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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Methods and devices for sterilizing medical equipment

A hydrogen peroxide sterilization device for sterilizing a medical device, the hydrogen peroxide sterilization device including a hermetic outer container, a hydrogen peroxide pouch, and a hydrogen peroxide solution in the solution chamber. The hermetic outer container includes a sterilization enclosure configured to receive the medical device. The hydrogen peroxide pouch includes a permeable membrane enclosing a solution chamber and is configured to be positioned in the sterilization enclosure of the outer container. The permeable membrane is configured to allow hydrogen peroxide vapor to diffuse from the solution chamber through the permeable membrane into the sterilization enclosure.

Bohnert, George William↗
DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management↗
DOE OSTI2024

Equipping Neural Network Surrogates with Uncertainty for Propagation in Physical Systems

Coarse-grained or filtered models typically rely on closure models to account for unresolved scales. For instance, large eddy simulation for modeling turbulent fluid flows explicitly resolves the largest scales, but requires modeling closure terms to account for the sub-filter scales. With the vast amount of data available from high-fidelity simulations, there are unique opportunities to leverage data-driven modeling techniques to formulate expressive and flexible closure models. Despite their flexibility, data-driven models struggle in domain shift settings, i.e. when deployed in configurations not captured in the training dataset. In particular, the efficacy of neural network surrogates is difficult to assess a priori due to the deterministic, point-estimate nature of predictions. In high-consequence applications, such models require reliable uncertainty estimates in the data-informed and out-of-distribution regimes. To quantify uncertainties in both regimes, we employ Bayesian neural networks which are able to capture both epistemic and aleatoric uncertainties. We will discuss challenges associated with the training and evaluation of these networks. Furthermore, we will discuss uncertainty embedding strategies to enable efficient sampling and propagation of uncertainty through high-fidelity simulations.

Bayesian neural networks↗
DOE OSTI2024

Electric Vehicle Supply Equipment Cybersecurity Through Emulation

As the grid evolves, it is paramount to understand the risks that cyberattacks pose before assets are deployed. Leveraging the ARIES Cyber Range, NREL has created a platform to conduct analysis of EV charging protocol cybersecurity to understand the risks and impacts that cyberattacks may pose to critical infrastructure.

bug bounty prize↗
DOE OSTI2024

Electric Vehicle Supply Equipment Security Solutions

The EVs@Scale cybersecurity pillar deep dive focuses on the three main tasks that NREL has in the consortium: (1) Analysis of EV charging mobile applications, (2) Cybersecurity risk assessments of EVSE through DER-CF, and (3) PKI for EVSE.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2025

Custom Equipment Development for Processing of Surplus Plutonium

The Strategic Laboratory Assessment (SLA), a collaborative team of SRNL and ORNL personnel, has been established to advance the objectives of the Surplus Plutonium Disposition (SPD) Project, by identifying and developing technologies to accelerate disposition, reduce life cycle costs, minimize worker radiation exposure, improve worker safety, and minimize Surplus Plutonium Disposition Program risks. [1] The SLA team has identified can cutting and plutonium (Pu) oxide size reduction as two glovebox processes where technology enhancements would be valuable. The DOESTD-3013 package currently in use for Pu downblending requires cutting two nested cans before the inner convenience can that holds the Pu oxide may be accessed for further processing. A rotary tubing-style cutter is used for opening the 3013 packages within the glovebox. Collet changeouts are required between cutting of the outer and inner cans. The SLA team is currently developing and testing an adjustable-clamp can cutter design that eliminates collet changeouts and allows cutting of the outer and inner can at the same time, resulting in significant reduction of radiological dose and process time, as well as improved ergonomics. To meet the Pu oxide particle size requirement, size reduction of Pu oxide agglomerations must be performed within the process gloveboxes. The SLA team has identified jaw crushing technology as an alternative to the currently employed rotary mill. Jaw crusher advantages include reduced dust within the glovebox, increased batch sizes, and easier integration with other glovebox processes due to the flow-through nature of jaw crushing. Commercially manufactured jaw crushers are either too large and/or too heavy for implementation in the SPD gloveboxes, so the SLA team is developing and testing a custom jaw crusher to meet the needs of the SPD Project.

Krementz, Daniel [Savannah River National Laborato↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