Ryan Michael Katona

Nitrates, common atmospheric compounds, have been observed in environments where they can deposit on metal surfaces along with seawater aerosols. In many cases, nitrates have been shown to inhibit localized corrosion of stainless steels, a common infrastructure material component, at various nitrate:chloride ratios. In this study, full immersion polarization scans were performed on 304 Stainless Steel (SS304) coupons at a variety of scan rates, temperatures, and nitrate:chloride ratios to further examine the inhibitory influences. Additionally, atmospheric exposures were performed with droplet experiments in an atmospheric chamber under both static and diurnal relative humidity cycles. Atmospheric exposures examined the same initial nitrate:chloride ratios as explored in full immersion but also looked at influences of droplet size effects as well as dynamic conditions. Pit frequency and pit area calculations to quantify the effects of nitrates in these solutions were completed. It was found that the inhibition ratios for nitrates to chloride in full immersion do not translate directly to atmospheric conditions.

The safety and reliability of next-gen reactors, i.e. molten salt reactors (MSR), depends on materials performance and longevity in extreme environments, yet no in-situ, validated measurement techniques for environmentally assisted cracking (EAC), one potential degradation mechanism, exist for these conditions. This project combined the use of in-situ direct current potential drop (DCPD) crack growth rate determination applied to a horizontal load frame with high temperature fittings and novel sample geometries to enable evaluation in MSR environments and develop a full in-situ SCC measurement capability for extreme environments (high conductivity and elevated T), necessary to predict materials reliability. A successful demonstration of DCPD measurement in molten NaNO 3 /KNO 3 salts at 340 o C was accomplished and validated through post-test fractography imaging. This is the first ever public example, to the authors knowledge, of an in-situ crack growth rate measurement in a molten salt environment.

Complex physio-chemical phenomena can be investigated using high-fidelity physics-based finite element (FE) models. These models provide accurate predictions of the response of interest, as well as insights into its evolution over time. However, these models are computationally expensive, limiting widespread use. Machine learning (ML)-based protocols can significantly accelerate predictions from high-fidelity models. However, the training costs associated with these models can be prohibitive. This expense can be mitigated by effectively integrating results from lower-fidelity models. Therefore, a protocol that leverages low-fidelity results to accelerate high-fidelity predictions is desirable. This work introduces a multi-fidelity Gaussian process regression framework designed to emulate time-series outputs from high-fidelity models. The framework integrates low-fidelity model responses with Taylor-series approximations, facilitated by automatic differentiation, to propagate predictions and uncertainties across time and fidelity. The protocol is validated by accurately predicting both analytical and FE examples. A well-known example for time-series models, specifically the pendulum with moving support point and the evolution of the total current output of corroding galvanic couples in two case studies across five model fidelities are investigated. Although developed in the context of corrosion, this versatile ML framework shows immense potential for various engineering applications.

The influence of the external cathodic area, stress intensity, and electrochemical polarization level on the resultant crack growth rate and crack tip pH and potential was studied for stainless steel 304L in a sodium chloride environment. Before testing, the validity of in situ crack tip property measurements using microprobes and drilled holes was supported through modeling efforts and confirmed with experimental observations. It was found that increasing the R-ratio during corrosion fatigue increased the crack growth rate and decreased the measured crack tip pH of the 304L sample. Decreasing the electrochemical potential (i.e., cathodic polarization) of the bulk sample increased the crack tip pH and stopped measured crack extension under constant K conditions. Conversely, increasing the potential of the bulk sample decreased the crack tip pH and induced cracking under constant K conditions. Decreasing the cathodic area external to the crack increased the measured pH at the crack tip and decreased the crack growth rate under fatigue conditions. For the given configuration, environment, and alloy studied, fatigue crack growth is not sustained by cathodic reactions solely occurring in the crack wake and notch. These results have implications for the ability to predict atmospheric crack growth rates from laboratory, full immersion experiments.