High Temperature Deformation Constitutive Model of Superalloy Inconel 617B for Nuclear Power

doi:10.3901/JME.2021.22.218

Abstract

Abstract: The performance of lithium-ion batteries decays sharply in a low-temperature environment, which restricts the promotion and application of electric vehicles in all-climate conditions. Based on an electric triggered extremely fast heating (XFH) system, the modeling method of electrothermal characteristics is researched. The electrical characteristics of the heating system are characterized, and then the finite element model of heat generation and thermal diffusion of the battery considering material anisotropy is established. The experimental verification indicated that the current error is less than 98.2 mA, and the temperature rise error is less than 4.09%. The influence of duty cycle and initial SOC on the heating performance is clarified by simulation, furthermore, the heating consistency of battery pack during the heating process is studied. The results show that the battery can be heated from -20℃ to 20℃ in 270 s, and the maximum temperature difference is less than 3.94℃. The effect of heating inconsistency on the battery cell inconsistency and the control parameters of the heating system is analyzed. The results show that there is a positive linear correlation between the temperature rise inconsistency and the internal resistance standard deviation, and it is significantly affected by the control frequency and duty cycle. The influence of duty ratio on temperature rise is up to 15%.

Key words: superalloy inconel 617B, hot deformation, constitutive model, BP artificial neural network

CLC Number:

TG301

LUO Rui, CUI Shugang, CHEN Leli, LIU Tian, CAO Yun, GAO Pei, QIU Yu, CHENG Xiaonong. High Temperature Deformation Constitutive Model of Superalloy Inconel 617B for Nuclear Power[J]. Journal of Mechanical Engineering, 2021, 57(22): 218-225.

