JOM

, Volume 66, Issue 12, pp 2569–2577 | Cite as

Using Coupled Mesoscale Experiments and Simulations to Investigate High Burn-Up Oxide Fuel Thermal Conductivity

  • Melissa C. Teague
  • Bradley S. Fromm
  • Michael R. Tonks
  • David P. Field
Article

Abstract

Nuclear energy is a mature technology with a small carbon footprint. However, work is needed to make current reactor technology more accident tolerant and to allow reactor fuel to be burned in a reactor for longer periods of time. Optimizing the reactor fuel performance is essentially a materials science problem. The current understanding of fuel microstructure have been limited by the difficulty in studying the structure and chemistry of irradiated fuel samples at the mesoscale. Here, we take advantage of recent advances in experimental capabilities to characterize the microstructure in 3D of irradiated mixed oxide (MOX) fuel taken from two radial positions in the fuel pellet. We also reconstruct these microstructures using Idaho National Laboratory’s MARMOT code and calculate the impact of microstructure heterogeneities on the effective thermal conductivity using mesoscale heat conduction simulations. The thermal conductivities of both samples are higher than the bulk MOX thermal conductivity because of the formation of metallic precipitates and because we do not currently consider phonon scattering due to defects smaller than the experimental resolution. We also used the results to investigate the accuracy of simple thermal conductivity approximations and equations to convert 2D thermal conductivities to 3D. It was found that these approximations struggle to predict the complex thermal transport interactions between metal precipitates and voids.

References

  1. 1.
    M. Teague, B. Gorman, J. King, D. Porter, and S. Hayes, J. Nucl. Mater. 441, 267 (2013).CrossRefGoogle Scholar
  2. 2.
    M. Teague, B. Gorman, B. Miller, and J. King, J. Nucl. Mater. 444, 475 (2014).CrossRefGoogle Scholar
  3. 3.
    M. Teague and B. Gorman, Prog. Nucl. Energy 72, 67 (2014).CrossRefGoogle Scholar
  4. 4.
    M. Tonks, D. Gaston, P. Millett, D. Andrs, and P. Talbot, Comput. Mater. Sci. 51, 20 (2012).CrossRefGoogle Scholar
  5. 5.
    M.R. Tonks, P.C. Millett, P. Nerikar, S. Du, D. Andersson, C.R. Stanek, D. Gaston, D. Andrs, and R. Williamson, J. Nucl. Mater. 440, 193 (2013).CrossRefGoogle Scholar
  6. 6.
    M. Teague, M. Tonks, S. Novascone, and S. Hayes, J. Nucl. Mater. 444, 161 (2014).CrossRefGoogle Scholar
  7. 7.
    H. Kleykamp, J. Nucl. Mater. 131, 221 (1985).CrossRefGoogle Scholar
  8. 8.
    J. Spino, K. Vennix, and M. Coquerelle, J. Nucl. Mater. 231, 179 (1996).CrossRefGoogle Scholar
  9. 9.
    H. Kleykamp, J. Nucl. Mater. 171, 181 (1990).CrossRefGoogle Scholar
  10. 10.
    Idaho National Laboratory, Moose Framework: Advanced Capability, Delivered Simply, 2014. http://www.mooseframework.org.
  11. 11.
    P.C. Millett, M.R. Tonks, K. Chockalingam, Y. Zhang, and S. Biner, J. Nucl. Mater. 439, 117 (2013).CrossRefGoogle Scholar
  12. 12.
    B.S. Fromm, K. Chang, D.L. McDowell, L.-Q. Chen, and H. Garmestani, Acta Mater. 60, 5984 (2012).CrossRefGoogle Scholar
  13. 13.
    P.P. Mukherjee and C.-Y. Wang, J. Electrochem. Soc. 153, A840–A849 (2006).CrossRefGoogle Scholar
  14. 14.
    S.B. Lee, A.D. Rollett, and G.S. Rohrer, Mater. Sci. Forum 558, 915. http://www.scientific.net/MSF.558-559.915.
  15. 15.
    L. Chen, Annu. Rev. Mater. Res. 32, 113 (2002).CrossRefGoogle Scholar
  16. 16.
    EDAX Inc., OIM Data Analysis, Version 7.1 (Mahwah, NJ: EDAX Inc., 2014).Google Scholar
  17. 17.
    Math-Works Inc., MATLAB, Version 8.3.0.532 (R2014a) (Natick, MA: The Math-Works Inc., 2014).Google Scholar
  18. 18.
    M.A. Groeber and M.A. Jackson, Integr. Mater. Manuf. Innov. 3, 5 (2014).CrossRefGoogle Scholar
  19. 19.
    BlueQuartz Software, DREAM.3D, Version 4.2.4993 (Springboro, OH: BlueQuartz Software, 2014).Google Scholar
  20. 20.
    Pixelmator Team, Pixelmator, Version 3.2 (Vilnius, Lithuania: Pixelmator Team, 2014).Google Scholar
  21. 21.
    N. Moelans, B. Blanpain, and P. Wollants, Phys. Rev. B 78, 024113 (2008).CrossRefGoogle Scholar
  22. 22.
    K. Bakker, H. Kwast, and E. Cordfunke, J. Nucl. Mater. 226, 128 (1995).CrossRefGoogle Scholar
  23. 23.
    K. Bakker, Int. J. Heat Mass Trans. 40, 3503 (1997).CrossRefMATHGoogle Scholar
  24. 24.
    P. Millett, D. Wolf, T. Desai, S. Rokkam, and A. El-Azab, J. Appl. Phys. 104, 033512 (2008).CrossRefGoogle Scholar
  25. 25.
    P.C. Millett and M. Tonks, J. Nucl. Mater. 412, 281 (2011).CrossRefGoogle Scholar
  26. 26.
    K. Chockalingam, P. Millett, and M. Tonks, J. Nucl. Mater. 430, 166 (2012).CrossRefGoogle Scholar
  27. 27.
    J.J. Carbajo, G.L. Yoder, S.G. Popov, and V.K. Ivanov, J. Nucl. Mater. 299, 181 (2001).CrossRefGoogle Scholar
  28. 28.
    S. Yamanaka and K. Kurosaki, J. Alloy. Compd. 353, 269 (2003).CrossRefGoogle Scholar
  29. 29.
    E. Mason and S. Saxena, Phys. Fluids 1, 361 (1958).CrossRefMathSciNetGoogle Scholar
  30. 30.
    S. Saxena, High Temp. Sci. 3, 168 (1971).Google Scholar

Copyright information

© The Minerals, Metals & Materials Society (outside the U.S.) 2014

Authors and Affiliations

  • Melissa C. Teague
    • 1
  • Bradley S. Fromm
    • 2
    • 3
  • Michael R. Tonks
    • 3
  • David P. Field
    • 4
  1. 1.Fuel Performance and DesignIdaho National LaboratoryIdaho FallsUSA
  2. 2.Material Science and Engineering ProgramWashington State UniversityPullmanUSA
  3. 3.Fuel Modeling and SimulationIdaho National LaboratoryIdaho FallsUSA
  4. 4.School of Mechanical and Materials EngineeringWashington State UniversityPullmanUSA

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