877821
关于科研通互助平台的介绍 1717487