References

[1] GUO Yan, WANG Bohan, HOU Shufang. Aging precipitation behavior and mechanical properties of inconel 617 superalloy[J]. Acta Metallurgica Sinica(English Letters), 2013, 26(3):307-312.
[2] 党莹樱, 赵新宝, 尹宏飞, 等. Inconel 617合金持久强度的评估[J]. 中国电机工程学报, 2014, 34(S1):172-175. DANG Yingying, ZHAO Xinbao, YIN Hongfei, et al. Assessment of creep-rupture strength for inconel 617alloy[J]. Proceedings of the CSEE, 2014, 34(S1):172-175.
[3] HARI P R, ARIVAZHAGAN N, RAO M N, et al. Comparative studies on corrosion of alloy 617 OCC at700℃in air and simulated coal ash environments[J]. Materials Today:Proceedings, 2018, 5(5):11452-11459.
[4] REN Weiju, SWIMDEMAN R. A review paper on aging effects in alloy 617 for gen IV nuclear reactor applications[J]. Journal of Pressure Vessel Technology, 2009, 131(2):15.
[5] REN Weiju, SWIMDEMAN R. A review of aging effects in alloy 617 for Gen IV nuclear reactor applications[J]. American Society of Mechanical Engineers Pressure Vessels and Piping Division Pvp, 2006(2):024002-024016.
[6] 杨康, 祝志超, 张雪姣, 等. 镍基617合金的热变形和动态再结晶行为[J]. 材料热处理学报, 2019, 40(10):151-157. YANG Kang, ZHU Zhichao, ZHANG Xuejiao, et al. Hot deformation and dynamic recrystallization behavior of nickel-based alloy 617[J]. Transactions of Materials and Heat Treatment, 2019, 40(10):151-157.
[7] SAMANTARAY D, MANDAL S, BHADURI A K. Acomparative study on Johnson Cook, modified Zerilli-Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr-1Mo steel[J]. Computational Materials Science, 2009, 47:568-576.
[8] DAI Qingsong, DENG Yunlai, TANG Jianguo, et al. Deformation characteristics and strain-compensated constitutive equation for AA5083 aluminum alloy under hot compression[J]. Transactions of Nonferrous Metals Society of China, 2019, 29:2252-2261.
[9] 程晓农, 桂香, 罗锐, 等. 核电装备用奥氏体不锈钢的高温本构模型及动态再结晶[J]. 材料导报, 2019, 33(11):1775-1781. CHENG Xiaonong, GUI Xiang, LUO Rui, et al. Constitutive equation and dynamic recrystallization behavior of 316L Austenitic stainless steel for nuclear power equipment[J]. Materials Reports, 2019, 33(11):1775-1781.
[10] GENG Peihao, QIN Guoliang, ZHOU Jun, et al. Hot deformation behavior and constitutive model of GH4169superalloy for linear friction welding process[J]. Journal of Manufacturing Processes, 2018, 32:469-481.
[11] CAI Zhongman, JI Hongchao, PEI Weichi, et al. Hot workability, constitutive model and processing map of3Cr23Ni8Mn3N heat resistant steel[J]. Vacuum, 2019, 165:324-336.
[12] 江洋, 王宝雨, 霍元明, 等. 25Cr Mo4钢热压缩变形行为及流变应力本构方程[J]. 塑性工程学报, 2020, 27(5):167-173. JIANG Yang, WANG Baoyu, HUO Yuanming, et al. Thermal compressive deformation behavior and flow stress constitutive equation of 25Cr Mo4 steel[J]. Journal of Plasticity Engineering, 2020, 27(5):167-173.
[13] 易茵菲. 应变硬化指数、应变速度敏感性与材料的塑性变形能力[J]. 上海金属(有色分册), 1982(4):45-55. YI Yinfei. Strain hardening index, strain velocity sensitivity and plastic deformation ability of materials[J]. Shanghai Metals(Nonferrous Fascicule), 1982(4):45-55.
[14] SRINIBASU G, RAO R N, NANDY T K, et al. Artificial neural network approach for prediction of titanium alloy stress-strain curve[J]. Procedia Engineering, 2012, 38:3709-3714.
[15] 王涛, 万志鹏, 孙宇, 等. 镍基变形高温合金动态软化行为与组织演变规律研究[J]. 金属学报, 2018, 54(1):83-92. WANG Tao, WAN Zhipeng, SUN Yu, et al. Dynamic softening behavior and microstructure evolution of nickel base superalloy[J]. Acta Metallurgica Sinica, 2018, 54(1):83-92.
[16] 周计明, 齐乐华, 陈国定. 热成形中金属本构关系建模方法综述[J]. 机械科学与技术, 2005, 24(2):212-216. ZHOU Jiming, QI Lehua, CHEN Guoding. Investigation on the constitutive relationship of materials forming in high temperature[J]. Mechanical Science and Technology, 2005, 24(2):212-216.
[17] SELLARS C M, MCTEGART W J. On the mechanism of hot deformation[J]. Acta Metallurgica, 1966, 14(9):1136-1138.
[18] WANG Yitao, LI Jianbo, XIN Yunchang, et al. Effect of Zener-Hollomon parameter on hot deformation behavior of Co Cr Fe Mn Ni C0. 5 high entropy alloy[J]. Materials Science and Engineering A, 2019, 768:138483.
[19] ZHOU Jia, WAN Xinming, ZHANG Junping, et al. Modeling of constitutive relationship of aluminum alloy based on BP neural network model[J]. Materials Today:Proceedings, 2015, 2:5023-5028.
[20] LIN Y C, ZHANG Jun, ZHONG Jue. Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel[J]. Computational Materials Science, 2008, 43:752-758.
[21] 刘俊林. 基于BP神经网络的2. 25Cr1Mo0. 25V钢高温流变应力模型研究[D]. 太原:太原科技大学, 2014. LIU Junlin. Research on high temperature flow stress model of 2. 25Cr1Mo0. 25V steel based on BP neural network[D]. Taiyuan:Taiyuan University of Science and Technology, 2014.
[22] 孙宇, 曾卫东, 王邵丽, 等. 应用人工神经网络建立Ti-22Al-25Nb合金高温本构关系模型[J]. 塑性工程学报, 2009, 16(3):126-129. SUN Yu, ZENG Weidong, WANG Shaoli, et al. Modeling the constitutive relationship of Ti-22Al-25Nb alloy using artificial neural network[J]. Journal of Plasticity Engineering, 2009, 16(3):126-129.
[23] LI Hongying, WEI Dongdong, LI Yanghua, et al. Application of artificial neural network and constitutive equations to describe the hot compressive behavior of28Cr Mn Mo V steel[J]. Materials and Design, 2012, 35:557-562.
[24] TAO Zhijun, YANG He, LI Heng, et al. Constitutive modeling of compression behavior of TC4 tube based on modified Arrhenius and artificial neural network models[J]. Rare Metals, 2016, 35(2):162-171.