期刊文献+

Optimum design of flow distribution in quenching tank for heat treatment of A357 aluminum alloy large complicated thin-wall workpieces by CFD simulation and ANN approach 被引量:5

利用CFD模拟和ANN模型对A357铝合金大型复杂构件淬火槽内介质流场分布的优化设计(英文)
下载PDF
导出
摘要 Based on the computational fluid dynamics (CFD) method, a quenching tank with two agitator systems and two flow-equilibrating devices was selected to simulate flow distribution using Fluent software. A numerical example was used to testify the validity of the quenching tank model. In order to take tank parameters (agitation speed, position of directional flow baffle and coordinate position in quench zone) into account, an approach that combines the artificial neural network (ANN) with CFD method was developed to study the flow distribution in the quenching tank. The flow rate of the quenching medium shows a very good agreement between the ANN predicted results and the Fluent simulated data. Methods for the optimal design of the quenching tank can be used as technical support for industrial production. 基于计算流体动力学(CFD)的方法,建立具有两个搅拌系统和两个稳流装置的淬火槽模型,使用Fluent软件对槽内介质的流场分布进行模拟计算。采用文献中的淬火槽内介质流场模拟及实验验证了研究模型的有效性。为了综合考虑搅拌速度、定向流挡板的位置及槽内有效淬火区的位置等因素对介质流场的影响,采用CFD方法与人工神经网络(ANN)相结合的方法对淬火槽内流场的分布进行了研究。结果表明,人工神经网络预测的槽内介质流场大小与Fluent模拟结果非常吻合。这种淬火槽的优化设计方法,可对实际工业生产提供技术支持。
出处 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2013年第5期1442-1451,共10页 中国有色金属学报(英文版)
关键词 A357 aluminum alloy computational fluid dynamics quenching tank flow distribution artificial neural network A357铝合金 计算流体动力学 淬火槽 流速分布 人工神经网络
  • 相关文献

参考文献26

  • 1ES-SAlD 0 S, LEE D, PROST W D, GAMBERINI A, MESSrERI S. Alternative heat treatments for A357- T6 aluminum alloy [J]. Eng Fail Anal, 2002, 9(1): 99-107.
  • 2YANG X w, ZHU JC, LA] Z H, KONG Y R, ZHAO R D, HE D. Application of artificial neural network to predict flow stress of as quenched A357 alloy [J]. Mater Sci Tech, 2012, 28(2): 151-155.
  • 3TOTTEN G E, BATES C E, CLINTON N A. Handbook of quenchants and quenching tecbnology [M]. Materials Park, OH: ASM International, 1993: 35-45.
  • 4CALLISTER w D. Materials science and engineering, an introduction [M]. USA, Hoboken: Wiley, 1994: 783-786.
  • 5ECKERSLEY J S, MEiSTER T J. Intelligent design takes advantage of residual stresses [C]/lProc of the 3rd International Conference on Practical Applications of Residual Stress Technology. Ohio: ASM International, 1991: 175-181.
  • 6RUUD C O. Residual stresses and their measurement. quenching and distortion control [C]//Proc of the First International Conference on Quenching and Control of Distortion. OH: ASM International, 1992: 193-198.
  • 7THAKKAR R, SHAH R, VAN ARK V. Effects of hole making processes and surface conditioning on fatigue behavior of 6061- T6 aluminum [C]//Proc of SAE 2000 World Congress. Detroit: SAE International, 2000.
  • 8TOTTEN G E, WEBSTER G M, GOPINATH N. Quenching fundamentals: Effect of agitation [J]. Adv Mater Process, 1996, 149: 73-76.
  • 9TENSI H M, TOTTEN G E, WEBSTER G M. Limitation of the use of grossman quench severity factors [C]//Proceedings of 17th heat treating society conference proceedings including the 1st international induction heat treating symposium. Ohio: ASM International, 1998: 423-431.
  • 10VERSTEEG H K, MALALASEKERA W. An introduction to computational fluid dynamics: The finite volume method [M]. New Jersey: Prentice Hall, 1996: 45-65.

同被引文献55

  • 1郭拉凤,李保成,张治民.基于神经网络的TC21合金本构关系模型(英文)[J].中国有色金属学会会刊:英文版,2013,23(6):1761-1765. 被引量:4
  • 2马永杰.热处理生产的环保与节能[J].热加工工艺,2006,35(14):64-66. 被引量:4
  • 3YANG Xia-wen, ZHU Jing-chuan, LAI Zhong-hong, LIU Yong, HE Dong, NONG Zhi-sheng. Finite element analysis of quench temperature field, residual stress and distortion in A357 aluminum alloy large complicated thin-wall workpieces [J]. Transactions of Nonferrous Metals Society of China, 2013, 23(6): 1751 1760.
  • 4ES-SAID O S, LEE D, PROST W D, GAMBERINI A, MESSIERI S. Alternative heat treatments for A357-T6 aluminum alloy [J]. Engineering Failure Analysis, 2002, 9(1): 99-107.
  • 5YANG Xia-wei, ZHU Jing-chuan, NONG Zhi-sheng, HE Dong, LAI Zhong-hong, LIU Yong, LIU Fa-wei. Prediction of mechanical properties of A357 alloy using artificial neural network [J]. Transactions of Nonferrous Metals Society of China, 2013, 23(3): 788 795.
  • 6CALLISTER W D. Materials science and engineering: An introduction [M]. New Jersey: John-Wiley, 1994: 783-784.
  • 7YANG X W, ZHU J C, NONG Z S, LAI Z H, HE D. FEM simulation of quenching process in aluminum alloy cylindrical bars and reduction of quench residual stress through cold stretching process [J]. Computational Materials Science, 2013, 69: 396-413.
  • 8ELKATATNY I, MORSI Y, BLICBLAU A S, DAS S, DOYLE E D. Numerical analysis and experimental validation of high pressure gas quenching [J]. International Journal of Thermal Sciences, 2003, 42(4) 417-423.
  • 9BAKER A J. Potential for CFD in heat treating (computational fluid dynamics) [J]. Advanced Materials & Processes, 1997, 10: 44-47.
  • 10CANALE L C F, TOTTEN G E. Quenching technology: A selected overview of the current state-of-the-art [J]. Materials Research, 2005, 8(4): 461-467.

引证文献5

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部