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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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An Interagency Greenhouse Gas Information and Analysis System
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2021 NASA System Analysis Symposium-Introduction
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The Man-machine Integration Design and Analysis System (MIDAS) Software Training Documentation
This document is a training manual for MIDAS v5 that takes an user through the hardware and software requirements for the MIDAS V5 software, the steps required to download the software, and the steps that a user needs to take to create a MIDAS simulation. The training guide also illustrates how the models interact to generate MIDAS predictions of operator performance along task, workload, and situation awareness timelines and provides the test routines that were conducted to verify the operation of the integrated MIDAS models. The training guide shows the user how the MIDAS task model interacts with and controls an anthropometric model through its use of behavioral primitives. The training documentation also illustrates one approach that has been used to filter and analyze MIDAS output.
ASCEND: The Space Superhighway: Systems Analysis of an In-Space Delivery Network
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System Analysis and Generative Design for IceNode, a Buoyant Vehicle for Measuring Melt Rate under Ice Shelves
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Measurement System Analysis of a Novel Phase-Based Ultrasonic NDE Technique for Bond Strength Measurement
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Navigation Sensor Technology Assessment Capability for Data-Driven Systems Analysis
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Using Multi-Agency Collaboration and a Comprehensive Data Management Approach to Achieve an Impactful Greenhouse Data Exploration and Analysis System
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Surface Mass Balance and Melt Over the Greenland Ice Sheet in NASA GMAO Analysis Systems
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Insight and Evidence Motivating the Simplification of Dual-Analysis Hybrid Systems into Single-Analysis Hybrid Systems
Many hybrid data assimilation systems currently used for NWP employ some form of dual-analysis system approach. Typically a hybrid variational analysis is responsible for creating initial conditions for high-resolution forecasts, and an ensemble analysis system is responsible for creating sample perturbations used to form the flow-dependent part of the background error covariance required in the hybrid analysis component. In many of these, the two analysis components employ different methodologies, e.g., variational and ensemble Kalman filter. In such cases, it is not uncommon to have observations treated rather differently between the two analyses components; recentering of the ensemble analysis around the hybrid analysis is used to compensated for such differences. Furthermore, in many cases, the hybrid variational high-resolution system implements some type of four-dimensional approach, whereas the underlying ensemble system relies on a three-dimensional approach, which again introduces discrepancies in the overall system. Connected to these is the expectation that one can reliably estimate observation impact on forecasts issued from hybrid analyses by using an ensemble approach based on the underlying ensemble strategy of dual-analysis systems. Just the realization that the ensemble analysis makes substantially different use of observations as compared to their hybrid counterpart should serve as enough evidence of the implausibility of such expectation. This presentation assembles numerous anecdotal evidence to illustrate the fact that hybrid dual-analysis systems must, at the very minimum, strive for consistent use of the observations in both analysis sub-components. Simpler than that, this work suggests that hybrid systems can reliably be constructed without the need to employ a dual-analysis approach. In practice, the idea of relying on a single analysis system is appealing from a cost-maintenance perspective. More generally, single-analysis systems avoid contradictions such as having to choose one sub-component to generate performance diagnostics to another, possibly not fully consistent, component.
Analysis of systems hardware flown on LDEF: New findings and comparison to other retrieved spacecraft hardware
The Long Duration Exposure Facility (LDEF) was retrieved in 1990 after spending 69 months in low-earth-orbit (LEO). A wide variety of mechanical, electrical, thermal, and optical systems, subsystems, and components were flown on LDEF. The Systems Special Investigation Group (Systems SIG) was formed by NASA to investigate the effects of the 69 month exposure on systems related hardware and to coordinate and collate all systems analysis of LDEF hardware. This report is the Systems SIG final report which updates earlier findings and compares LDEF systems findings to results from other retrieved spacecraft hardware such as Hubble Space Telescope. Also included are sections titled (1) Effects of Long Duration Space Exposure on Optical Scatter, (2) Contamination Survey of LDEF, and (3) Degradation of Optical Materials in Space.
Analysis of systems hardware flown on LDEF. Results of the systems special investigation group
The Long Duration Exposure Facility (LDEF) was retrieved after spending 69 months in low Earth orbit (LEO). LDEF carried a remarkable variety of mechanical, electrical, thermal, and optical systems, subsystems, and components. The Systems Special Investigation Group (Systems SIG) was formed to investigate the effects of the long duration exposure to LEO on systems related hardware and to coordinate and collate all systems analysis of LDEF hardware. Discussed here is the status of the LDEF Systems SIG investigation through the end of 1991.
An interactive system for analysis of global cloud imagery
Synoptic images of the global cloud pattern composited from six contemporaneous satellites provide an unprecedented view of the global cloud field. Having horizontal resolution of about 0.5 deg and temporal resolution of 3 h, the global cloud imagery (GCI) resolves most of the variability of organized convection, including several harmonics of the diurnal cycle. Although the GCI has these attractive features, the dense and 3D nature of that data make it a formidable volume of information to treat in a practical and efficient manner. An interactive image-analysis system (IAS) has been developed to investigate the space-time variability of global cloud behavior. In the IAS, data, hardware, and software are integrated into a single system providing a variety of space-time covariance analyses in a menu-driven format. Owing to its customized architecture and certain homogeneous properties of the GCI, the IAS calculates such quantities effectively. Many covariance statistics are derived from 3D data with interactive speed, allowing the user to interrogate the archive iteratively in a single session. The 3D nature of those analyses and the speed with which they are performed distinguish the IAS from conventional image processing of 2D data.