Pipeline mechanical properties determination using non-destructive method with consideration of microstructural changes
Keywords:
electric resistivity, hardness, neural networks, pipelines, yield strength.Abstract
Existing pipeline networks used for transportation of oil and gas are being exposed for operation for decades resulting in serious material degradation process occurs in such cases. In this research results of experimental investigation aimed at determination of the electrical resistivity of structural steels used in gas transmission pipelines with the help of the developed experimental unit that implements the four-point method was studied. Multi-parameter approach was utilized in the study while neural networks were used for non-linear approximation of yield strength of pipelines as a function of hardness and electrical resistivity. Samples with special heat-treatment for microstructure distinguishing as well as a number of samples taken from the long-term used pipelines were selected. Destructive tensile testing was performed for all samples under investigation and results were used as references in the study. It was shown that the four-point method can be used to overall metal structures, since the measured value of electrical resistivity does not affect the whole width of the object of control, but only so-called conditional effective width. Under the conditional effective width of the sample should be understood that part of the sample, in which the density of direct current passing through the object, is the largest and which actually affects the measured value of electrical resistivity. Combined measurement of the hardness together with electrical resistivity after neural network processing showed to achieve 26 MPa accuracy for yield strength determination at real-life pipelines.
Downloads
References
structure (30 years on)’, Fatigue Fract Engng Mater Struct,
vol.32, pp. 461–463.
[2] Bida, GV, Pochuev, ND, Stashkov, AN 2002,
‘Nondestructive method for testing stress-strain properties of
oil pipes’, Russian J Non-Destructive Testing, vol. 38 (10),
pp. 725–738.
[3] Matyuk, VF, Goncharenko, SA, Hartman, H,
Reichelt, H 2003, ‘Modern state of non-destructive testing of
mechanical properties and stamping ability of steel sheets in a
manufacturing technological flow’, Russian J Non-Destructive
Testing, vol. 39 (5), pp. 347–380.
[4] Perez-Benitez, JA, Capo-Sanchez, J, AngladaRivera,
J, Padovese, LR 2008, ‘A study of plastic deformation
around a defect using the magnetic Barkhausen noise in
ASTM 36 steel’, NDT&E Int, vol. 41, pp. 53–58.
[5] Karpash, OM, Molodetskyy, IA, Karpash, MO 2004,
‘General overview of metals physical and mechanical
properties evaluation methods’, Technical diagnostics and
non-destructive testing, vol. 2, pp. 18–22 [in Ukrainian with
English abstract].
[6] Karpash, OM, Karpash, MO 2006, ‘New neuralbased
method for evaluation of mechanical properties of
steels’: proceedings of 9th European Conference of Nondestructive
testing, Berlin, P.114.
[7] Horkunov, ES 1985, ‘Interrelation between
magnetic, electric properties and structure condition of
thermally processed steels – as a basis for products strength
properties determination by non-destructive methods’,
Guidelines, Sverdlovsk, USSR Academy of Sciences [in
Russian].
[8] Nahm, SH, Kim, KMYu, Kim, A 2002, ‘Evaluation
of fracture toughness of degraded Cr-Mo-V steel using
electrical resistivity’, Journal of Material Science, vol. 37
(16), pp. 3549–3553.
[9] Karpash, MO, Karpash, OM, Dotsenko, YR 2008,
‘New challenges for mechanical properties evaluation of longterm
used metallic structures’: proceedings of 4th
International Symposium on Hydrocarbons and Chemistry,
Ghardaia (Algeria), p. 64.
[10] Bowler, N, Yongqlang, H 2005, ‘Electrical
conductivity measurement of metal plates using broadband
eddy-current and four-point methods’, Measurement Science
and Technology, vol. 16 (11), pp. 2193–2200.
[11] Heaney, MB 1999, Electrical Conductivity and
Electrical Conductivity and Resistivity. The Measurement,
Instrumentation and Sensors Handbook, CRC Press LLC,
Chapter 43.
[12] Dotsenko, YR 2010, ‘Mathematical modelling of
control resistivity materials by electric four-probe method’,
Prospecting and Development of Oil and Gas Fields, vol. 1.
pp.82–90 [in Ukrainian with English Abstract available on
http://www.nung.edu.ua/pub/rrngr/2010_34/10dyrecm.pdf].
[13] Karpash, MO, Dotsenko, YR, Kaprash. OM 2010,
‘New methods for mechanical properties evaluation of steel
structures with consideration of its microstructure’:
proceedings of 10th European Conference of Non-Destructive
Testing, Moscow, Part 2, pp. 270–271.
[14] Haykin, S, 1999. Neural networks. A
comprehensive foundation, Second edition. Prentice Hall, New
Jersey.
[15] Karpash, OM, Dotsenko, YR, Karpash, MO, Mitra,
A 2010, ‘Experimental investigation for electrical resistivity
measurement of metallic structures using four-point method’:
proceedings of 5th International Symposium on Hydrocarbons
and Chemistry (ISHC5), Sidi Fredj (Algeria), p. 112.
Downloads
Published
How to Cite
Issue
Section
License
Copyright Notice