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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Integrated optical signal processing with magnetostatic waves

Magneto-optical devices based on Bragg diffraction of light by magnetostatic waves (MSW's) offer the potential of large time-bandwidth optical signal processing at microwave frequencies of 1 to 20 GHz and higher. A thin-film integrated-optical configuration, with the interacting MSW and guided-optical wave both propagating in a common ferrite layer, is necessary to avoid shape-factor demagnetization effects. The underlying theory of the MSW-optical interaction is outlined, including the development of expressions for optical diffraction efficiency as a function of MSW power and other relevant parameters. Bradd diffraction of guided-optical waves by transversely-propagating magnetostatic waves and collinear TE/TM mode conversion included by MSW's have been demonstrated in yttrium iron garnet (YIG) thin films. Diffraction levels as large as 4% (7 mm interaction length) and a modulation dynamic range of approx 30 dB have been observed. Advantages of these MSW-based devices over the analogous acousto-optical devices include: much greater operating frequencies, tunability of the MSW dispersion relation by varying either the RF frequency or the applied bias magnetic field, simple broad-band MSW transducer structures (e.g., a single stripline), and the potential for very high diffraction efficiencies.

Fisher, A. D.↗
DOE OSTI2025

Science of Scale-Up: Accelerating chemical manufacturing technology development workshop report

The Science of Scale-Up: Accelerating chemical manufacturing technology development workshop report outlines key insights and actionable recommendations for accelerating the scale-up of disruptive chemical manufacturing technologies. Convened in October 2024, the workshop brought together approximately fifty experts from academia, industry, national laboratories, and government agencies to address the barriers and solutions for maturing technologies from proof-of-concept to commercialization. The report identifies seven critical themes for enabling faster scale-up. These themes were explored through general discussions and breakout sessions focused on three specific chemical manufacturing technologies—electrochemical, thermochemical, and biological conversion processes. The findings emphasize the importance of interdisciplinary collaboration, robust funding mechanisms, and shared resources to overcome technical barriers and accelerate technology deployment. The report also highlights technology-specific challenges and opportunities, including the need for advanced materials, scalable manufacturing processes, and integrated testing environments. For electrochemical manufacturing processes, durability and material optimization are key priorities, while thermochemical processes require novel reactor designs and better supply chain integration. Biological conversion processes face hurdles in strain engineering, reactor design, and process integration. Across all technologies, the workshop emphasized the importance of leveraging computational tools, standardized protocols, and collaborative networks to address knowledge gaps and technical barriers. By acting on these insights, stakeholders can reduce the timeline for scaling up critical chemical manufacturing technologies, ensuring their timely impact on manufacturing competitiveness, and environmental sustainability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Integrated Salt Waste Processing at the Savannah River Site - 17413

Treatment and disposition of salt waste is the critical path to completion of the Savannah River Site (SRS) Liquid Waste (LW) Disposition and Cleanup Program. The new Salt Waste Processing Facility (SWPF) is a critical link in the pre-treatment chain and will process high-curie salt waste removed from various SRS liquid waste storage tanks. Significant infrastructure modifications are underway throughout the existing LW facilities to integrate the SWPF into the LW system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Integrated salt waste processing at the Savannah River Site - 15301

Treatment and disposition of salt waste is the critical path to completion of the Savannah River Site Liquid Waste (LW) Disposition and Cleanup Program. The new Salt Waste Processing Facility (SWPF) is a critical link in the pre-treatment chain and will process high curie salt waste removed from various SRS liquid waste storage tanks. Significant infrastructure modifications are needed in the existing LW facilities in order to integrate the SWPF into the LW system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

An Integrated Risk Assessment Process of Safety-Related Digital I&C Systems in Nuclear Power Plants

Upgrading the existing analog instrumentation and control (I&C) systems to state-of-the-art digital I&C (DI&C) systems will greatly benefit existing light water reactors. However, the issue of software common cause failure (CCF) remains an obstacle in terms of qualification for digital technologies. Existing analyses of CCFs in I&C systems mainly focus on hardware failures. With the application and upgrading of new DI&C systems, design flaws could cause software CCFs to become a potential threat to plant safety, considering that most redundancy designs use similar digital platforms or software in their operating and application systems. With complex multilayer redundancy designs to meet the single failure criterion, these I&C safety systems are of particular concern in U.S. Nuclear Regulatory Commission licensing procedures. In Fiscal Year 2019, the Risk-Informed Systems Analysis (RISA) Pathway of the U.S. Department of Energy’s Light Water Reactor Sustainability Program initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades and designs. An integrated risk assessment for the DI&C process was proposed for this strategy to identify potential key digital-induced failures, implement reliability analyses of related digital safety I&C systems, and evaluate the unanalyzed sequences introduced by these failures (particularly software CCFs) at the plant level. Here this paper summarizes these RISA efforts in the risk analysis of safety-related DI&C systems at Idaho National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Establishment of a Vertically Integrated Domestic Manufacturing Process for Production of Substrates Needed for Manufacture of Gas Diffusion Layers

