Microstructure and Corrosion Behavior of Laser-Welded Al-Mn-Zr Alloy for Heat Exchanger.

In this study, an Al-Mn-Zr alloy was designed and its microstructure and corrosion behavior compared after laser welding to that of AA3003. As the results of immersion and electrochemical tests showed, both alloys had a faster corrosion rate in the fusion zone than in the base metal. Laser welding caused interdendritic segregation, and spread the intermetallic compounds (IMCs) evenly throughout in the fusion zone. This increased the micro-galvanic corrosion sites and destabilized the passive film, thus increasing the corrosion rate of the fusion zone. However, Zr in the Al-Mn alloy reduced the size and number of IMCs, and minimized the micro-galvanic corrosion effect. Consequently, Al-Mn-Zr alloy has higher corrosion resistance than AA3003 even after laser welding.

Derivation of Corrosion Depth Formula According to Corrosion Factors in District Heating Water through Regression Analysis.

In order to predict the corrosion depth of a district heating pipeline, it is necessary to analyze various corrosion factors. In this study, the relationship between corrosion factors such as pH, dissolved oxygen, and operating time and corrosion depth was investigated using the Box-Behnken method within the response surface methodology. To accelerate the corrosion process, galvanostatic tests were conducted in synthetic district heating water. Subsequently, a multiple regression analysis was performed using the measured corrosion depth to derive a formula for predicting the corrosion depth as a function of the corrosion factors. As a result, the following regression formula was derived for predicting the corrosion depth: "corrosion depth (µm) = -133 + 17.1 pH + 0.00072 DO + 125.2 Time - 7.95 pH × Time + 0.002921 DO × Time".

Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil.

Soil corrosion is always a critical concern to corrosion engineering because of the economic influence of soil infrastructures as has been and has recently been the focus of spent nuclear fuel canisters. Besides corrosion protection, the corrosion prediction of the canister is also important. Advanced knowledge of the corrosion rate of spent nuclear fuel canister material in a particular environment can be extremely helpful in choosing the best protection method. Applying machine learning (ML) to corrosion rate prediction solves all the challenges because of the number of variables affecting soil corrosion. In this study, several algorithms of ML, including series individual, boosting, bagging artificial neural network (ANN), series individual, boosting, bagging Chi-squared automatic interaction detection (CHAID) tree decision, linear regression (LR) and an ensemble learning (EL) merge the best option that collects from 3 algorithm methods above. From the performance of each model to find the model with the highest accuracy is the ensemble stacking method. Mean absolute error performance matrices are shown in Fig. 15. Besides applying ML, the significance of the input variables was also determined through sensitivity analysis using the feature importance criterion, and the carbon steel corrosion rate is the most sensitive to temperature and chloride.

Evaluation of the Influence of the Combination of pH, Chloride, and Sulfate on the Corrosion Behavior of Pipeline Steel in Soil Using Response Surface Methodology.

External damage to buried pipelines is mainly caused by corrosive components in soil solution. The reality that numerous agents are present in the corrosive environment simultaneously makes it troublesome to study. To solve that issue, this study aims to determine the influence of the combination of pH, chloride, and sulfate by using a statistical method according to the design of experiment (DOE). Response surface methodology (RSM) using the Box-Behnken design (BBD) was selected and applied to the design matrix for those three factors. The input corrosion current density was evaluated by electrochemical tests under variable conditions given in the design matrix. The output of this method is an equation that calculates the corrosion current density as a function of pH, chloride, and sulfate concentration. The level of influence of each factor on the corrosion current density was investigated and response surface plots, contour plots of each factor were created in this study.

Calibrating the Impressed Anodic Current Density for Accelerated Galvanostatic Testing to Simulate the Long-Term Corrosion Behavior of Buried Pipeline.

Various studies have been conducted to better understand the long-term corrosion mechanism for steels in a soil environment. Here, electrochemical acceleration methods present the most efficient way to simulate long-term corrosion. Among the various methods, galvanostatic testing allows for accelerating the surface corrosion reactions through controlling the impressed anodic current density. However, a large deviation from the equilibrium state can induce different corrosion mechanisms to those in actual service. Therefore, applying a suitable anodic current density is important for shortening the test times and maintaining the stable dissolution of steel. In this paper, to calibrate the anodic current density, galvanostatic tests were performed at four different levels of anodic current density and time to accelerate a one-year corrosion reaction of pipeline steel. To validate the appropriate anodic current density, analysis of the potential vs. time curves, thermodynamic analysis, and analysis of the specimen's cross-sections and products were conducted using a validation algorithm. The results indicated that 0.96 mA/cm2 was the optimal impressed anodic current density in terms of a suitable polarized potential, uniform corrosion, and a valid corrosion product among the evaluated conditions.

Optimization of Cathodic Protection Design for Pre-Insulated Pipeline in District Heating System Using Computational Simulation.

Cathodic protection (CP) has been used as a primary method in the control of corrosion, therefore it is regarded as the most effective way for protecting buried pipelines. However, it is difficult to apply CP to a pipeline for district heating distribution systems, because the pipeline has thermally insulated coatings which could disturb the CP. Theoretical calculation and field tests alone are not enough for a reliable CP design, and therefore additional CP design methods such as computational analysis should be used. In this study, the CP design for pre-insulated pipelines is tested considering several environmental factors, such as temperature and coating defect ratio. Additionally, computational analysis is performed to verify and optimize the CP design. The simulation results based on theoretical methods alone failed to satisfy the CP criteria. Then, a re-design is conducted considering the IR drop. Consequently, all of the simulation results of defective pipelines satisfied the CP criteria after adding the proper CP current.