Corrosion Model Research Articles

Spatial distribution of corrosion products from a bridge pier

AbstractThis paper studies the spatial distribution of corrosion by-products by a bridge pier within a conductive medium. An electrochemical impedance spectroscopy (EIS) technique was used to investigate an uncoated metallic bridge pier submerged in static distilled water. An equivalent circuit model, derived from EIS results, served as the foundation for the study. Further, the role of diffusion was analysed, considering its significance in characterising the transfer of particles from the pier into the surrounding water. This exploration revealed the complex interaction between the diffusion processes of various corrosion by-products as a function of distance. In addition, by evaluating the spatial distribution of iron (II) corrosion by-products and modelling nanoparticle diffusion, the research examined the impact of diffusion and concentration on corrosion particle transmission. The findings, analysed via Nyquist and Bode plots, demonstrate significant differences between theoretical and empirical diffusion coefficients. Results indicated that under natural corrosion conditions, the primary product of the corrosion reaction, iron (II), disperses into the medium when oxidation occurs. The elevated resistivity due to the presence of iron (II) underscores the diffusion effect, leading to corrosion product precipitation and reaching saturation level. Additionally, the results demonstrated ideal values for the diffusion coefficient, which are crucial for advanced corrosion modelling. The results emphasised the need for empirical data to improve corrosion prediction models and informed maintenance strategies for submerged structures.

Nonlocal Nernst-Planck-Poisson System for Modeling Electrochemical Corrosion in Biodegradable Magnesium Implants

AbstractThis paper provides a comprehensive derivation and application of the nonlocal Nernst-Planck-Poisson (NNPP) system for accurate modeling of electrochemical corrosion with a focus on the biodegradation of magnesium-based implant materials under physiological conditions. The NNPP system extends and generalizes the peridynamic bi-material corrosion model by considering the transport of multiple ionic species due to electromigration. As in the peridynamic corrosion model, the NNPP system naturally accounts for moving boundaries due to the electrochemical dissolution of solid metallic materials in a liquid electrolyte as part of the dissolution process. In addition, we use the concept of a diffusive corrosion layer, which serves as an interface for constitutive corrosion modeling and provides an accurate representation of the kinetics with respect to the corrosion system under consideration. Through the NNPP model, we propose a corrosion modeling approach that incorporates diffusion, electromigration and reaction conditions in a single nonlocal framework. The validity of the NNPP-based corrosion model is illustrated by numerical simulations, including a one-dimensional example of pencil electrode corrosion and a three-dimensional simulation of a Mg-10Gd alloy bone implant screw decomposing in simulated body fluid. The numerical simulations correctly reproduce the corrosion patterns in agreement with macroscopic experimental corrosion data. Using numerical models of corrosion based on the NNPP system, a nonlocal approach to corrosion analysis is proposed, which reduces the gap between experimental observations and computational predictions, particularly in the development of biodegradable implant materials.

Corrosion-life prediction model for 316L stainless steel under electronic special gases containing trace moisture employed in semiconductor manufacturing industry

Special gases with trace moisture cause the formation of dynamic acidic microdroplets (DMD), which results in corrosion of semiconductor manufacturing devices. In this study, a predictive model for corrosion damage in 316L stainless steel (SS) was developed by combining the DMD process and the pitting initiation model. The DMD process was modeled using the BET model to describe the moisture-to-solution conversion. The pitting initiation model was reconstructed by incorporating the Sridhar model, temporal corrosion model, and Macdonald model. Finally, the predicted results were validated by various experimental data, indicating that the prediction model was accurate and highly reliable.

Service life prediction model for biogenic acid corrosion: Advancement of life factor method for concrete sewer design

Current service life prediction models for concrete sewer design, including the widely used Life Factor Method (LFM), do not efficiently incorporate certain key corrosion contributing factors, such as material selection, concrete mix design, and sewer environment. This paper describes an advancement of the LFM model to cover these factors, broaden its usage, and improve concrete sewer design and applications. By integrating the first principles of the original LFM model, advanced knowledge from the literature, and results from field performance studies of various sewer concretes, a practical and relatively simple LFM model was advanced. With concrete mix design, oxide composition of all concrete ingredients, and sewer environmental and hydraulic conditions, the so-called ‘advanced LFM model’ could predict the corrosion rate of various concrete mixes. The model emphasised the influence of various modern binders, including calcium aluminate and calcium sulpho-aluminate cements, and various aggregate types including siliceous, calcareous, and alumina-based aggregates, when subjected to different sewer environments.

