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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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NASA NTRS2022

A New Organization Metric for Synoptic Scale Tropical Convective Aggregation

Organization metrics were originally developed to measure how densely convective clouds are arranged at mesoscales. In this work, we apply organization metrics to describe tropical synoptic scale convective activity. Such activity is identified by cloud-precipitation (hybrid) regimes defined at 1-degree and 1-hourly resolution. Existing metrics were found to perform inadequately for such convective regime aggregates because the large domain size and co-existence of sparse aggregate occurrences with noisy isolated convection often violate assumptions inherent in these metrics. In order to capture these characteristics, in this study the existing “convective organization potential” (COP) metric was modified so as to focus on local organization and provide increased weight to aggregate size. The resulting “area-based COP” (ABCOP) follows the principle that the more numerous the objects, the higher the chance of organization. It is thus optimized to capture large-scale convective events occurring during phenomena such as ENSO and MJO, while also performs as well as existing metrics for small domain sizes.

Synoptic scales↗
NASA NTRS1978

NASA metrication activities

NASA's organization and policy for metrification, history from 1964, NASA participation in Federal agency activities, interaction with nongovernmental metrication organizations, and the proposed metrication assessment study are reviewed.

Vlannes, P. N.↗
NASA NTRS2011

DCS: A Case Study of Identification of Knowledge and Disposition Gaps Using Principles of Continuous Risk Management

The Human Research Program (HRP) is formulated around the program architecture of Evidence-Risk-Gap-Task-Deliverable. Review of accumulated evidence forms the basis for identification of high priority risks to human health and performance in space exploration. Gaps in knowledge or disposition are identified for each risk, and a portfolio of research tasks is developed to fill them. Deliverables from the tasks inform the evidence base with the ultimate goal of defining the level of risk and reducing it to an acceptable level. A comprehensive framework for gap identification, focus, and metrics has been developed based on principles of continuous risk management and clinical care. Research towards knowledge gaps improves understanding of the likelihood, consequence or timeframe of the risk. Disposition gaps include development of standards or requirements for risk acceptance, development of countermeasures or technology to mitigate the risk, and yearly technology assessment related to watching developments related to the risk. Standard concepts from clinical care: prevention, diagnosis, treatment, monitoring, rehabilitation, and surveillance, can be used to focus gaps dealing with risk mitigation. The research plan for the new HRP Risk of Decompression Sickness (DCS) used the framework to identify one disposition gap related to establishment of a DCS standard for acceptable risk, two knowledge gaps related to DCS phenomenon and mission attributes, and three mitigation gaps focused on prediction, prevention, and new technology watch. These gaps were organized in this manner primarily based on target for closure and ease of organizing interim metrics so that gap status could be quantified. Additional considerations for the knowledge gaps were that one was highly design reference mission specific and the other gap was focused on DCS phenomenon.

Norcross, Jason↗
NASA NTRS2023

m:N Working Group Spring Meeting Summary

On May 9th and 11th, 2023, the NASA-led MultiVehicle (m:N) Working Group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Pseudo Satellite (HAPS), and Urban Air Mobility (UAM)] met in Denver, CO at XPONENTIAL 2023, co-hosted by the Association for Uncrewed Vehicle Systems International (AUVSI) and Messe Düsseldorf North America (MDNA), for an in-person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year, however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N working group is co-chaired by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups, including the FAA and RTCA’s SC-228 Detect and Avoid.

multi-vehicle↗
NASA NTRS2022

m:N Working Group

On November 29th and 30th, 2022, the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Ames Research Center in Mountain View, CA for an in person meeting. The option to dial in remotely and use Conference.IO to engage with questions was offered as well. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA and RTCA’s SC-228 Detect and Avoid. Each subgroup is run by a government/industry team.

multi-vehicle↗
NASA NTRS2023

m:N Working Group Annual Status Report

This document serves as an annual report of m:N Unmanned Aircraft Systems (UAS) subgroup activities, addressed challenges, and roadmaps for the future. The subgroups consist of small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM). Also included in this report are participant lists for each subgroup (Appendix B) and future roadmap and outreach plans.The subgroups meet in a virtual format multiple times throughout the year. Twice a year, participants from all the subgroups come together as part of the m:N working group to brief each other in person on progress, challenges, and path forward ideas for successful incorporation of UAS into the airspace. Recent m:N in person meetings include: - November 29-30, 2022 at the NASA Ames Research Center in Mountain View, CA - May 9 & 11, 2023 at AUVSI’s Xponential conference in Denver, CO - The next in person working group meeting is planned for November 28-30, 2023 at NASA Langley in Hampton, Virginia The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the Federal Aviation Administration (FAA) and Radio Technical Commission for Aeronautics (RTCA’s) SC-228 Detect and Avoid committee.

