Computation Assisted Materials Development for Improved Corrosion Resistance: A: Low Temperature Corrosion
Program Organizers: Rishi Pillai, Oak Ridge National Laboratory; Laurence Marks, Northwestern University
Wednesday 8:00 AM
October 20, 2021
Room: A222
Location: Greater Columbus Convention Center
Session Chair: David Shifler, Office of Naval Research
8:00 AM Introductory Comments
8:05 AM Invited
Factors That Influence Materials Corrosion and How Modeling May Predict These Effects: David Shifler1; 1Office of Naval Research
The Naval environment is very aggressive. Seawater affects nearly all structural materials to some extent. Marine corrosion is dependent on a number of factors such as environmental zone, alloy composition, water or fuel chemistry, pH, biofouling, microbiological organisms, pollution and contamination, alloy surface films, geometry and surface roughness, galvanic interactions, fluid velocity characteristics and mode, oxygen content, heat transfer rate, quality of air intake, and temperature. Understanding how these factors both at ambient and at temperatures approaching 1000-1500C may influence corrosion in the marine environment will provide keys to mitigation and control efforts. Computational and physical modeling of these effects combined with machine learning and experimental validation can provide insight and guidance to comprehend major and minor elements influencing corrosion that can subsequently provide a pathway to the creation and development of new materials and emerging corrosion control technologies.
8:35 AM
Back to the Basics: Revisiting Copper to Build Thermodynamic Corrosion Models: Lauren Walters1; Liang-Feng Huang2; James Rondinelli1; 1Northwestern University; 2Ningbo Institute of Materials Technology and Engineering
We present a thermodynamic model of the corrosion behavior of copper, subject to multiple environmental factors, e.g., solution pH, electrode potential, temperature, and pressure, assessed through density functional theory calculated Pourbaix diagrams. Existing discrepancies between thermodynamically predicted and electrochemically observed behaviors of copper in aqueous electrochemical conditions are addressed. Additionally, to reduce computational resources and utilize high-fidelity DFT methods, we introduce a revised correct-relative-chemical-potential (CRCP) scheme that leverages highly accurate hybrid density functionals that include non-local Fock exchange. Our work demonstrates best practices for using first principles calculations and methodologies and how to obtain new insight for the design of improved corrosion resistant materials.
8:55 AM
Computational Modeling of Corrosion and Mechanical Failure in Magnesium-Aluminum Vehicle Joints: Kubra Karayagiz1; Adam Powell1; Qingli Ding1; Brajendra Mishra1; 1Worcester Polytechnic Institute
The joining of Al and Mg is challenging due to galvanic corrosion, a common issue in dissimilar metal joints. While the advanced joining technologies, such as friction stir welding, produce fine-grained microstructure leading to slower corrosion, a better understanding of corrosion mechanisms on performance is needed for widespread adoption of this technique in the auto industry. Computational modeling tools to study the corrosion and mechanical failure in friction stir welded Al-Mg vehicle joints are presented in this work. First, a phase-field model to study corrosion in Al-Mg joints is developed. The model accounts for the conservation of charge, transport of ions in the electrolyte, and the electrochemical reactions at the metal-electrolyte interface. Next, a finite element model is developed to study the effect of corrosion on joint performance. The geometry of the mechanical hooks is extracted from the scanning electron microscopy images. Experiments are conducted for validation purposes.
9:15 AM
Development of a Damage Function for Galvanic Corrosion Degradation of Coated Al Alloy Systems: Mahdi Jokar1; Gerald Frankel1; 1Ohio State University
Artificial Neural Networks (ANNs) and random forest regression were used to develop a predictive damage function for galvanic corrosion of 7075-T6 Al alloy panel with different coating systems in various environmental factors. In this research, lost volume has been modeled based on different parameters such as pretreatment, primer coating, topcoat, chloride concentration, RH, galvanic current, impressed current and environment. The best model was based on lost volume as output and the same factors except environment descriptors as inputs. The room mean square error (RMSE) for this function was 0.2 mm3. Although the RMSE was higher than for some other models, it is more realistic not to use standard environments as an input. To predict lost volume in this formula, the ANN model involved three nodes in one hidden layer with hyperbolic tangent functions. The ANN was able to get a good fit for training and validation (RMSE=0.2 and R2=0.7).