In this project, AvCarb, LLC evaluated the baseline performance metrics of commercial carbon veils and their corresponding Gas Diffusion Layers (GDLs) with the goal of establishing an optimized, vertically integrated production system for wet-laid nonwoven substrates used in gas diffusion media for electrochemical energy storage and conversion devices. Mechanical testing and microstructural characterization were conducted and used to develop a multiscale computational model capable of simulating and predicting the performance of GDLs in fuel cells. Although the project successfully generated foundational transport and modeling data, it was terminated prior to identifying the critical GDL design parameters necessary for full optimization. The program aimed to improve carbon veil fabrication through enhanced fiber dispersion, fiber-fiber adhesion control, and improved web formation, enabling the production of high-quality, uniform substrates. Simulations were intended to guide mixing and solution delivery system design and process conditions, followed by production-scale trials to evaluate fiber dispersion, web uniformity, and mechanical robustness. At full deployment, the proposed production line would have been capable of producing approximately 650,000 m² of carbon veil annually. This capability remains strategically important, as the United States currently lacks a domestic source of wet-laid nonwoven carbon substrates that satisfy the stringent quality requirements for fuel-cell GDLs and electrolyzers representing an ongoing supply-chain vulnerability. Beyond supply-chain benefits, the project established a robust benchmarking dataset for existing commercial carbon veils while advancing next-generation material concepts targeting improved performance and manufacturing consistency.

Olson, Cynthia Lemay↗
DOE OSTI2021

Implementing the LSST Software Stack for DESGW processing and the Integration of Convolutional Neural Networks into the DESGW Pipeline

The Dark Energy Survey Gravitational Wave (DESGW) group strives to understand thelargescale structure of the universe and galaxies by looking for electromagnetic signatures intelescope images following gravitational wave detections. The DESGW group uses a processing pipeline to perform difference imaging to look for potential candidates. In order to perform these searches more effectively, we first explored using an alternative processing pipeline, the LSST Software Stack to analyze the telescope images. Because the LSST software stack is currently transitioning between its generation 2 and generation 3 system, we decided that while the software will be usable in the future once generation 3 is complete, at the moment its incompleteness makes it impractical to use. We then decided to work on improving the current pipeline by developing a module that integrates a Convolutional Neural Network (CNN) to test difference imaging products for bad subtractions due to misalignment.

Navarro, Alexander↗
DOE OSTI2014

PRELIMINARY ENVIRONMENTAL, HEALTH AND SAFETY RISK ASSESSMENT ON THE INTEGRATION OF A PROCESS UTILIZING LOW-ENERGY SOLVENTS FOR CARBON DIOXIDE CAPTURE ENABLED BY A COMBINATION OF ENZYMES AND VACUUM REGENERATION WITH A SUBCRITICAL PC POWER PLANT

The results of the preliminary environmental, health and safety (EH&S) risk assessment for an enzyme-activated potassium carbonate (K2CO3) solution post-combustion CO2 capture (PCC) plant, integrated with a subcritical pulverized coal (PC) power plant, are presented. The expected emissions during normal steady-state operation have been estimated utilizing models of the PCC plant developed in AspenTech’s AspenPlus® software, bench scale test results from the University of Kentucky, and industrial experience of emission results from a slipstream PCC plant utilizing amine based solvents. A review of all potential emission species and their sources was undertaken that identified two credible emission sources, the absorber off-gas that is vented to atmosphere via a stack and the waste removed from the PCC plant in the centrifuge used to reclaim enzyme and solvent. The conditions and compositions of the emissions were calculated and the potential EH&S effects were considered as well as legislative compliance requirements. Potential mitigation methods for emissions during normal operation have been proposed and solutions to mitigate uncontrolled releases of species have been considered. The potential emissions were found to pose no significant EH&S concerns and were compliant with the Federal legislation reviewed. The limitations in predicting full scale plant performance from bench scale tests have been noted and further work on a larger scale test unit is recommended to reduce the level of uncertainty.

Fitzgerald, David↗
DOE OSTI2017

Manufacturing Process for OLED Integrated Substrate

The main objective of this project was to develop a low-cost integrated substrate for rigid OLED solid-state lighting produced at a manufacturing scale. The integrated substrates could include combinations of soda lime glass substrate, light extraction layer, and an anode layer (i.e., Transparent Conductive Oxide, TCO). Over the 3 + year course of the project, the scope of work was revised to focus on the development of a glass substrates with an internal light extraction (IEL) layer. A manufacturing-scale float glass on-line particle embedding process capable of producing an IEL glass substrate having a thickness of less than 1.7mm and an area larger than 500mm x 400mm was demonstrated. Substrates measuring 470mm x 370mm were used in the OLED manufacturing process for fabricating OLED lighting panels in single pixel devices as large as 120.5mm x 120.5mm. The measured light extraction efficiency (calculated as external quantum efficiency, EQE) for on-line produced IEL samples (>50%) met the project’s initial goal.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

62 RADIOLOGY AND NUCLEAR MEDICINE↗