Advertisement

Inorganic Materials

, Volume 54, Issue 15, pp 1523–1531 | Cite as

Statistical Estimate of Determining the Critical Temperature of Brittleness for Metal of the VVER-1000 Reactor Vessel Using Impact Bending Test Data

  • A. G. KazantsevEmail author
  • V. M. Markochev
  • B. A. Sugirbekov
MECHANICS OF MATERIALS: STRENGTH, RESOURCE, AND SAFETY
  • 8 Downloads

Abstract

Using statistical modeling (Monte Carlo method), numerical experiments were performed to determine the critical temperature Tc of brittleness according to the PNAE G-7-002-86 and RD EO 0598-2004 methodologies. The data of the Charpy impact test (V-notched) used in the calculations were obtained using more than 1200 samples of 15Kh2NMFAA steel cut from various zones along the thickness, height, and circumferential direction of the VVER-1000 reactor vessel. The tests were carried out in the temperature range from –95 to +20°C. On the basis of statistical criteria, we show that the shell material of the reactor vessel can be considered as homogeneous. The values of the destruction energy of impact samples in the brittle-viscous transition region are found to be distributed according to a bimodal law. The distribution parameters are determined at various temperatures. Using the statistical modeling, the distribution laws of the critical temperature of brittleness are determined. The average values of Tc obtained using PNAE G-7-002-86 are shown to be approximately 10°C higher than those obtained using RD EO 0598-2004. We determined the boundaries of the intervals in which the values of the critical temperature of brittleness with 90% probability fall depending on the number of samples tested and the test scheme. Recommendations for improving the methodology of determining the critical temperature of brittleness are given.

Keywords:

impact viscosity statistics brittle-viscous transition critical temperature of brittleness statistical modeling Monte Carlo method 

Notes

REFERENCES

  1. 1.
    RD 0598-2004. Metodika opredeleniya kriticheskoi temperatury khrupkosti materialov korpusov reaktorov po rezul’tatam ispytanii malorazmernykh obraztsov na udarnyi izgib (RD 0598-2004. Measuring the Critical Temperature of Brittleness of RPV Materials Using Results of Impact Tests of Small-Size Samples), Moscow: Rosenergoatom, 2004.Google Scholar
  2. 2.
    PNAE G-7-002-86. Pravila i normy v atomnoi energetike. Normy rascheta na prochnost’ oborudovaniya i truboprovodov atomnykh energeticheskikh ustanovok (PNAE G‑7-002-86. Rules and Standards in Nuclear Energetics. Standards of Calculation for Durability of Equipment and Pipelines for Nuclear Power Installations), Moscow: Energoatomizdat, 1989.Google Scholar
  3. 3.
    Stepnov, M.N., Veroyatnostnye metody otsenki kharakteristik mekhanicheskikh svoistv (Probabilistic Methods for Evaluation of the Characteristics of the Mechanical Properties), Novosibirsk: Nauka, 2005.Google Scholar
  4. 4.
    Kazantsev, A.G., Markochev, V.M., and Sugirbekov, B.A., Evaluation of errors of determination of the critical temperature of metal brittleness of cases of VVER-1000 case, Tyazh. Mashinostr., 2015, no. 10, pp. 19–27.Google Scholar
  5. 5.
    Davidenkov, N.N., Dinamicheskie ispytaniya metallov (Dynamic Testing of Metals), Moscow: Narkomat Tazh. Prom. SSSR, 1936.Google Scholar
  6. 6.
    Shevandin, E.I. and Razov, I.A., Khladnolomkost’ i predel’naya plastichnost’ metallov v sudostroenii (Cold Brittleness and Maximum Plasticity of Metals in Ship-Building Industry), Leningrad: Sudostroenie, 1965.Google Scholar
  7. 7.
    Kantor, M.M. and Bozhenov, V.A., Scattering of values of impact toughness of low-alloy steel in the ductile-brittle transition temperature region, Inorg. Mater.: Appl. Res., 2014, vol. 5, no. 4, pp. 293–302.CrossRefGoogle Scholar
  8. 8.
    Markochev, V.M. and Aleksandrova, O.V., Fractional power function in description of the probability, Zavod. Lab., Diagn. Mater., 2012, vol. 78, no. 11, pp. 71–73.Google Scholar
  9. 9.
    Buslenko, N.P. and Shreider, Yu.A., Metod statisticheskikh ispytanii (Monte-Karlo) i ego realizatsiya v tsifrovykh mashinakh (Method of Statistical Tests (Monte Carlo) and Its Implementation in Digital Machines), Moscow: Fizmatlit, 1961.Google Scholar
  10. 10.
    Chernobaeva, A.A., Nikolaev, Yu.A., Skundin, M.A., Zhurko, D.A., Krasikov, E.A., Medvedev, K.I., Kostromin, V.N., Drobkov, G.V., and Ryasanov, S.V., Data scatter cause analysis of the temperature surveillance specimens of VVER-1000 metal, J. At. Energy, 2012, vol. 113, no. 6, pp. 337–344.Google Scholar
  11. 11.
    Chernobaeva, A.A., Kuleshova, E.A., Skundin, M.A., Maltsev, D.A., Chirko, L.I., and Revka, V.N., Revision of date base of VVER-1000 thermal aging surveillance specimens, Proc. 22nd Int. Conf. on Structural Mechanics in Reactor Technology (SMiRT 22), San-Francisco, Raleigh, NC: Int. Assoc. Struct. Mech. Reactor Technol., 2013, pp. 138–147.Google Scholar
  12. 12.
    Chernobaeva, A.A., Verification of the models of radiation embrittlement of RPV materials and procedures of their application to evaluate operated reactor pressure vessels, Doctoral (Eng.) Dissertation, Moscow, 2009.Google Scholar
  13. 13.
    RD EO 1.1.2.0.0789-2012. Metodika opredeleniya vyazkosti razrusheniya po rezul’tatam ispytanii obraztsov-svidetelei dlya rascheta prochnosti i resursa korpusov reaktorov VVER-1000 (RD EO 1.1.2.0.0789-2012. Measuring the Destruction Viscosity Using the Results of Tests of Samples for Calculation of the Strength and Capacity of VVER-1000 Reactor Cases), Moscow: Rosenergoatom, 2012.Google Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • A. G. Kazantsev
    • 1
    Email author
  • V. M. Markochev
    • 2
  • B. A. Sugirbekov
    • 1
  1. 1.State Research Center of Russia CNIITMASHMoscowRussia
  2. 2.National Research Nuclear University (MEPhI)MoscowRussia

Personalised recommendations