## 1 Introduction
By taking the slope of the potential versus current curve, as the current flow is induced between the working and counter electrodes [10] and using the Stern-Geary equation, the resistance can then be used to find the corrosion rate of the material. Corrosion rate measurements in the flow loop have been done by various researchers to evaluate the flow accelerated corrosion and erosion. [PERSON] et al. [11] used a flow loop to investigate the corrosion resistance of SA-543 and X65 steels in an oil-water emulsion containing H\({}_{2}\)S and CO\({}_{2}\) using polarization curves. LPR was measured using a potentiostat and two-electrode system for 24 hrs at an interval of 30 min. The effect of salt concentration and pH on corrosion rate was not considered in this work. [PERSON] et al. [12] investigated the flow accelerated corrosion of oil field water in the loop system at pipe elbows at turbulent conditions using the LPR method and correlated the corrosion rates with fluid velocities and shear stresses, and the electrochemical results were validated using CFD simulation results. [PERSON] et al. [13] used the polarization method to evaluate the corrosion behavior of X52 steel at an elbow of a loop system and its correlation with flow velocity, shear stress, and the volume fraction of particles. The work by [PERSON] et al. [14] did a parametric study to evaluate the effects of CO\({}_{2}\) on corrosion rates of carbon steels (C1010, C1018, and X65) using a flow loop. Localized corrosion rates were determined at two temperatures (40 \({}^{\circ}\)C and 90 \({}^{\circ}\)C), pH, salt concentrations, flow regimes, and CO\({}_{2}\) partial pressure using the LPR method. In [14], the salt concentration was varied from 0 to 1%. The effect of flow rate on corrosion rate was not considered in this work, and the superficial gas and liquid velocities were assumed as 10 m/s and 0.1 m/s, respectively. One of the earliest corrosion prediction models was by [PERSON] and [PERSON] [15] in 1975, where the authors studied the relationship between the corrosion rate on grit blasted steel specimens (X52) and partial pressure of CO\({}_{2}\). [PERSON] provided a model to determine the corrosion rates incorporating the effects of pH and temperature. The relation was modified in his later works and provided a modified equation [16], which accounts for the effect of dissolved iron at lower and higher temperatures. Corrosion studies of 316 Stainless Steel were performed by [PERSON] et al. [17] using oil-water in a flow loop, and the rates were determined using electrical resistance probes and weight loss methods. The findings were used to develop a predictive equation for corrosion rates at different temperatures, flow velocities, and carbon dioxide partial pressure for the oil-water mixture. In recent works, the CO\({}_{2}\)-multi-phase flow corrosion model is presented by [PERSON] et al. [18], which considers the kinetics of electrochemical reactions, diffusion effects, and the effect of steel type. The model also considers the kinetics of scale growth and precipitations and accounts for the effect of steel types, multi-phase flow, and H\({}_{2}\)S. Nothing is mentioned of the steel types in the paper except they are mild steels. [PERSON] [19] developed a corrosion prediction tool HYDROCOR, a computer-based engineering spreadsheet for localized corrosion in carbon pipelines considering various scenarios in the field. The model accounts for various environments with corroding agents like CO\({}_{2}\), H\({}_{2}\)S, (organic) acids, bacteria, and oxygen, and the spreadsheet model is coupled with models for water chemistry, multi-phase flow, oil protectiveness, heat transfer, and thermodynamics using FORTRAN to predict different scenarios. [PERSON] et al. [20] used \"a Case-based Reasoning-Taylor Series (CBR-TS) model\" to predict the corrosion behavior in field pipelines considering operation parameters.