Bayesian updating of relationships between crack width and corrosion level in reinforced concrete based on a large set of experimental data

AbstractThe evaluation of rebar corrosion in reinforced concrete (RC) structures presents a significant challenge for structural engineers, with on‐site visual inspections and crack width measurements playing a key role in the preliminary assessment. A general approach is to use corrosion models to predict the corrosion level of existing structures, and subsequently update these predictions by means of on‐site data. In this process, empirical models are often applied that relate the observed damage, such as corrosion‐induced cracks, to the corrosion level. However, these models have large uncertainties and are often not applicable to conditions that deviate from the test setups used to derive the empirical relations. This research aims to expand the applicability of these practical, existing empirical models for on‐site corrosion assessment through Bayesian updating techniques. To this aim, a Bayesian framework is developed to update crack width–corrosion models. The Bayesian updating methodology is illustrated for a simple regression model, and for a more complex relation that accounts for additional variables. A novel online database (KUL‐edCCRC) is used that is published accompanying this paper. The obtained results illustrate that the updated models have better agreement with the experimental results, and it is found that the confidence intervals of the regression parameters decrease for an increasing number of observations, even for a small number of additional datapoints. The results hence prove the efficiency of the approach to integrate information from new observations with prior information to adjust the crack width–corrosion model for specific conditions or cases, resulting in a more relevant model as more information becomes available.

Spatial Modeling and Economical Evaluation of Water Corrosion and Scaling in Water Distribution Network

Spatial Modeling and Economical Evaluation of Water Corrosion and Scaling in Water Distribution Network

Key Challenges for Internal Corrosion Modelling of Wet Gas Pipelines

Abstract The presence of CO2 and H2S in wet gas pipelines often creates the potential for high internal corrosion rates, which is typically mitigated by the injection of corrosion inhibitors. In practice, however, it is difficult to ensure that the inhibitor is always injected at the right level, while actual conditions in the pipeline may sometimes vary from those for which the inhibitor was qualified. Consequently, pipelines are also likely to be inspected from time to time using In-Line-Inspection tools. Various empirical and mechanistic models are used to estimate corrosion rates in such pipelines, both during the design phase to establish corrosion allowances and inhibitor availability requirements, and then during operation to help interpret inspection results and guide further operational decisions. These models can differ considerably in how they incorporate the effects of surface scaling, while the effects of inhibitors are generally not included in any mechanistic sense. This paper provides an overview of corrosion assessment for wet gas pipelines, with a particular focus on recent developments in modeling scale formation and the influence of inhibitors.

Exploring machine learning to study and predict the chloride threshold level for carbon steel reinforcement

Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of deterioration in reinforced concrete structures, posing a significant infrastructure challenge. The chloride threshold level (CTL) of rebar, which represents the critical amount of chloride needed to initiate active corrosion, is crucial in corrosion and service life prediction models. However, substantial uncertainties and a multitude of influencing factors, along with the absence of a universally accepted testing framework, hinder the achievement of a consistent CTL range for service life models and complicate comparisons of published values. This study addresses these challenges by developing multiple machine learning models to predict CTL, considering 21 carefully selected features. A comprehensive database of 423 data points was compiled from an exhaustive literature review. Seven machine learning models—linear regression, decision tree, random forest, K-nearest neighbors, support vector machine, artificial neural network, and an ensemble model—were developed and optimized. The ensemble model achieved superior prediction performance, with a mean absolute error of 0.218 % by weight of binder, root mean square error of 0.321 %, and a coefficient of determination of 0.751 on unseen CTL data. Partial dependence plots generated using the support vector machine model quantified the effect of each feature on CTL. The random forest model identified SiO₂ binder content and exposed rebar area to chlorides as the most influential factors. The study also examined the impact of supplementary cementitious materials (SCMs), finding that only blast furnace slag positively affected CTL.

Corrosion performance of super duplex stainless steel and pipeline steel dissimilar welded joints: a comprehensive investigation for marine structures

This study investigates the corrosion behavior of dissimilar gas tungsten arc (GTA) welded joints between super duplex stainless steel (sDSS 2507) and pipeline steel (X-70) using electrochemical and immersion corrosion tests. The GTA welds were fabricated using ER2594 and ER309L filler metals. The study examined the electrochemical characteristics and continuous corrosion behavior of samples extracted from various zones of the weldments in a 3.5 wt.% NaCl solution, employing electrochemical impedance spectroscopy, potentiodynamic polarization methods, and an immersion corrosion test. EIS and immersion investigations revealed pitting corrosion in the X-70 base metal/X-70 heat-affected zone, indicating inferior overall corrosion resistance due to galvanic coupling. The corrosion byproducts identified in complete immersion comprised α-FeOOH, γ-FeOOH, Fe3O4, and Fe2O3, whereas γ-FeOOH and Fe3O4 were predominant in dry/wet cyclic conditions. Corrosion escalated with dry/wet cycle conditions while maintaining a lower level in complete immersion. The corrosion mechanism involves three wet surface stages in dry/wet cycles and typical oxygen absorption during complete immersion. Proposed corrosion models highlight the influence of Cl−, O2, and rust layers.

