Abstract
Flow-accelerated corrosion (FAC) is influenced by the mass transfer coefficient (km), the solubility of ferrous iron species (Ceq), and material properties. This study presents a decoupled FAC Utility to estimate wall thinning (TL) by integrating erosion and corrosion mechanisms. The erosion component is computed using shear velocity (Uτ) from steady-state Computational Fluid Dynamics (CFD) simulations and specified ferrous iron diffusivity (D). The corrosion component is derived from a chemistry model that incorporates temperature (T), molality (mA), pH, and a constant chromium effect factor (1-β).
The FAC Utility is validated using velocities of 1.5, 3.0, and 4.5 m/s, temperatures ranging from 50 to 150 °C, and pH values of 8.9 and 7.0 in both 2D and 3D geometries. In 2D simulations, peak TL values occur 5–15 mm downstream of the turbulence promoter for velocities of 3.0 and 4.5 m/s, with localized spikes exceeding experimental maxima but matching average values. At 1.5 m/s, peak TL occurs between 4–12 mm, though the model slightly underpredicts. In 3D simulations, TL values range from 0.5 to 0.75 mm per 10,000 h, with peak locations consistently between 5 and 15 mm, aligning well with experimental data. The predicted Ceq of 1.1e-6 mol/kg also matches literature values.
Temperature-dependent validation at 4.5 m/s and pH 7.0 confirms the model’s accuracy, with TL values between 0.1 and 0.22 mm per 10,000 h for temperatures from 50 to 80 °C, consistent with experimental trends. The FAC Utility effectively predicts wall thinning under varying flow and thermal conditions.
Introduction
Flow-accelerated corrosion (FAC) involves two primary mechanisms: (i) the production of soluble iron at the oxide–water interface, and (ii) the transport of soluble and particulate corrosion products from the surface to the bulk fluid across the diffusion boundary layer. Material properties, water chemistry, and hydrodynamic factors such as temperature, ferrous ion concentration, chromium content in the alloy, pH, flow regime, and the presence of oxidants influence the rate of wall thickness loss due to FAC. Total material degradation results from corrosion, driven primarily by electrochemical reactions, and erosion, governed by mechanical forces acting on the surface. Hydrodynamic parameters—including fluid velocity, surface roughness, pipe geometry, and fluid quality—significantly affect the mass transfer of corrosion products. Corrosion and erosion processes often act synergistically, leading to enhanced material loss.
Flow-accelerated wall thinning poses a significant challenge in several industries, including power generation, oil and gas, aerospace, marine, chemical processing, district heating and cooling, water treatment, mining, and automotive sectors. This phenomenon occurs when high-velocity fluid interacts with metallic surfaces, leading to gradual degradation and potential safety risks.
Foundational work by Sweeton and Baes (1970) [1] focused on the thermodynamic equilibrium of Fe3O4 in acidic and basic aqueous solutions at elevated temperatures. Their study provided a method to compute the equilibrium constants of various ferrous ion species as a function of temperature, using least-squares fitting. A nonlinear expression was developed to estimate the molality of H+, which was used to determine ferrous ion concentrations.
In 1986, Poulson and Robinson [2] established a relationship between the mass transfer coefficient and hydrodynamic parameters through dimensionless analysis, which they validated experimentally using corroding copper specimens in dilute hydrochloric acid. Remy and Bouchacourt (1990) [3] introduced a non-dimensional wall-thinning kinetic number and proposed a sensitivity-based classification of components in power plant systems to optimize inspection frequency and scope. Later, Yoneda et al. (2016) [4] conducted a series of experiments to quantify wall thinning in carbon steel (JIS STPT480) under varying flow velocities (1.5–4.5 m/s), temperatures (50–150 °C), and pH values (7.0–9.8), reporting corrosion rates in mm per 10,000 h.
Building on experimental insights, numerical methods have been widely adopted to predict wall-thinning behavior and estimate mass transfer coefficients. Chang et al. (2006) [5] performed a one-way fluid–structure interaction (FSI) analysis on a thinned pipe, where hydrodynamic forces obtained from computational fluid dynamics (CFD) simulations at various velocities were applied to finite element analysis (FEA). Their FSI-based predictions showed a 2–10 % increase over conventional FEA results. In 2016, Sun and Ding [6] conducted an FSI study on welded pipes with different chromium contents upstream and downstream of the weld, examining the combined effects of FAC, entrance effects, and weld residual stresses. Building on earlier experiments, Yoneda et al. (2017) [7] proposed a CFD-based method to calculate pipe wall friction and derive the mass transfer coefficient, including the Reynolds number exponent, which was validated against power plant data. Su et al. (2024) [8] investigated significant pipeline thinning downstream of a butterfly valve in a hydrogenation unit by combining microstructural analysis of FeS2 corrosion products with CFD simulations. They demonstrated that flow restriction by the valve disc induces high-velocity jets and elevated wall shear stress in regions of severe thinning, thereby elucidating the electrochemical-erosive FAC mechanism.
Although CFD offers detailed insights into FAC phenomena, its high computational cost and complexity limit its utility in real-time applications, especially for large-scale systems. As a result, process simulation methods have gained prominence. Elmegaard and Houbak (2002) [9] reviewed academic and commercial simulators, highlighting their capabilities, implementation strategies, limitations, and future development needs.
Complementing numerical and simulation-based tools, software platforms for power plant life-cycle management have been developed. Siemens KWU initiated the WATHEC (Wall Thinning due to Erosion Corrosion) code in 1973, supported by extensive laboratory validation using non-destructive testing. In 1998, AREVA enhanced this tool into the COMSY software [10], employing the Kastner model [11] to support component life-cycle planning. Similarly, Électricité de France (EDF) developed the BRT-CICERO code [12], based on the Sanchez-Caldera model [13] for monitoring FAC in the secondary piping systems of pressurized water reactors and assisting in inspection scheduling.
Recent studies have also explored data-driven approaches to improve prediction speed and scalability. For example, Chae et al. (2019) [14] employed a hybrid machine learning model—combining support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM) networks—using vibration data to predict FAC-induced wall thinning, demonstrating accurate results across various conditions.
This study introduces a computational framework developed within the OpenFOAM environment to predict wall thickness loss due to flow-accelerated corrosion (FAC) in single-phase, deoxygenated, and alkalized aqueous systems. The proposed methodology employs a decoupled approach to independently calculate two key parameters influencing FAC: the mass transfer coefficient and the concentration of ferrous iron species. The mass transfer coefficient is determined as a function of wall shear stress and effective kinematic viscosity derived from steady-state CFD simulations. Meanwhile, the ferrous ion concentration is computed using the equilibrium relationships proposed by Sweeton and Baes [1], incorporating CFD-derived temperature, pH, and molality values. To account for temperature-dependent diffusivity of ferrous ions, the model permits either a single constant input value or a temperature-dependent tabulated dataset. However, the current implementation uses a constant chromium effect factor, excluding potential temperature variations in the chromium content.
The structure of the article is as follows: Section 2 details the numerical methodology, including governing equations, solver implementation, turbulence modeling, and formulations for wall thinning and mass transfer. Section 3 presents the two-dimensional (2D) validation case, discussing case setup, meshing strategy, and FAC utility calculations. Section 4 provides the results of 2D simulations, introduces the three-dimensional (3D) case, and analyzes the impact of temperature and shear velocity on wall thinning behavior.
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