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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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201 records · Page 12
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DOE OSTI2025

SaltStone Wastewater Cement Study Using Isothermal Calorimetry, Standard Concrete Characterization Techniques, and CemGEMS - 25169

Cementitious reagents are used to solidify/stabilize aqueous radioactive, hazardous, and mixed salt solutions, and sludges to meet low-level radioactive waste (LLW) and Resource Conservation and Recovery Act (RCRA) requirements for disposal at Department of Energy (DOE). It is flexible enough to solidify radioactive wastewater saturated in complex species that include but are not limited to radioactive isotopes from the bombardment of neutrons in reactor operation, corrosion products from metallic components, and a variety of soluble organic compounds. [1,2]. Waste form testing typically includes processing or fresh properties, cured properties, compressive strength and hydraulic properties, porosity, density, saturated and unsaturated moisture transport, and leachability of contaminants in the waste form pore solution. Properties are collected over a relatively limited time, typically 28 to 365 days [3]. In addition, changes in the waste form as the result of time and changing conditions are important for concrete engineers to predict overall performance of the forms and potential release of contaminants in the disposal process via unintended filtration into the environment [4]. These predictions are determined/calculated characterizing young waste forms (relative to the standard age of concrete) and are based on transport through soluble ions in pore solutions. Characterization methods include X-ray, SEM, and isothermal calorimetry among other methods used to define the composition, amorphous vs. crystalline nature of the components, and the energetic formation mechanisms for multi-phase mineral systems. [5–7] Isothermal calorimetry is a well standardized technique for cements and concretes and can be used to predict the timing and nature of the hydration reactions.[8] The technique can measure long term energetic

Bustamante, Michael E. [Savannah River National La↗
DOE OSTI2022

Understanding Fission Gas Bubble Distribution and Zirconium Redistribution in Neutron-irradiated U-Zr Metallic Fuel Using Machine Learning

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Currently, Idaho National Laboratory (INL) has been the leading national laboratory for research, development, and demonstration (RD&D) on metallic fuel. Advanced post-irradiation characterization will help to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. Characterization capabilities ranging from sub-nanometer to micrometer, such as scanning electron microscopy (SEM), focused ion beam (FIB) sampling, transmission electron microscopy (TEM) characterization, and local thermal conductivity microscopy (TCM), have been utilized recently on irradiated U-10Zr fuel samples to gain a better understanding of nuclear fuel microstructure and property evolution inside a reactor. The FIB/SEM coupled with energy dispersive X-ray spectroscopy (EDS) can capture the essential information to achieve better understanding of fuel behaviors. Inside a nuclear reactor, the phase and microstructure of U-10Zr is constantly changing under neutron bombardment. For example, the gaseous fission product atoms have a limited solubility inside fuel matrix and tend to precipitate out in bubble form, which not only contribute to fuel thermal conductivity degradation but also provide a shortcut for movement of fission products, i.e. lanthanides. The resultant deposition of lanthanides at the cladding inner surface will potentially trigger a chemical reaction/interaction between nuclear fuel and cladding at reactor operational conditions, threatening fuel integrity and safety. FIB/SEM coupled with EDS can provide the fission bubble information as well as probe into phase separation or Zr redistribution, which is fundamental to predict the fuel performance. With high velocity image data generating method, such as FIB/SEM, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to generate a bubble classifier and to categorize bubbles into three categories: isolated bubble, connected without lanthanides, and connected with lanthanides bubbles[3]. This work presents a showcase of this approach on six regions of a fuel cross-section along the radial temperature gradient. We obtained distributions of bubble categories and porosity rates along the six regions. Moreover, a secondary phase U-Zr2 was determined and found on regions 5 and 6. The secondary phase fraction was increasing from 15.61% in region 5 to 34.79% in region 6 based on this approach . This quantitative data offers insights into the lanthanide migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