A probabilistic time-dependent corrosion wastage model for offshore platforms based on reverse diffusion model

Corrosion wastage poses significant challenges to offshore platforms, making accurate corrosion depth prediction crucial for structural integrity and extending service life. This study addresses the limitations in measurement processes by integrating a reverse diffusion model into the traditional probabilistic corrosion wastage model, enabling comprehensive analysis of corrosion data. The reverse diffusion model effectively manages data uncertainty and noise, leading to a proposed probabilistic model that incorporates measurement uncertainty. This model not only mitigates data noise and errors but also improves the reliability and robustness of corrosion depth predictions for offshore platforms. Initially, statistical analysis of corrosion data from offshore mobile platforms confirmed that the corrosion rate and depth distribution of local components follow a Weibull distribution. The effectiveness of the reverse diffusion model was validated by comparing the probability distributions of original data with those processed using the model. Finally, leveraging the optimized data and an empirical corrosion model for ships, a probabilistic model for time-dependent corrosion wastage in offshore platforms was developed, and its reliability was assessed. The proposed model enhances the accuracy of corrosion predictions, contributing to better management of corrosion risks in offshore platform structures.

Corrosion-induced thickness diminution of an ageing bulk carrier

The 2030 Agenda for Sustainable Development introduced 17 Sustainable Development Goals (SDGs). The International Maritime Organization (IMO) has endorsed connections between the maritime sector and SDGs, emphasising sustainable production in the marine environment. Consequently, technical considerations, such as ship structure sustainability, are crucial for marine safety. The impact of corrosion influences the structural degradation of vessels and the risk of structural failure and fuel oil spills significantly. The formulation of predictive corrosion models, grounded in historical data accumulated during vessel operations, emerges as an optimal strategy for designing structural elements and guaranteeing their structural integrity. This paper analyses non-linear deterministic and stochastic corrosion models, focusing on an ageing bulk carrier, to provide design-phase information for sustainable structures. Utilising data on measured steel plate thickness over a 20-year operational period, non-linear models are employed to calculate the millimetres of thickness diminution. Assuming corrosion onset at 7, 8, and 9 years, corresponding coefficients are computed, based on the developed model. A model is also considered, in which there were no assumptions about the beginning of corrosion initiation. The corresponding annual corrosion rate was observed separately in each of the models, as a deterministic variable and as a probabilistic random variable. This approach aims to enhance the understanding of the causal relationship between corrosion processes and structural performance, contributing to the development of sustainable design practices in the maritime industry.

Numerical Simulation and Modeling of the Trielectrode-Coupled Corrosion Mechanism at the Laser-Welded Interface of N80 Steel

Laser welding, a prevalent technique in automotive structure manufacturing, can introduce compositional disparities and stresses within the weld zone, exacerbating corrosion complexity at the weld interface. This paper develops a triple galvanic corrosion model for N80 steel welds under stress conditions, leveraging the COMSOL software for simulations. The findings demonstrate that, within the elastic stress range, an elevation in stress intensifies the overall electrode surface potential, ultimately transitioning into plastic stress and fostering a more negative potential. This, in turn, amplifies the cathode-anode potential difference, rendering the material more susceptible to corrosion. The deeper the depression between the weld zone and the heat-affected zone, the higher this potential difference becomes, exacerbating the corrosion risk. Under stress, the corrosion rate markedly surpasses that observed in unstressed conditions. During the 160 day corrosion period, the weld seam and heat affected zone undergo a transition from plastic strain to elastic strain, while the base metal zone experiences both plastic strain and elastic strain simultaneously. This dynamic strain evolution leads to temporal variations in the stress-induced cathode-anode potential differences. In comparison to stress-free corrosion scenarios, the impact of stress on the cathode-anode potential difference initially intensifies, yet this effect tapers off over time.

