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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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High Gain DC-DC Converter Collaborative: University of Houston Design.
Abstract not provided.
High Gain DC-DC Converter Collaborative: The Ohio State University Design.
Abstract not provided.
Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators
Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
Optically-coupled Switched-mode Converter forSmartphons Wireless Charging Application
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Hydrothermal Catalysis Flow Process to Convert Brown Grease into Green Gasoline, Green Jet, and Green Diesel Fuels
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Deep Symbolic Optimization for Electric Component Sizingin Fixed Topology Power Converters
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A High-Frequency Ćuk Converter for Fusion Science Applications Final Technical Report
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Methods and systems for electrospinning using low power voltage converter
An electrospinning system, method, and apparatus comprises a dual polarity high voltage power supply with much less power out for safe operation, a solution dispensing assembly held at high positive potential by the dual polarity power supply, a Corona discharge assembly held at high negative potential by the dual polarity power supply, and a drum collector held at ground potential wherein a solution is drawn from the solution dispensing assembly to the drum collector thereby forming a fiber mat.
An Intelligent System for Automatic Selection of DC-DC Converter Topology with Optimal Design
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Practical approaches to modeling and control design for a wave energy converter.
Abstract not provided.
A Multi-Channel Radiation-Tolerant, Low-Power, High-Speed and Resolution Analog-to-Digital Converter for Nuclear Physics Detectors
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Non-circular optical fiber and mode shape converter and method
A class of fibers is described that have a non-circular cross section on one or both ends that can by optimized to capture the optical radiation from a laser diode or diode array and deliver the light in the same or different shape on the opposite end of the fiber. A large multimode rectangular waveguide may be provided which can accept the radiation from a high-power diode bar and transform it into a circular cross section on the opposite end, while preserving brightness.
Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators
Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
Novel Bremsstrahlung Converter Designs for Ultra High Dose Rate Radiotherapy Systems
Ultra-high dose rates (UHDR) have been shown to improve the therapeutic index in the treatment of cancer using external beam radiotherapy in many preclinical settings by inducing the so-called “FLASH” effect [1]; however, the underlying biological and physical mechanism(s) are still unknown. Recent work has been to expand the energy range of photons used in UHDR studies from keV photons generated in synchrotron to photons less than 8 MeV produced by accelerated electrons undergoing the bremsstrahlung interaction [2]. This work is a part of the FLASH-Experimental X-ray small Animal Conformal Therapy (FLASH-EXACT) project is aimed at producing a preclinical system that induces the FLASH effect with photons >10 MeV.
Method of preparing electrocatalysts for converting carbon dioxide to chemicals
Electrocatalysts composed of single atoms or metal clusters dispersed over porous carbon support were prepared by a lithium-melt method. The new catalysts demonstrated high selectivity, high Faradic efficiency and low overpotential toward to the electrocatalytic reduction of carbon dioxide to chemicals.
Wave Energy Converter Optimization with Multi-Resonance Controller of the Electrical Power Take-off.
Abstract not provided.