multi-vehicle↗
NASA NTRS2024

m:N Working Group Meeting Summary November 2023

From November 28th to 30th, 2023 the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Langley Research Center in Hampton, VA for an in person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together in person to further identify and discuss challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA, RTCA, and ASTM. Each subgroup is run by a government/industry team (see below). sUAS Subgroup Garrett Sadler (NASA) Scott Scheff (HF Designworks) Large UAS Subgroup Conrad Rory (NASA) Brandon Suarez (Reliable Robotics) HAPS Subgroup Andy Thurling (Thurling Aero Consulting) Jeff Homola (NASA) UAM Subgroup Mike Politowicz (NASA) Scott Scheff (HF Designworks), member-at-large

m:N operations↗
NASA NTRS2006

A Predictive Approach to Eliminating Errors in Software Code

NASA s Metrics Data Program Data Repository is a database that stores problem, product, and metrics data. The primary goal of this data repository is to provide project data to the software community. In doing so, the Metrics Data Program collects artifacts from a large NASA dataset, generates metrics on the artifacts, and then generates reports that are made available to the public at no cost. The data that are made available to general users have been sanitized and authorized for publication through the Metrics Data Program Web site by officials representing the projects from which the data originated. The data repository is operated by NASA s Independent Verification and Validation (IV&V) Facility, which is located in Fairmont, West Virginia, a high-tech hub for emerging innovation in the Mountain State. The IV&V Facility was founded in 1993, under the NASA Office of Safety and Mission Assurance, as a direct result of recommendations made by the National Research Council and the Report of the Presidential Commission on the Space Shuttle Challenger Accident. Today, under the direction of Goddard Space Flight Center, the IV&V Facility continues its mission to provide the highest achievable levels of safety and cost-effectiveness for mission-critical software. By extending its data to public users, the facility has helped improve the safety, reliability, and quality of complex software systems throughout private industry and other government agencies. Integrated Software Metrics, Inc., is one of the organizations that has benefited from studying the metrics data. As a result, the company has evolved into a leading developer of innovative software-error prediction tools that help organizations deliver better software, on time and on budget.

Source record↗
NASA NTRS1993

Specification-based software sizing: An empirical investigation of function metrics

For some time the software industry has espoused the need for improved specification-based software size metrics. This paper reports on a study of nineteen recently developed systems in a variety of application domains. The systems were developed by a single software services corporation using a variety of languages. The study investigated several metric characteristics. It shows that: earlier research into inter-item correlation within the overall function count is partially supported; a priori function counts, in themself, do not explain the majority of the effort variation in software development in the organization studied; documentation quality is critical to accurate function identification; and rater error is substantial in manual function counting. The implication of these findings for organizations using function based metrics are explored.

Jeffery, Ross↗
NASA NTRS1994

John F. Kennedy Space Center, Safety, Reliability, Maintainability and Quality Assurance, Survey and Audit Program

This document is the product of the KSC Survey and Audit Working Group composed of civil service and contractor Safety, Reliability, and Quality Assurance (SR&QA) personnel. The program described herein provides standardized terminology, uniformity of survey and audit operations, and emphasizes process assessments rather than a program based solely on compliance. The program establishes minimum training requirements, adopts an auditor certification methodology, and includes survey and audit metrics for the audited organizations as well as the auditing organization.

Source record↗
NASA NTRS2011

Bootstrapping Process Improvement Metrics: CMMI Level 4 Process Improvement Metrics in a Level 3 World

The measurement techniques for organizations which have achieved the Software Engineering Institutes CMMI Maturity Levels 4 and 5 are well documented. On the other hand, how to effectively measure when an organization is Maturity Level 3 is less well understood, especially when there is no consistency in tool use and there is extensive tailoring of the organizational software processes. Most organizations fail in their attempts to generate, collect, and analyze standard process improvement metrics under these conditions. But at JPL, NASA's prime center for deep space robotic exploration, we have a long history of proving there is always a solution: It just may not be what you expected. In this paper we describe the wide variety of qualitative and quantitative techniques we have been implementing over the last few years, including the various approaches used to communicate the results to both software technical managers and senior managers.

