Skip to main content
Log in

Microstructure Simulation and Experiment for the Weak Weld Joint of a Domed Storage Tank during an Explosion Based on the Cellular Automaton Method

  • Technical Article
  • Published:
Journal of Materials Engineering and Performance Aims and scope Submit manuscript

Abstract

With the continuous augment of global oil demand and reserve volume, domed storage tanks are becoming increasingly widespread. The weak weld joint, designed on the top of the storage tank, will fail first in the pool fire. Therefore, it is of considerable significance to accurately obtain the change rules of the weld microstructure for studying the explosion of a domed oil storage tank, which can provide sufficient guarantee for the goods evacuation and personnel rescue. Dynamic recrystallization occurs in the failure process of a weld joint. Based on the cellular automaton method (CA method), the dynamic failure model for the weld joint was established and solved in this paper. The transient changes for stress, strain, temperature, and damage were calculated, and the grain size distribution and dynamic change rule of the weld joint were obtained. The tensile test was carried out to weak weld. A high-speed camera collected the fracture morphology of the weld joint during fracture. The simulation results were compared with the experimental results to verify the validity of the proposed model and analysis method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. L. Shi, J. Shuai and K. Xu, Fuzzy Fault Tree Assessment Based on Improved AHP for Fire and Explosion Accidents for Steel Oil Storage Tanks, J. Hazard Mater., 2014, 278, p 529–538. https://doi.org/10.1016/j.jhazmat.2014.06.034

    Article  CAS  Google Scholar 

  2. J. Taveau, Explosion of Fixed Roof Atmospheric Storage Tanks, Part 1: Background and Review of Case Histories, Process Saf. Prog., 2011, 30(4), p 381–392. https://doi.org/10.1002/prs.10459

    Article  CAS  Google Scholar 

  3. Z. Lu, D.V. Swenson and D.L. Fenton, Frangible Roof Joint Behavior of Cylindrical Oil Storage Tanks Designed to API 650 Rules, J. Press. Vessel Technol., 1996, 118(3), p 326–331. https://doi.org/10.1115/1.2842195

    Article  CAS  Google Scholar 

  4. K. Spranghers, I. Vasilakos, D. Lecompte, H. Sol and J. Vantomme, Numerical simulation and experimental validation of the dynamic response of aluminum plates under free air explosions, Int. J. Impact Eng, 2013, 54, p 83–95. https://doi.org/10.1016/j.ijimpeng.2012.10.014

    Article  Google Scholar 

  5. D. Wang, P. Zhang and L. Chen, Fuzzy Fault Tree Analysis for Fire and Explosion of Crude Oil Tanks, J. Loss Prev. Process Ind., 2013, 26(6), p 1390–1398.

    Article  Google Scholar 

  6. W. Li, Q. Shao and J. Liang, Numerical Study on Oil Temperature Field During Long Storage in Large Floating Roof Tank, Int. J. Heat Mass Transf., 2019, 130, p 175–186. https://doi.org/10.1016/j.ijheatmasstransfer.2018.10.024

    Article  Google Scholar 

  7. R. Luo, Q. Zheng, J.J. Zhu et al., Dynamic Recrystallization Behavior of Fe-20Cr-30Ni-0.6 Nb-2Al-Mo Alloy, Rare Metals, 2019, 38(2), p 181–188. https://doi.org/10.1007/s12598-016-0871-8

    Article  CAS  Google Scholar 

  8. K.G.F. Janssens, Random grid, three-dimensional, space-time coupled cellular automata for the simulation of recrystallization and grain growth, Modell. Simul. Mater. Sci. Eng., 2003, 11(2), p 157. https://doi.org/10.1088/0965-0393/11/2/304

    Article  Google Scholar 

  9. H.W. Hesselbarth and I.R. Göbel, Simulation of Recrystallization by Cellular Automata, Acta Metall. Mater., 1991, 39(9), p 2135–2143. https://doi.org/10.1016/0956-7151(91)90183-2

    Article  CAS  Google Scholar 

  10. J.A. Spittle and S.G. Brown, A Cellular Automaton Model of Steady-State Columnar-Dendritic Growth in Binary Alloys, J. Mater. Sci., 1995, 30(16), p 3989–3994. https://doi.org/10.1007/BF00360698

    Article  CAS  Google Scholar 

  11. R.L. Goetz and V. Seetharaman, Modeling Dynamic Recrystallization Using Cellular Automata, Scripta Mater., 1998, 38(3), p 405–413. https://doi.org/10.1016/S1359-6462(97)00500-9

    Article  CAS  Google Scholar 

  12. H. Yan, Z. Liwen and N. Jing, Cellular Automata and its Application in Mesoscopic Simulation of Materials, Heat Treat. Met., 2005, 05, p 72–77.

    Google Scholar 

  13. W. Yu, E.J. Palmiere and S.P. Banks, Cellular Automata Modelling of Grain Coarsening During Reheating-II Abnormal Grain Growth, J. Univ. Sci. Technol. Beijing, 2005, 12(1), p 26–32.

