Engineering topics
Aitkaliyeva, Assel (ORCID:0000000314816804)
Publications and source records attributed to Aitkaliyeva, Assel (ORCID:0000000314816804).
Deep learning-enabled probing of irradiation-induced defects in time-series micrographs
Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.
Compositional Effects of Additively Manufactured Refractory High-Entropy Alloys under High-Energy Helium Irradiation
High-Entropy Alloys (HEAs) are proposed as materials for a variety of extreme environments, including both fission and fusion radiation applications. To withstand these harsh environments, materials processing must be tailored to their given application, now achieved through additive manufacturing processes. However, radiation application opportunities remain limited due to an incomplete understanding of the effects of irradiation on HEA performance. In this letter, we investigate the response of additively manufactured refractory high-entropy alloys (RHEAs) to helium (He) ion bombardment. Through analytical microscopy studies, we show the interplay between the alloy composition and the He bubble size and density to demonstrate how increasing the compositional complexity can limit the He bubble effects, but care must be taken in selecting the appropriate constituent elements.
Microstructural dependence on fuel matrix composition in irradiated U-Mo dispersion fuels
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