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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Online Monitoring of Radiochemical Processing Streams for the Plutonium-238 Supply Program

Online monitoring with spectrophotometry is being developed to improve the timeliness of analytical measurements for the Plutonium-238 Supply Program at Oak Ridge National Laboratory. A commercially available online monitoring software was used to calculate and view process data in real time to help identify process deviations and optimize system performance. Monitoring detailed process data will improve processing efficiency and help technicians make decisions during hot-cell operations.

Sadergaski, Luke↗
DOE OSTI2021

Buffer-free process cycle for CO 2 sequestration and carbonate production from brine waste streams with high salinity

A method includes: (1) using a chelating agent, extracting divalent ions from a brine solution as complexes of the chelating agent and the divalent ions; (2) using a weak acid, regenerating the chelating agent and producing a divalent ion salt solution; and (3) introducing carbon dioxide to the divalent ion salt solution to induce precipitation of the divalent ions as a carbonate salt. Another method includes: (1) combining water with carbon dioxide to produce a carbon dioxide solution; (2) introducing an ion exchanger to the carbon dioxide solution to induce exchange of alkali metal cations included in the ion exchanger with protons included in the carbon dioxide solution and to produce a bicarbonate salt solution of the alkali metal cations; and (3) introducing a brine solution to the bicarbonate salt solution to induce precipitation of divalent ions from the brine solution as a carbonate salt.

Sant, Gaurav↗
DOE OSTI2021

Architecture Of A Multi-channel Data Streaming Device With An Fpga As A Coprocessor

The design of a data acquisition system often involves the integration of a Field Programmable Gate Array (FPGA) with analog front-end components to achieve precise timing and control. Reuse of these hardware systems can be difficult since they need to be tightly coupled to the communications interface and timing requirements of the specific ADC used. A hybrid design exploring the use of FPGA as a coprocessor to a traditional CPU in a dataflow architecture is presented. Reduction in the volume of data and gradual transitioning of data processing away from a hard real-time environment are both discussed. Chief design concerns, including data throughput and precise synchronization with external stimuli, are addressed. The discussion is illustrated by the implementation of a multi-channel digital integrator, a device based entirely on commercial off-the-shelf (COTS) equipment.

Nogiec, Jerzy M.↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