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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

Mass Optimization of a Multilayered Shield for Transportable Microreactors

The ability to easily transport microreactors is a major selling point for deploying microreactors to remote areas. However, this creates a unique shielding challenge, especially when the microreactor is being shipped after irradiation. A traditional reactor configuration utilizes a separate biological shield and pressure vessel to meet radiological shielding and pressure needs. The limited space available for transportable microreactors for both shielding and pressure vessels requires a revised assessment of separating out the biological shield and pressure vessel. To address these concerns, we examine a nuclear-grade sandwich composite (NGSC) that combines the reactor pressure vessel and biological shielding functions into a single component. Through a series of optimization problems for both transportation and operational use cases, the NGSC is able to minimize dose, minimize the vessel cost, and ensure that weight requirements are met for transportation. Initial results show that using a tungsten-tetraboride cermet in the first two layers of a six-layer NGSC provides adequate shielding for both use cases. These results show promise that an NGSC has enough overlap between operational and transportation cases to help reduce the design space for future analysis and assessment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
DOE OSTI2026

Process-driven roadmap for depositing super duplex stainless steel via wire Arc additive manufacturing

Here, this study systematically investigates the effects of shielding gas, bead spacing, weld mode, and travel speed on the phase balance, porosity, and hardness of wire arc additively manufactured (WAAM) super duplex stainless steel (ER2594). Robotic WAAM was employed to fabricate multilayer walls under systematically varied process conditions, followed by phase transformation simulations, X-ray computed tomography (XCT), electron backscatter diffraction (EBSD), and microhardness evaluation. Thermodynamic simulations predicted rapid cooling of AM process can suppress the potential formation of deleterious precipitates which was later validated via cross-sectional microstructure analyses of printed samples. XCT revealed porosity levels below 0.2% for all deposits, with reduced travel speed significantly lowering defect volume. Microstructural analyses revealed the evolution of various austenite precipitates, including grain boundary austenite (GBA), Widmanstätten austenite (WA), and intergranular austenite (IGA), sequentially upon cooling of the ferrite phase. Among all process parameters, weld transfer mode exhibited the strongest influence on phase balance; pulsed mode promoted higher ferrite retention (~ 36%) compared to RapidX mode. No consistent relationship between stabilized phase fraction and captured microhardness was observed. This work provides critical insights for optimizing WAAM parameters to control phase balance and mechanical performance, which is essential for producing high-integrity super duplex stainless-steel components for nuclear and marine applications.

Grain orientation↗