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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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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Flange Tritium Waste Containers Operational Concerns: How to Address the Issues Through the Venting Design

As a part of the U.S. Department of Energy (DOE), Los Alamos National Laboratory (LANL or Laboratory) must comply with regulations in the Clean Air Act (CAA) put forth in 40 Code of Federal Regulations (CFR) 61, Subpart H “National Emission Standards for Emissions of Radionuclides Other Than Radon from Department of Energy Facilities.” The standard states, “Emissions of radionuclides to the ambient air from Department of Energy facilities shall not exceed those amounts that would cause any member of the public to receive in any year an effective dose equivalent of 10 mrem/yr [millirem per year] [1].” This regulation is referred to as the National Emission Standards for Hazardous Air Pollutants for Radionuclides (Radionuclide NESHAP or Rad-NESHAP).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Mineral Alteration of Shales by CO 2 and Brine Containing Surfactants

The overall goal of the proposed research was to determine the effect of surfactants on shale mineral reactivity with CO 2 and brine. The specific objectives of the proposed work were to 1) Determine the effect of shale mineralogy and surfactant properties on surfactant adsorption and wettability alteration, 2) Determine the effects of surfactant adsorption and wettability alteration on mineral reactivity with CO 2 and brine for varying hydrodynamic conditions, and 3) Develop a modified mineral reaction model with CO 2 and brine that considers the effects of surfactant adsorption. Four tasks were performed to address the project objectives. These were 1) Select and characterize shale samples, 2) Evaluate the effects of surfactant and reservoir properties on adsorption and mineral reactivity, 3) Determine the effects of surfactant adsorption, wettability alteration, and hydrodynamics on mineral reaction kinetics, and 4) Quantify how surfactant sorption and mineral wettability alteration affect mineral reaction modeling. This final report summarizes results in each of these tasks, as well as associated publications.

58 GEOSCIENCES↗
DOE OSTI2023

Avoid Procuring or Storing 2-Propanol in Opaque White, Clear Containers

One of the most important responsibilities chemical owners at Los Alamos National Laboratory (LANL) have is to ensure the safety of the chemicals they own. An important consideration is how chemicals react to their environment. In the case of 2-propanol, a common solvent found in research laboratories used often in manufacturing and analytical processes, it can develop peroxides with exposure to light. 2-Propanol is classified as a Group B (class 3) peroxide-forming solvent. However, there is a dearth of literature that summarizes the risks surrounding peroxide formation in 2-propanol (IPA), leading to a lack of awareness of these hazards in the chemistry community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Image Classification Using Convolutional Neural Networks to Automate Visual Inspection of CCO Containers (Rev. 1)

The Savannah River Site is automating the receipt and inspection of Criticality Control Overpack drums to aid in nuclear waste disposal. The Advanced Engineering group is automating this receipt and inspection process to assist the site’s goals. Part of this process includes visually inspecting CCO drums to ensure that no defects or security risks are present. This research focuses on developing a machine learning model to automatically classify drums as passing or failing inspection. The machine learning model was implemented using the open-source Tensorflow library and uses a Convolutional Neural Network architecture to extract features and differentiate between images. The model was able to achieve an average of 85% accuracy on a dataset of 288 drums. This project’s goal was to prove the validity of computer vision in an automation process and provide a starting place for continued research.

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