Materials Science Forum Vol. 1198

Abstract: Steel is a critical material in many industrial applications, particularly in the petroleum sector, where components are often exposed to water-containing crude oil. The presence of water in crude oil can accelerate corrosion processes that compromise extraction, transportation, and export operations, resulting in increased maintenance costs, operational downtime, and negative environmental impacts. Wettability, defined as the tendency of a liquid to spread on or adhere to a solid surface, provides essential insight into the interaction between liquid phases and metallic surfaces such as pipe steel, and can influence corrosion behaviour and adhesion characteristics. Contact angles are commonly used to quantify wettability, and are affected by both the properties of the liquid and the composition of the solid materials.This study reviews previous research and analyses experimental results to evaluate the influence of oil and water wettability on the surfaces of four steel pipe materials. The experimental investigation involved the measurement of contact angles of glycerin oil, hydraulic oil, petroleum, and a hydraulic oil/petroleum mixtures on four steel surfaces (1.4050 steel, 1.4301 steel, C60, and 42CrMo4) using KSV software to record dynamic changes in contact angle over a period of 5 minutes for each sample. The main observations were that the wettability of hydraulic oil and petroleum was better than that of glycerin oil and water measured on all types of steel surfaces. Moreover, the wettability of petroleum and hydraulic oil increased while water and glycerin oil decreased when the Cr content of the steel increased (for example, when Cr content was 18wt.%, Θ_petroleum= 8°, but Θ_water= 76° ).

Abstract: High voltage bare conductor surfaces were tested by spray method of IEC TS 63073. Theconductors have different surface properties: a standard conductor with untreated surface, a paintedone, a sand-blasted surface and three old conductors after long service life was tested. The oldconductors operate in significantly different areas of Hungary which makes the surfacecontamination and the rate of atmospheric corrosion different. For comparable tests specializedequipment was developed, which makes the spray test in the same way and with same parameters inall tests. The equipment contains digital cameras to make photos from the conductor’s surfacewhich can be compared to the chart of technical specification. The tests were conducted usingdifferent water qualities. The surface treated and old conductors show good hydrophilicity in alltests while the untreated and painted conductors wettability was wrong. The related technicalspecification designates classes where HC7 means the water film on the surface, while HC1 meanslarge drops of water on the surface. The surface treatment makes HC7 class surface of theconductors. The contamination and atmospheric corrosion also make HC6-HC7 class surface. Theas-manufactured or painted surface were evaluated as HC2-HC4. Acidic additions make thewettability class larger.

Abstract: This study aims to predict the effective Young’s modulus of Ti6Al4V lattice structures based on strut thickness and strut length using regression-based machine learning methods. Four predictive models, Linear Regression (LR), Polynomial Regression (PR), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were developed and compared in MATLAB using 25 sets of simulation data. Among these models, PR and GPR demonstrated the most promising performance in the actual versus predicted comparison, achieving the lowest root mean square error (RMSE) and the highest coefficient of determination (R²) for the given data size. Optimal training size for all models was around 36% to 52% of the total data set, which has a critical significance for data efficiency. To evaluate data sufficiency and model reliability, 5-fold Cross Validation was performed, and learning curves were generated to analyze how prediction error varies with the number of training samples. In the Learning curve, the PR model achieved its lowest RMSE, followed by GPR, which had the second best RMSE at its optimal training size. LR performed well for comparatively linear data, whereas SVR showed great variations and many shortcomings with the limited dataset.