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ADVANCED MATERIALS & PROCESSES • AUGUST 2014 17
compu t a t i ona l
materials engi-
neering (ICME)
paradigm that has
already begun to
accelerate the
timeline for mate-
rials development and lead
to cost reduction[8]. Surface composition and hydrophobicity
control can be achieved using techniques like physical and
chemical vapor or electrochemical deposition. Recent devel-
opments in additive manufacturing of alloy components may
provide unique opportunities for tailoring customized alloys
with graded compositions. Other advanced concepts include
diamond-like carbon and graphene coatings[9].
Corrosion mitigation: When a CRA is incompatible with
design constraints, or when dealing with an existing system,
environmental controls to mitigate corrosion must be em-
ployed. Protecting metallic surfaces via coatings, introducing
aqueous or vapor phase inhibitors, cathodic (electrochemical)
protection, and/or sacrificial anodes are several proactive
methods that can be used to bias systems away fromcorrosion
and toward a passive, failure-resistant state. Process controls
should be employed as much as possible to monitor and con-
trol factors such as temperature, humidity, pH, and ionic con-
tent. Contemporary challenges in mitigation include
developing chromate-free, environmentally friendly inhibitor
compounds, and gaining a better understanding of the influ-
ence of process conditions on corrosion rate, particularly for
localized corrosion[10]. Avoiding the occurrence of galvanic cor-
rosion cells by minimizing dissimilar metal joints is a way to
prevent corrosion failure at the design stage[11].
Lifetime prediction: Given a specific material choice and a
mitigation strategy, models can be employed to develop a risk
assessment of the material’s susceptibility to corrosion failure
as a function of age and environment. Model accuracy de-
pends on the known science behind the particular problem,
plus data reliability for conditions and corrosion rates. Inmany
cases, empirical models have been constructed fromhistorical
data, and find the most application in industry. At the same
time, science-based thermodynamic and kineticmodels assess
the corrosivity of a given environment-materials combination.
Science-basedmodels have the advantage of being independ-
ent of time and space, unlike historically derived empirical
models. Science-based predictions are also highly constrained
according to the physics, chemistry, and metallurgical aware-
ness incorporated into the model.
Given the differences between empirical and science-
based models, recent modeling has moved toward Bayesian
inference, which synthesizes empirical, probabilistic estimates
of corrosion failure with science-based models[12]. Further-
more, multiscale modeling is moving toward an integrated
simulation of materials chemistry and microstructure along-
side finite element simulation of transport phenomena and
stress states in the material, with environment-side chemistry
via reactive-transport models.
Inspection: Materials design and corrosionmitigation en-
able design-level decisions that prepare against corrosion fail-
ures. Lifetime prediction allows engineers to estimate the
time to failure and the most critical locations where corro-
sion is likely to occur. The final defense is then to ac-
tivelymonitor assets in response to the anticipated risk.
The development of microelectronics and wireless
technology has increased the application of sensor and
inspection technologies. Examples include ultra-
sound techniques for probing corrosion under insu-
lation, and more recently, backscatter computed
radiation tomography[13] . Updated information re-
garding the state of materials degradation provides
an opportunity to refresh failure models, and pos-
sibly construct new models that more accurately
represent system evolution. Numerical simulation
can also be coupled with data generated from in-
spection to help assess the risk of failure, such as
the incorporation of dent measurements with fi-
nite-element simulation to assess risks for SCC[14].
Given the uncertainties associated with modeling,
lifetime prediction, and experimental testing, in-
spection will always be necessary to detect corrosion
events before failure occurs.
For more information: Jose E. Ramirez is principal engineer,
materials technology development, DNV GL – Oil & Gas,
5777 Frantz Rd., Dublin, OH, 43017, 614.761.6926,
, .
Fig. 2 — Strategy tetrahedron
for corrosion failure prevention.
Materials design
Inspection
Lifetime
prediction
Corrosion
mitigation
Pipeline
inspection under
insulation using
backscatter
computed
tomography[13].
References
1. G.H. Koch, et al., Corrosion Costs and Preventive Strategies in the United
States, CC Technologies Labs Inc., Dublin, Ohio; Y. Paul Virmani, U.S. Fed-
eral Highway Administration, Turner-Fairbank Highway Research Center,
McLean, Va.; J.H. Payer, Case Western Reserve University, Cleveland, Pub-
lication No. FHWA-RD-01-156, 2002.
2. G. Schmitt, Global Needs for Knowledge Dissemination, Research, and
Development in Materials Deterioration and Corrosion Control, World
Corrosion Organization, 2009.
3. O. Yevtushenko, R. Babler, and I. Carillo-Salgado, Materials Challenges
for Stainless Steel in Carbon Capture and Storage, Mater. Performance, p
74-77, May 2014.
4. G.A. Jacobson, Pipeline Corrosion Issues Related to Carbon Capture,
Transportation, and Storage, Mater. Performance, p 24-31, May 2014.
5. B. Hayman, J. Wedel-Heinen, and P. Brondsted, MRS Bull., Vol 33, p
343, 2008.
6. B.D. Cunningham, J. Huang, and D.G. Baird, Int. Mater. Rev., Vol 52, p
1. 2007.
7. M. Kumagai, et al., Electrochim. Acta, Vol 53, p 4205, 2008.
8. G.B. Olson, Genomic Materials Design: The Ferrous Frontier, Acta
Mater., Vol 61, p 771-781, 2013.
9. D. Prasai, et al., Graphene: Corrosion-Inhibiting Coating, ACS Nano, Vol
6 (2), p 1102-1108, 2012.
10. M.W. Kendig and R.G. Buchheit, Corrosion Inhibition of Aluminum
and Aluminum Alloys by Soluble Chromates, Chromate Coatings and Ch-
romate-Free Coatings, Corrosion, Vol 59 (5), p 379-400, 2003.
11. A. Rose and K. Legg, Predicting Corrosion in Military Aircraft, Vol 53,
p 58-60, 2014.
12. F. Ayello, et al., Internal Corrosion Threat Assessment of Pipelines using
Bayesian Networks, Corrosion 2014, San Antonio, NACE Intl., 3851, 2014.
13. D. Constantinis, Radiation Backscatter-Based Nondestructive Technol-
ogy Detects Corrosion Under Insulation on Offshore Oil and Gas Platforms,
Mater. Performance, Vol 53 (5), p 14-16, 2014.
14. U. Arumugam, Application of Plastic Strain Damage Models to
Characterize Dent with Crack, Corrosion 2013, Orlando, FL, NACE
Intl., 2858, 2013.