Modeling of zirconium alloy cladding corrosion behavior based on neural ordinary differential equation

Current zirconium alloy cladding corrosion models are mainly semi-empirical and show significant dispersion when compared to measured data. This study introduces neural ordinary differential equation (neural ODE) to model corrosion behavior, utilizing data-model fusion approach for network training. Initially, a semi-empirical model for zirconium alloy cladding corrosion is established through differential evolution algorithm, generating a large dataset for pre-training the neural network. The network is then fine-tuned using measured data. These methods effectively address the challenges of sparse cladding corrosion data and data available only at fixed time points, resulting in a more accurate model. The results shows that the differential evolution algorithm can identify a set of appropriate parameters for the semi-empirical model, achieving a standard deviation of 0.040. The neural ODE model demonstrates even higher accuracy, reducing the standard deviation to 0.031 and improving accuracy by approximately 25%. Additionally, the model demonstrates excellent generalization capacity on other time points and new power histories.

Investigation of Metal–H2O Systems at Elevated Temperatures: Part I. Development of a Solubility Apparatus Specialized for Super-Ambient Conditions

Abstract Metals used in aqueous environments where high temperatures and pressures are present are susceptible to corrosion. This is the case for nuclear power plants, especially CANDUTM reactors, where the liquid water systems can reach over 300 °C at pressures well above 101.325 kPa (1 atm). In such situations, failure to control corrosion has economic and safety consequences. To extend corrosion modeling tools, such as Pourbaix diagrams, to harsh aqueous conditions, there is a need for experimental thermochemical data performed at elevated temperatures. This is particularly true for metal and alloy systems where such information is unavailable or unreliable. A relatively simple approach to obtaining temperature dependent thermodynamic properties is the investigation of solid–liquid phase, or solubility, equilibria. However, this requires specially designed instrumentation that can withstand harsh temperatures and extreme pH conditions, while providing accurate data. In this work, an apparatus developed for super-ambient highly acidic and alkaline solubility experiments is presented. Solubility measurements were made in the zinc oxide system, which has been extensively studied. Using these equilibrium data and comparing to the literature, allowed the instrumentation and analysis process to be validated. In situ pH measurements using a constant volume, batch reactor system are described along with a brief presentation of the ZnO dissolution equilibria results at 85 °C (358.15 K).

Cellular Automata Simulation of Pitting Corrosion of Stainless Steel in Marine Environments

Pitting corrosion poses a significant hidden risk to the reliable operation of stainless steel equipment. In this study, the evolution of pitting corrosion in stainless steel in marine environments was simulated by developing two-dimensional cellular automata (CA). The model demonstrated its capability to generate corrosion sectional views and top views that were consistent with experimental results. The simulation results obtained from the CA pitting corrosion model converged with the experimental data, yielding minimal errors of 4%. This convergence substantiated the effective ability of the model to simulate pitting corrosion evolution. When the CA model was used to predict pitting development in stainless steel, the obtained predicted values aligned within a 10% margin of error compared with experimental values. The developed CA model was used as a foundation to investigate the effect of chloride ion concentration on both pitting rate and pit morphology. The result showed that the impact of chloride ions concentration on pitting growth was more significant when concentrations were below 3.5wt.%. The establishment of the CA model makes it possible to simulate and predict pitting development patterns, thereby providing guidance for ensuring the safe operation of marine equipment.

Corrosion behavior and mechanism of Cr10Mo1 steel subjected to different corrosive ions in simulated concrete pore solution

The corrosion of pre-passivated Cr10Mo1 steels in simulated concrete pore solution of saturated Ca(OH)2 induced by different corrosive ions were investigated, namely Cl−, SO2- 4, and combination of Cl− and SO2- 4. Electrochemical methods, Pourbaix analysis of Fe–Cr–H2O system and DFT calculation were adopted to characterize corrosion behavior and reveal underlying mechanism. Results showed that while sulfate may induce corrosion at higher threshold value, the scenario of chloride alone leads to lowest corrosion potential, linear polarization resistance and highest corrosion rate, and the presence of SO2- 4 ions mitigate corrosion risk under the coexistence condition of chloride and sulfate. A two-step reaction model of competitive adsorption-catalytic corrosion was proposed based on theoretical analysis and DFT calculation results.

