Open Access Open Access  Restricted Access Subscription or Fee Access

Computational Study of Corrosion in Steel in Aqueous Solution: a Review


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireme.v14i8.19238

Abstract


Corrosion affects most of the material strength and causes damages to the material and the structure. Corrosion has become a major problem of material stability and it is difficult to predict its phenomenon. It can happen in any environment and can deteriorate metallic material. Computational models have been implemented in order to study and predict the phenomenon and the evolution of the corrosion. This paper presents a critical review of the computational model from various softwares that have been developed to observe the growth and the behaviour of corrosion on the metallic material in aqueous solution. The metallic material undergoes redox reaction with the presence of oxygen and water to form corrosion. This paper reviews the corrosion model of steel at different pH, temperatures and types of ionic species. The pH of the aqueous solution is either acidic or alkaline which will determine the rate of corrosion by the ionic reaction in the electrolyte.
Copyright © 2020 Praise Worthy Prize - All rights reserved.

Keywords


Computational Modelling; Corrosion; Simulation; Steel

Full Text:

PDF


References


M. G. Fontana, Corrosion Engineering, 3rd ed. McGraw-Hill Book Company, 1987.

S. Salleh, Modelling Pitting Corrosion in Carbon Steel Materials, University of Manchester, 2013.

T. Q. Ansari, J.-L. Luo, and S.-Q. Shi, Modeling the effect of insoluble corrosion products on pitting corrosion kinetics of metals, npj Mater. Degrad., vol. 3, no. 1, p. 28, Dec. 2019.
https://doi.org/10.1038/s41529-019-0090-5

J. Bhandari, F. Khan, R. Abbassi, V. Garaniya, and R. Ojeda, Modelling of pitting corrosion in marine and offshore steel structures – A technical review, J. Loss Prev. Process Ind., vol. 37, pp. 39–62, Sep. 2015.
https://doi.org/10.1016/j.jlp.2015.06.008

M. G. Stewart and A. Al-Harthy, Pitting corrosion and structural reliability of corroding RC structures: Experimental data and probabilistic analysis, Reliab. Eng. Syst. Saf., vol. 93, no. 3, pp. 373–382, Mar. 2008.
https://doi.org/10.1016/j.ress.2006.12.013

H. Zhang, S. Xu, B. Nie, and Y. Wen, Effect of corrosion on the fracture properties of steel plates, Constr. Build. Mater., vol. 225, pp. 1202–1213, Nov. 2019.
https://doi.org/10.1016/j.conbuildmat.2019.07.345

Y. Zhu, M. L. Free, R. Woollam, and W. Durnie, A review of surfactants as corrosion inhibitors and associated modeling, Prog. Mater. Sci., vol. 90, pp. 159–223, Oct. 2017.
https://doi.org/10.1016/j.pmatsci.2017.07.006

M. H. Wood, A. L. Vetere Arellano, and L. Van Wijk, Corrosion ‐ Related Accidents in Petroleum Refineries, Italy, 2013.

L. Popoola, A. Grema, G. Latinwo, B. Gutti, and A. Balogun, Corrosion problems during oil and gas production and its mitigation, Int. J. Ind. Chem., vol. 4, no. 1, p. 35, Sep. 2013.
https://doi.org/10.1186/2228-5547-4-35

G. Engelhardt and D. D. Macdonald, Deterministic Prediction of Pit Depth Distribution, Corrosion, 1998.

Z. H. Xiao, S. Y. Hu, J. L. Luo, S. Q. Shi, and C. H. Henager, A quantitative phase-field model for crevice corrosion, Comput. Mater. Sci., vol. 149, pp. 37–48, Jun. 2018.
https://doi.org/10.1016/j.commatsci.2018.03.011

P. T. Brewick, V. G. DeGiorgi, A. B. Geltmacher, and S. M. Qidwai, Modeling the influence of microstructure on the stress distributions of corrosion pits, Corros. Sci., vol. 158, pp. 1–10, 2019.
https://doi.org/10.1016/j.corsci.2019.108111