    CAS  Google Scholar 

  14. W. Yu, E.J. Palmiere, S.P. Banks and J. Han, Cellular Automata Modelling of Austenite Grain Coarsening During Reheating-I, Normal Grain Coarse., 2004, 11(6), p 517–523. https://doi.org/10.2320/jinstmet.68.1086

    Article  Google Scholar 

  15. Y. Hu, J. Xie, Z. Liu et al., CA Method with Machine Learning For Simulating the Grain And Pore Growth Of Aluminum Alloys, Comput. Mater. Sci., 2018, 142, p 244–254. https://doi.org/10.1016/j.commatsci.2017.09.059

    Article  CAS  Google Scholar 

  16. W. Xu, R. Yuan, H. Wu et al., Study on the Dynamic Recrystallization Behavior of Ti-55 Titanium Alloy During Hot Compression Based on Cellular Automaton Model Method, Proc. Eng., 2017, 207, p 2119–2124. https://doi.org/10.1016/j.proeng.2017.10.1109

    Article  CAS  Google Scholar 

  17. Y.X. Liu, Y.C. Lin and Y. Zhou, 2D Cellular Automaton Simulation of Hot Deformation Behavior in a Ni-Based Superalloy Under Varying Thermal-Mechanical Conditions, Mater. Sci. Eng., A, 2017, 691, p 88–99. https://doi.org/10.1016/j.msea.2017.03.039

    Article  CAS  Google Scholar 

  18. L. Wang, G. Fang and L. Qian, Modeling of Dynamic Recrystallization of Magnesium Alloy Using Cellular Automata Considering Initial Topology of Grains, Mater. Sci. Eng., A, 2018, 711, p 268–283. https://doi.org/10.1016/j.msea.2017.11.024

    Article  CAS  Google Scholar 

  19. L. Madej, M. Sitko, A. Legwand et al., Development and Evaluation of Data Transfer Protocols in the Fully Coupled Random Cellular Automata Finite Element Model of Dynamic Recrystallization, J. Comput. Sci., 2018, 26, p 66–77. https://doi.org/10.1016/j.jocs.2018.03.007

    Article  Google Scholar 

  20. S.M. Bararpour, H.J. Aval and R. Jamaati, Cellular Automaton Modeling of Dynamic Recrystallization in Al-Mg Alloy Coating Fabricated Using the Friction Surfacing Process, Surf. Coat. Technol., 2021, 407, p 126784. https://doi.org/10.1016/j.surfcoat.2020.126784

    Article  CAS  Google Scholar 

  21. N. Yazdipour, C.H.J. Davies and P.D. Hodgson, Microstructural Modeling of Dynamic Recrystallization Using Irregular Cellular Automata, Comput. Mater. Sci., 2009, 44(2), p 566–576. https://doi.org/10.1016/j.commatsci.2008.04.027

    Article  CAS  Google Scholar 

  22. A. Laasraoui and J. Jonas, Prediction of Steel Flow Stresses at High Temperatures and Strain Rates, Metall. Trans. A, 1991, 22(7), p 1545–1558. https://doi.org/10.1007/bf02667368

    Article  Google Scholar 

  23. J.J. Jonas, X. Quelennec, L. Jiang and É. Martin, The Avrami Kinetics of Dynamic Recrystallization, Acta Mater, 2009, 57(9), p 2748–2756. https://doi.org/10.1016/j.actamat.2009.02.033

    Article  CAS  Google Scholar 

  24. X. Quelennec, E. Martin, L. Jiang and J.J. Jonas, Work Hardening and Kinetics of Dynamic Recrystallization in Hot Deformed Austenite, Proc. Natl. Acad. Sci, 2010, 109(41), p 2794–2802. https://doi.org/10.1073/pnas.1205742109

    Article  Google Scholar 

  25. R. Ding and Z.X. Guo, Coupled Quantitative Simulation of Microstructural Evolution and Plastic Flow During Dynamic Recrystallization, Acta Mater, 2001, 49(16), p 3163–3175. https://doi.org/10.1016/s1359-6454(01)00233-6

    Article  CAS  Google Scholar 

  26. R. Ding and Z.X. Guo, Microstructural Modelling of Dynamic Recrystallisation Using an Extended Cellular Automaton Approach, Comput. Mater. Sci., 2002, 23(1), p 209–218. https://doi.org/10.1016/s0927-0256(01)00211-7

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by Natural Science Foundation of Liaoning Province Guidance Program Project (2019ZD0277); Liaoning University of Science and Technology Innovation Team Building Project (601009830); Liaoning University Innovative Talent Support Program (20201020).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Chang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Fig. 25, 26, 27, 28, 29, 30, 31, 32 and 33.

Fig. 25
figure 25

Two neighbor types of the cellular automaton (a) V. Neumann neighbor (b) Moore neighbor

Fig. 26
figure 26

Internal condition in the oil storage tank and actual explosion scene (a) Two zones in the oil tank (b) The explosion scene

Fig. 27
figure 27

Damage change in points at different temperatures (a) Damage of horizontal points at 600 °C b Damage of vertical points at 600 °C

Fig. 28
figure 28

Displacement change in points at different temperatures (a) Displacement of horizontal points at 600 °C (b) Displacement of vertical points at 600 °C

Fig. 29
figure 29

Stress change in points at different temperatures (a) Stress of horizontal point tracking at 600 °C (b) Stress of vertical point tracking at 600 °C

Fig. 30
figure 30

Temperature change in points at different temperatures (a) Temperature of horizontal point tracking at 600 °C (b) Temperature of vertical point tracking at 600 °C

Fig. 31
figure 31figure 31

Dynamic recrystallization in different points and temperatures (a) Dynamic recrystallization of vertical points at 300 °C (b) Dynamic recrystallization of horizontal points at 600 °C (c) Dynamic recrystallization of vertical points at 600 °C

Fig. 32
figure 32

Average grain size (a) Horizontal points at 600 °C (b) Vertical points at 600 °C

Fig. 33
figure 33

Specimen size drawing (a) Dimensional data of specimen in vertical direction (b) Specimen size data in the horizontal direction

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, L., Dacheng, Z., Xinxue, C. et al. Microstructure Simulation and Experiment for the Weak Weld Joint of a Domed Storage Tank during an Explosion Based on the Cellular Automaton Method. J. of Materi Eng and Perform 31, 8094–8112 (2022). https://doi.org/10.1007/s11665-022-06813-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11665-022-06813-5

Keywords

Navigation