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Donohoe, Brendan

Publications and source records attributed to Donohoe, Brendan.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Pit growth kinetics in aluminum: effects of salt loading and relative humidity

Abstract The growth kinetics of localized corrosion, e.g. pits, in corrosive environments often controls the service life of metallic components. Yet, our understanding of these kinetics is largely based on coupon-level, e.g. mass-loss, studies which provide limited insights into the evolution of individual damage events. It is critical to relate observed cumulative loss trends, such as links between changing humidity and mass loss rates, to the growth kinetics of individual pits. Towards this goal, we leverage in-situ X-ray computed tomography to measure the growth rates of over sixty pits in aluminum in four different humid, chloride environments over ≈3 days of exposure. Pit growth rates and final volumes increased with increasing droplet volume, which was observed to increase with increasing humidity and salt loading. Two factors, droplet spreading and oxide jacking, dramatically increased pit growth rates and final volumes.

Noell, Philip J. (ORCID:0000000346998999)↗
DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗
DOE OSTI2023

Testing Paired Neural Network Models for Aftershock Identification

Aftershock sequences are a burden to real-time seismic monitoring. Cross-correlation can be used because aftershocks exhibit similar waveforms, but the method is computationally expensive. Deep learning may be an alternative, as it is computationally efficient, but great attention to training and testing is required in order to trust that the model can generalize to new aftershock sequences. This is problematic for aftershock sequences, because large-magnitude earthquakes are unpredictable and are globally widespread. Here, we test several paired neural network (PNN) models trained on a augmented (noise-added) earthquake dataset, to determine whether they can be generalized to process real aftershock sequences. Two aftershock datasets that were originally detected by cross-correlation and subsequently validated by an expert analyst were used. We found that current PNN models struggle to generalize to aftershock sequences. However, we identify approaches to improve training future PNN models and believe that improvements may be achieved by transfer learning.

58 GEOSCIENCES↗