W. Mai and S. Soghrati, New phase field model for simulating galvanic and pitting corrosion processes, Electrochim. Acta, vol. 260, pp. 290–304, 2018.
https://doi.org/10.1016/j.electacta.2017.12.086

C. Lin and H. Ruan, Multi-phase-field modeling of localized corrosion involving galvanic pitting and mechano-electrochemical coupling, Corros. Sci., vol. 177, no. March, p. 108900, 2020.
https://doi.org/10.1016/j.corsci.2020.108900

C. Lin, H. Ruan, and S. Q. Shi, Phase field study of mechanico-electrochemical corrosion, Electrochim. Acta, vol. 310, pp. 240–255, 2019.
https://doi.org/10.1016/j.electacta.2019.04.076

B. Jegdić, J. Popić, B. Bobić, and M. Stevanović, Chemical corrosion of metals and alloys, Zast. Mater., vol. 57, no. 2, pp. 205–211, 2016.
https://doi.org/10.5937/zasmat1602205j

D. M. Dražic and J. P. Popic, Anomalous dissolution of metals and chemical corrosion, J. Serbian Chem. Soc., vol. 70, no. 3, pp. 489–511, 2005.
https://doi.org/10.2298/jsc0503489d

A. Kahyarian, A. Schumaker, B. Brown, and S. Nesic, Acidic corrosion of mild steel in the presence of acetic acid: Mechanism and prediction, Electrochim. Acta, vol. 258, pp. 639–652, 2017.
https://doi.org/10.1016/j.electacta.2017.11.109

A. F. Chadwick, J. A. Stewart, R. A. Enrique, S. Du, and K. Thornton, Numerical Modeling of Localized Corrosion Using Phase-Field and Smoothed Boundary Methods, J. Electrochem. Soc., vol. 165, no. 10, pp. C633–C646, 2018.
https://doi.org/10.1149/2.0701810jes

Joseph R. Davis, Corrosion: Understanding the Basics. United States of America: ASM International, 2000.

T. S. Huang, S. Zhao, G. S. Frankel, and D. A. Wolfe, A statistical model for localized corrosion in 7xxx aluminum alloys, Corrosion, vol. 63(9), pp. 819–827, 2007.
https://doi.org/10.5006/1.3278431

P. Ernst and R. . Newman, Pit growth studies in stainless steel foils. I. Introduction and pit growth kinetics, Corros. Sci., vol. 44, no. 5, pp. 927–941, May 2002.
https://doi.org/10.1016/s0010-938x(01)00133-0

S. yu Cai, L. Wen, and Y. Jin, A comparative study on corrosion kinetic parameter estimation methods for the early stage corrosion of Q345B steel in 3.5wt% NaCl solution, Int. J. Miner. Metall. Mater., vol. 24, no. 10, pp. 26–28, 2017.
https://doi.org/10.1007/s12613-017-1502-6

J. Soltis, Passivity breakdown, pit initiation and propagation of pits in metallic materials – Review, Corros. Sci., vol. 90, pp. 5–22, Jan. 2015.
https://doi.org/10.1016/j.corsci.2014.10.006

S. Salleh and N. P. C. Stevens, A mathematical model to study the propagation of pitting corrosion in steel immersed in chloride solution, ARPN J. Eng. Appl. Sci., vol. 13, no. 1, pp. 134–139, 2018.