New insights into the corrosion mechanism of 316Ti austenitic steel exposed to 550 ℃ lead-bismuth eutectic with 5×10−6 wt% and 5×10−7 wt% oxygen concentration

The corrosion behavior of 316Ti steel in Lead-Bismuth Eutectic (LBE) was studied in this work. It reveals a transition in external oxidation between 5×10−6 wt% and 5×10−7 wt% DOC. The columnar magnetite (Fe3O4) formed under 5×10−6 wt% DOC grew via solid-state diffusion, while metal-dissolution/oxide-precipitation mechanism guided the external oxidation under 5×10−7 wt% DOC, forming equiaxed magnetite. Internal oxidation is influenced by the Ni-dissolution which facilitates the inner oxide growth. The internal oxide layer formed under 5×10−7 wt% DOC exhibit a better resistance to LBE penetration and oxide cracking. A corrosion model was proposed to elucidate the influence of DOC on the corrosion behavior of 316Ti steel.

A nonlinear phase-field model of corrosion with charging kinetics of electric double layer

Abstract A nonlinear phase-field model is developed to simulate corrosion damage. The motion of the electrode-electrolyte interface follows the usual kinetic rate theory for chemical reactions based on the Butler-Volmer equation. The model links the surface polarization variation associated with the charging kinetics of an electric double layer (EDL) to the mesoscale transport. The effects of the EDL are integrated as a boundary condition on the solution potential equation. The boundary condition controls the magnitude of the solution potential at the electrode$-$electrolyte interface. The ion concentration field outside the EDL is obtained by solving the electro$-$diffusion equation and Ohm's law for the solution potential. The model is validated against the classic benchmark pencil electrode test. The framework developed reproduces experimental measurements of both pit kinetics and transient current density response. The model enables more accurate information on corrosion damage, current density, and environmental response in terms of the distribution of electric potential and charged species. The sensitivity analysis for different properties of the EDL is performed to investigate their role in the electrochemical response of the system. Simulation results show that the properties of the EDL significantly influence the transport of ionic species in the electrolyte.

Investigation of the time-dependent bearing capacities of concrete structures in different environments considering climate change

The escalating trend of emissions of greenhouse gases, including CO2, prompts significant modifications in climate variables, such as temperature and relative humidity (RH). For concrete civil infrastructures, the carbonation and chlorination corrosion processes are sensitive to changes in climate variables. The focus of this study is to evaluate the time-sensitive deterioration of concrete components in different corrosion environments with consideration of large uncertainties of climate change. Future projections for atmospheric CO2, temperature, and RH until the end of the twenty first century are examined. By implementing an aging corrosion model that incorporates climate change, the study observes that under the SSP5-8.5 emission scenario, the average carbonation depth for the RC structures in the general atmospheric environment increases by 58.7%. Furthermore, the chlorination corrosion rate in marine environments nearly doubles that under aging conditions alone. By 2100, corrosion-affected RC beams in the SSP5-8.5 scenario will lose 9.5 and 29.2% of their flexural capacity, respectively. Moreover, under carbonation and chlorination environments, the beams will face an additional loss of 20.7 and 27.6% in shear capacity, respectively. In performing the above calculations, we have considered the uncertainty prediction of climate change and the uncertainty analysis of the influence of model parameters and material variability.

Research on eccentric-compressive behavior of steel stub columns with localized corrosion

To explore the impact of localized corrosion on the mechanical properties of hollow steel columns under eccentric loads, a specialized localized immersion-accelerated corrosion apparatus was developed. This device facilitated the creation of 17 locally corroded specimens, all subjected to eccentric loading. A systematic examination was conducted on various corrosion parameters including corrosion length, corrosion angle, localized corrosion rate, and corrosion location as well as loading conditions, which encompassed eccentricity and loading angle. Surface corrosion morphology and statistical parameters of the specimens were obtained using a 3D scanner. Subsequently, a nonuniform corrosion model, based on element displacement, was formulated through finite element analysis. Both experimental findings and numerical simulations indicate that localized corrosion does not alter the failure mode of locally corroded circular steel tubes (LCCSTs), which predominantly undergo local buckling in the compressed region. However, it was noted that localized corrosion leads to the elimination of the ascending segment in the load-displacement curve of the LCCSTs, thereby reducing both the ultimate load and ductility. Interestingly, the ultimate load of the LCCSTs was found to be relatively unaffected by variations in corrosion length and location but significantly influenced by factors such as localized corrosion rate, corrosion angle, eccentricity, and loading angle. Furthermore, through a comparative analysis of existing calculation models for the ultimate load of LCCSTs in major specifications, a practical model that accounts for localized corrosion is proposed. This model demonstrates high accuracy and is expected to enhance the understanding and prediction of the ultimate load capacity of LCCSTs in real-world applications.