Hihn, Jairus↗
NASA NTRS2004

Subsystem Details for the Fiscal Year 2004 Advanced Life Support Research and Technology Development Metric

This document provides values at the assembly level for the subsystems described in the Fiscal Year 2004 Advanced Life Support Research and Technology Development Metric (Hanford, 2004). Hanford (2004) summarizes the subordinate computational values for the Advanced Life Support Research and Technology Development (ALS R&TD) Metric at the subsystem level, while this manuscript provides a summary at the assembly level. Hanford (2004) lists mass, volume, power, cooling, and crewtime for each mission examined by the ALS R&TD Metric according to the nominal organization for the Advanced Life Support (ALS) elements. The values in the tables below, Table 2.1 through Table 2.8, list the assemblies, using the organization and names within the Advanced Life Support Sizing Analysis Tool (ALSSAT) for each ALS element. These tables specifically detail mass, volume, power, cooling, and crewtime. Additionally, mass and volume are designated in terms of values associated with initial hardware and resupplied hardware just as they are within ALSSAT. The overall subsystem values are listed on the line following each subsystem entry. These values are consistent with those reported in Hanford (2004) for each listed mission. Any deviations between these values and those in Hanford (2004) arise from differences in when individual numerical values are rounded within each report, and therefore the resulting minor differences should not concern even a careful reader. Hanford (2004) u es the uni ts kW(sub e) and kW(sub th) for power and cooling, respectively, while the nomenclature below uses W(sub e) and W(sub th), which is consistent with the native units within ALSSAT. The assemblies, as specified within ALSSAT, are listed in bold below their respective subsystems. When recognizable assembly components are not listed within ALSSAT, a summary of the assembly is provided on the same line as the entry for the assembly. Assemblies with one or more recognizable components are further described by the indented entries below them. See Yeh, et al. (2002), Yeh, et al. (2003), and Yeh, et al. (2004) for details about ALSSAT organization. Except for the dry food mass listed within the Food Processing, Packaging, and Storage within the Food Subsystem, total values for assemblies would be the sum of their components. The Dry Food Mass, however, is that portion of the food system that was neglected during the computation of the Fiscal Year 2004 ALS R&TD Metric. It is listed here to provide a reference, but it is otherwise ignored in the overall totals. See Hanford (2004) for details of this process and supporting rationale. When applicable, the technology label from ALSSAT is listed in the second column, and the associated abbreviations are listed below in Section 4. For more details of the technologies assumed for each mission, please see Hanford (2004) for descriptions of each subsystem and an overall life support system schematic.

Hanford, Anthony J.↗
NASA NTRS2005

Inorganic Photovoltaics Materials and Devices: Past, Present, and Future

This report describes recent aspects of advanced inorganic materials for photovoltaics or solar cell applications. Specific materials examined will be high-efficiency silicon, gallium arsenide and related materials, and thin-film materials, particularly amorphous silicon and (polycrystalline) copper indium selenide. Some of the advanced concepts discussed include multi-junction III-V (and thin-film) devices, utilization of nanotechnology, specifically quantum dots, low-temperature chemical processing, polymer substrates for lightweight and low-cost solar arrays, concentrator cells, and integrated power devices. While many of these technologies will eventually be used for utility and consumer applications, their genesis can be traced back to challenging problems related to power generation for aerospace and defense. Because this overview of inorganic materials is included in a monogram focused on organic photovoltaics, fundamental issues and metrics common to all solar cell devices (and arrays) will be addressed.

Hepp, Aloysius F.↗
NASA NTRS2021

How Well Do Multisatellite Products Capture the Space–Time Dynamics of Precipitation? Part I: Five Products Assessed via a Wavenumber–Frequency Decomposition

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Clement Guilloteau↗
NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗
NASA NTRS2022

QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape using the Apparent Visible Wavelength

The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the waterleaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis.

remote sensing reflectance↗
NASA NTRS2016

Performance Evaluation of the Approaches and Algorithms for Hamburg Airport Operations

In this work, fast-time simulations have been conducted using SARDA tools at Hamburg airport by NASA and real-time simulations using CADEO and TRACC with the NLR ATM Research Simulator (NARSIM) by DLR. The outputs are analyzed using a set of common metrics collaborated between DLR and NASA. The proposed metrics are derived from International Civil Aviation Organization (ICAO)s Key Performance Areas (KPAs) in capability, efficiency, predictability and environment, and adapted to simulation studies. The results are examined to explore and compare the merits and shortcomings of the two approaches using the common performance metrics. Particular attention is paid to the concept of the close-loop, trajectory-based taxi as well as the application of US concept to the European airport. Both teams consider the trajectory-based surface operation concept a critical technology advance in not only addressing the current surface traffic management problems, but also having potential application in unmanned vehicle maneuver on airport surface, such as autonomous towing or TaxiBot [6][7] and even Remote Piloted Aircraft (RPA). Based on this work, a future integration of TRACC and SOSS is described aiming at bringing conflict-free trajectory-based operation concept to US airport.

air traffic management↗
NASA NTRS2005

Optimal Sensor Selection for Health Monitoring Systems

Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

Santi, L. Michael↗