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Results for “componenet performance”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Enhanced Component Performance Study: Motor-Driven Pumps 1998–2012

This report presents an enhanced performance evaluation of motor-driven valves (MDPs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from fiscal year 1998 through 2012 for the component reliability as reported in the Equipment Performance and Information Exchange (EPIX). The MDP failure modes considered for standby systems are failure to start, failure to run less than or equal to 1 hour, and failure to run more than 1 hour; for normally running systems, the failure modes considered are failure to start and failure to run. An 8 hr unreliability estimate is also calculated and trended. The component reliability estimates and the reliability data are trended for the most recent 10-year period while yearly estimates for reliability are provided for the entire active period. One statistically significant increasing trend was identified in the MDP results for standby MDP run hours per reactor critical year. Statistically significant decreasing trends were identified for unreliability (for standby and normally running systems) frequency of failure-to-start events for standby systems and failure probability for standby systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Three-Dimensional Sensor Forward Modeling of Clouds and Precipitation in the Multi-Instrument Inverse Solver Testbed (MIIST)

Sensor forward models are an essential tool for interpreting remote sensing observations and performing quantitative estimates of geophysical parameters. Our three-dimensional forward modeling and retrieval framework allows us to perform detailed analyses of NASA field campaign datasets for a deeper understanding of the remote sensing of clouds and precipitation. This presentation details the componenets of this radiative transfer model used to simulate active (radar) and passive (microwave radiometer) observations, and we give some relevant examples based on both model precipitation systems and actual observations.

Adams, Ian S.↗
DOE OSTI2023

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning

Abstract Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid–solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data‐driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.

25 ENERGY STORAGE↗