MENU
  • Scitation Home

Thank you

For your interest in the AIP Conference Proceedings

Check for updates on crossmark
Prev Next
Free
Published Online: 26 July 2019

HeatTracer - A novel Monte Carlo radiative heat transfer tool for cavity receivers simulation

AIP Conference Proceedings 2126, 170003 (2019); https://doi.org/10.1063/1.5117673
João P. Cardoso1,2,a), Luís Filipe Mendes2,3,b), and João Farinha Mendes1,c)
more...View Affiliations
ABSTRACT
The development of new receivers for central receiver systems operating at high temperatures require the development of suitable tools able to accurately simulate heat and mass transfer processes. Such tools should be able to process complex receiver geometries with nongray and directionally nonideal surfaces. This work introduces the development of a new tool, HeatTracer, for the simulation of radiative heat transfer based on the Monte Carlo method, directed to the study of radiation exchange for arbitrary user-defined cavity geometries with spectral and/or directional nonidealities. The main characteristics of this tool are presented, including the main physical models as well as some of the more relevant mathematical and computational algorithms.

REFERENCES
Section:
Previous section

  1. 1. “Technological Roadmap: Solar Thermal Electricity – 2014 edition,” (OECD/IEA, Brussels, 2014). Google Scholar
  2. 2. “Concentrating Solar Power - Technology Brief,” (IEA-ETSAP and IRENA, 2013). Google Scholar
  3. 3. “The Power to Change: Solar and Wind Cost Reduction Potential to 2025,” (IRENA, 2016). Google Scholar
  4. 4. “Sunshot Vision Study,” DOE/GO-102012-3037 (U.S. Department of Energy, 2012). Google Scholar
  5. 5. “Solar Thermal Electricity Strategic Agenda 2020-2025,” (European Solar Thermal Electricity Association, Brussels, 2012). Google Scholar
  6. 6. H. Villafán-Vidales, C. Arancibia-Bulnes, D. Riveros-Rosas, H. Romero-Paredes, and C. Estrada, Renew. Sust. Energ. Rev. 75, 894–908 (2017). https://doi.org/10.1016/j.rser.2016.11.070, Google ScholarCrossref
  7. 7. E. Koepf, I. Alxneit, C. Wieckert, and A. Meier, Appl. Energ. 188, 620–651 (2017). https://doi.org/10.1016/j.apenergy.2016.11.088, Google ScholarCrossref
  8. 8. M. Mehos, C. Turchi, J. Vidal, M. Wagner, Z. Ma, C. Ho, W. Kolb, C. Andraka, and A. Kruizenga, “Con-centrating Solar Power Gen3 Demonstration Roadmap,” Technical Report NREL/TP-5500-67464 (National Renewable Energy Laboratory, 2017). Google Scholar
  9. 9. F. Larrouturou, C. Caliot, and G. Flamant, Sol. Energy 109, 153–164 (2014). https://doi.org/10.1016/j.solener.2014.08.028, Google ScholarCrossref
  10. 10. F. Larrouturou, C. Caliot, and G. Flamant, Sol. Energy 130, 60–73 (2016). https://doi.org/10.1016/j.solener.2016.02.008, Google ScholarCrossref
  11. 11. M. Modest, Radiative Heat Transfer, 3rd ed. (Academic Press, New York, 2013). Google ScholarCrossref
  12. 12. J. R. Howell, R. Siegel, and M. P. Mengüç, Thermal Radiation Heat Transfer, 5th ed. (CRC Press, Boca Raton, 2010). Google ScholarCrossref
  13. 13. Tonatiuh - Ray tracing for solar energy, 2018, URL: https://github.com/iat-cener/tonatiuh/wiki. Google Scholar
  14. 14. A. Haji-Sheikh and J. R. Howell, “Monte Carlo Methods,” in Handbook of Numerical Heat Transfer, edited by W. J. Minkowycz, E. M. Sparrow, and J. Y. Murthy (John Wiley & Sons, Inc., New Jersey, 2006) Chap. 8,, pp. 249–295, 2nd ed. Google Scholar
  15. 15. J. P. Cardoso, A. Mutuberria, C. Marakkos, P. Schoettl, T. Osório, and I. Les, “New Functionalities for the Tonatiuh Ray-tracing Software,” in SolarPACES 2017, AIP Conference Proceedings 2033, edited by R. Mancilla and C. Richter (American Institute of Physics, 2018), pp. 210010–1 to 210010–9. Google ScholarScitation
  16. 16. M. Matsumoto and T. Nishimura, ACM T. Model. Comput. S. 8, 3–30 (1998). https://doi.org/10.1145/272991.272995, Google ScholarCrossref
  17. 17. T. Möller and B. Trumbore, J. Graph. Tools 2, 21–28 (1997). https://doi.org/10.1080/10867651.1997.10487468, Google ScholarCrossref
  18. 18. R. J. Segura and F. R. Feito, Journal WSCG 9, 76–81 (2001). Google Scholar
  19. 19. J. J. Jiménez, C. J. Ogáyar, J. M. Noguera, and F. Paulano, “Performance analysis for GPU-based ray- triangle algorithms,” in GRGRAPP 2014: 9th International Conference on Computer Graphics Theory and Applications (Lisboa, 2014). Google Scholar
  20. 20. N. Thrane and L. O. Simonsen, “A comparison of acceleration structures for GPU assisted ray tracing,” MSc thesis, University of Aarhus, Denmark 2005. Google Scholar
  1. Published by AIP Publishing.

Article Metrics

Views
173
Citations
Crossref 0
Web of Science
ISI 0

Altmetric

Please Note: The number of views represents the full text views from December 2016 to date. Article views prior to December 2016 are not included.