D. De Meo and E. Oterkus, Finite element implementation of a peridynamic pitting corrosion damage model, Ocean Eng., vol. 135, no. July 2016, pp. 76–83, 2017.
https://doi.org/10.1016/j.oceaneng.2017.03.002

C. Liu and R. G. Kelly, A review of the application of finite element method (FEM) to localized corrosion modeling, Corrosion, vol. 75, no. 11, pp. 1285–1299, 2019.
https://doi.org/10.5006/3282

A. Turnbull and D. H. Ferriss, Mathematical modelling of the electrochemistry in corrosion fatigue cracks in steel corroding in marine environments, Corros. Sci., vol. 27, no. 12, pp. 1323–1350, 1987.
https://doi.org/10.1016/0010-938x(87)90129-6

B. Vuillemin, R. Oltra, R. Cottis, and D. Crusset, Consideration of the formation of solids and gases in steady state modelling of crevice corrosion propagation, Electrochim. Acta, vol. 52, no. 27, pp. 7570–7576, Oct. 2007.
https://doi.org/10.1016/j.electacta.2006.12.036

Y. H. Lee, Z. Takehara, and S. Yoshizawa, The enrichment of hydrogen and chloride ions in the crevice corrosion of steels, Corros. Sci., vol. 21, no. 5, pp. 391–397, 1981.
https://doi.org/10.1016/0010-938x(81)90075-5

J. Mankowski and Z. Szklarska-Smialowska, Studies on accumulation of chloride ions in pits growing during anodic polarization, Corros. Sci., vol. 15, no. 6–12, pp. 493–501, 1975.
https://doi.org/10.1016/0010-938x(75)90015-3

S. Salleh and N. P. C. Stevens, A Theoretical Model of Pitting Corrosion Using a General Purpose Finite Element Package, J. Mech. Eng. Technol., vol. 4, no. 1, pp. 1–14, 2012.

X. Sun and R. Duddu, A sequential non-iterative approach for modeling multi-ionic species reactive transport during localized corrosion, Finite Elem. Anal. Des., vol. 166, no. 103318, 2019.
https://doi.org/10.1016/j.finel.2019.103318

S. M. Sharland and P. W. Tasker, A mathematical model of crevice and pitting corrosion—I. The physical model, Corros. Sci., vol. 28, no. 6, pp. 603–620, Jan. 1988.
https://doi.org/10.1016/0010-938x(88)90027-3

K. Yaya, Y. Khelfaoui, B. Malki, and M. Kerkar, Numerical simulations study of the localized corrosion resistance of AISI 316L stainless steel and pure titanium in a simulated body fluid environment, Corros. Sci., vol. 53, no. 10, pp. 3309–3314, 2011.
https://doi.org/10.1016/j.corsci.2011.06.006

Z. Chen and F. Bobaru, Peridynamic modeling of pitting corrosion damage, J. Mech. Phys. Solids, vol. 78, pp. 352–381, May 2015.
https://doi.org/10.1016/j.jmps.2015.02.015

F. Bobaru and M. Duangpanya, A peridynamic formulation for transient heat conduction in bodies with evolving discontinuities, J. Comput. Phys., vol. 231, no. 7, pp. 2764–2785, 2012.
https://doi.org/10.1016/j.jcp.2011.12.017

F. Bobaru and M. Duangpanya, International Journal of Heat and Mass Transfer The peridynamic formulation for transient heat conduction, Int. J. Heat Mass Transf., vol. 53, no. 19–20, pp. 4047–4059, 2010.
https://doi.org/10.1016/j.ijheatmasstransfer.2010.05.024

J. Xia, T. Li, J.-X. Fang, and W. Jin, Numerical simulation of steel corrosion in chloride contaminated concrete, Constr. Build. Mater., vol. 228, p. 116745, Dec. 2019.
https://doi.org/10.1016/j.conbuildmat.2019.116745

Y. Li, S. Hu, X. Sun, and M. Stan, A review: Applications of the phase field method in predicting microstructure and property evolution of irradiated nuclear materials, npj Comput. Mater., vol. 3, no. 1, pp. 1–16, 2017.
https://doi.org/10.1038/s41524-017-0018-y

C. Tsuyuki, A. Yamanaka, and Y. Ogimoto, Phase-field modeling for pH-dependent general and pitting corrosion of iron, Sci. Rep., vol. 8, no. 1, 2018.
https://doi.org/10.1038/s41598-018-31145-7


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize