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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Collecting and Processing Earth Science Data Metrics at NASA ESDIS

Since the launch of Terra satellite in 1999, the number of Earth Science remote sensing data products created and distributed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) has increased from a few hundred to nearly ten thousand. NASA's Earth Science Data and Information System (ESDIS) Metrics System (EMS) collects metrics on data ingest, archive, and distribution by its Distributed Active Archive Centers (DAACs) and the Science Investigator-led Systems (SIPS), known as Data Providers. These metrics are critical in helping NASA management as well as data producers in resource planning and gaining a wide range of knowledge of data users and data usage.EMS receives flat files, or log files of data archive, ingest, and distribution either in their raw format, such as Apache web logs, or text files of log records formatted by the Data Providers. Tens of millions of records are processed each day to extract metrics on data products, user information, distribution protocols and services, and so on. The metrics are then made available to designated parties.This presentation provides an overview of the EMS processing workflow and improvement efforts made in recent years to handle ever-increasing number of data records and new metrics requirements, discusses several key steps including mapping log records to data products and identifying user communities along with geo-distribution, and demonstrates typical metrics capabilities produced by the EMS system. Challenges and potential approaches to improve the system are also discussed.

Pan, Jianfu↗
NASA NTRSDate not supplied

Analytical and Experimental Demonstration of an Alternate Mixing Performance Metric for High-Speed Fuel Mixing Studies

To experimentally assess the fuel/air mixing performance of high-speed fuel injectors, one-dimensional metrics that quantify the degree of mixing completeness downstream of the fuel injection location are required. The most accurate assessment of mixing performance is achieved with the mixing efficiency parameter. In order to experimentally determine the mixing efficiency parameter, the spatial distributions of both mass flux and fuel mass fraction must be measured. In-stream gas sampling techniques are commonly used to measure the fuel mass fraction distribution; however, the mass flux distribution is not easily determined because it requires the measurement of three independent aerothermodynamic variables in addition to the gas composition. Therefore, to experimentally determine the mixing efficiency parameter, the spatial distributions of four independent properties must be measured, with each property generally requiring its own unique probe. Because of this difficulty, it is commonly assumed that alternate metrics, which rely solely on the fuel distribution, are good indicators of mixing performance. However, since these alternate metrics do not provide a mass flux-weighted measure of mixing completeness, they can lead to incorrect conclusions being drawn about the mixing performance of the studied fuel injector configuration. Recognizing this shortcoming, this work proposes two new alternate mixing performance metrics that are easier to obtain than the mixing efficiency parameter. The analytical development of the new metrics, as well as their application to relevant CFD and experimental data of high-speed fuel injector configurations, is presented in this work. For two different experiments, the new metrics are shown to provide an excellent representation of the true mass flux-weighted mixing performance, unlike the traditionally-used alternatives. The results presented herein suggest that the new metrics can serve as accurate surrogates for mixing efficiency in future high-speed fuel/air mixing studies.

Cody R Ground↗
NASA NTRSDate not supplied

Metrics in Space Life Support Technology Selection

Engineering metrics are useful in space life support technology selection, but they must be carefully used. Metrics are only part of a complete system trade-off. Metrics do harm if they cause neglect of other important technical, organizational, or intuitive decision factors. Two metrics have damaged space life support, closure and Equivalent Systems Mass (ESM). Closure measures the fraction of the required system inputs that are produced by recycling system outputs. Increasing closure produces diminishing returns and becomes increasingly expensive. Increasing closure does not directly contribute to providing better life support. ESM measures the total launch mass required to provide life support. ESM includes the mass of the system hardware and of its power, cooling, pressurized volume, spares, and logistics. ESM predicts launch costs, but recently launch costs have been reduced by a factor of 20 or more. System development cost for space hardware is often much greater than launch cost. The past nearly exclusive use of ESM has led to the neglect of Life Cycle Cost (LCC), reliability, cost, and the other engineering factors. Closure and ESM have misguided space life support technology selection for more than twenty years and have adversely affected the expenditure of 100’s of millions of dollars. Metrics can be effectively used three ways in space life support technology selection: 1. A small set of key engineering metrics for preliminary screening. 2. A full set of engineering to guide technical selection. 3. Combining engineering metrics with organizational, political, and intuitive decision factors to understand technology selection. The past emphasis on closure and ESM served to support recycling life support over resupply and built on the intuitive appeal of a human ecosystem in space.

Harry W Jones↗
NASA NTRSDate not supplied

The Effective Use of Metrics in Space Life Support System Trade-Offs

Engineering metrics are useful in space life support technology selection, but they must be carefully used. Metrics are only part of a complete system trade-off. Metrics do harm if they cause neglect of other important technical, organizational, or intuitive decision factors. Two metrics have damaged space life support, closure and Equivalent Systems Mass (ESM). Closure measures the fraction of the required system inputs that are produced by recycling system outputs. Increasing closure produces diminishing returns and becomes increasingly expensive. Increasing closure does not directly contribute to providing better life support. ESM measures the total launch mass required to provide life support. ESM includes the mass of the system hardware and of its power, cooling, pressurized volume, spares, and logistics. ESM predicts launch costs, but recently launch costs have been reduced by a factor of 20 or more. System development cost for space hardware is often much greater than launch cost. The past nearly exclusive use of ESM has led to the neglect of Life Cycle Cost (LCC), reliability, cost, and the other engineering factors. Closure and ESM have misguided space life support technology selection for more than twenty years and have adversely affected the expenditure of 100’s of millions of dollars. Metrics can be effectively used three ways in space life support technology selection: 1. A small set of key engineering metrics for preliminary screening. 2. A full set of engineering to guide technical selection. 3. Combining engineering metrics with organizational, political, and intuitive decision factors to understand technology selection. The past emphasis on closure and ESM served to support recycling life support over resupply and built on the intuitive appeal of a human ecosystem in space.

Harry W. Jones↗
NASA NTRSDate not supplied

The Inspectability Metric: A Formalized System Of Measurement Enabling The Design For Inspection Framework

Nondestructive evaluation (NDE) engineers are often confronted with structural design choices that present challenges to meeting inspection requirements. These challenges, at best, increase the resources needed to design an inspection solution and, at worst, require resource intensive redesign of the structure. If the inspectability of the structure can be determined early in the design cycle, these challenging inspection scenarios can be avoided. The emergence of additive manufacturing has further compounded this problem by enabling the creation of highly optimized structures with no regard to inspection constraints. Design for inspection (DFI) offers a framework to integrate nondestructive evaluation (NDE) into the design process to alleviate the mechanisms that produce uninspectable designs. DFI is the concept of including inspectability in a multi-objective optimization framework so that it can be considered in parallel to other metrics such as mass and manufacturability. This allows rapid evaluation of the trade-off between design metrics to find solutions that meet the inspection needs of a particular material system, structural concept, or vehicle program. To enable DFI, there must be a system by which the inspectability of a structure can be measured. This system must be agile to produce results quickly, it must be versatile to work with the type of incomplete information one would encounter early in the design process (such as lack of inspection requirements), and it must be delivered in a form that is easily understood by designers. To meet this need, this presentation introduces the novel inspectability metric as a system to measure inspectability. The inspectability metric is a standardized, automation friendly procedure that uses simulations to determine inspectability. Along with guidelines to properly process designs and integrate with existing workflows, the inspectability metric provides a suite of simulation tests to interrogate the ability to find defects and the sensitivity to variability. The testing rubric is designed to maximize the coverage of the parameter space while minimizing the number of simulations needed. The inspectability metric has been in development in collaboration with industry partners to ensure compatibility with modern simulation tools and aerospace design workflows. In this study, we will demonstrate how the inspectability metric is able to determine the inspectability of multiple types of structures, including aerospace composites and additively manufactured parts. We will then show how the inspectability score can be plugged into existing design optimization tasks, such as structural sizing algorithms or design for manufacturing (DFM) frameworks.

Design for inspection↗
DOE OSTI2025

Velocity- and pressure-based metrics for estimating strike injuries during fish passage through hydro turbines

Hydropower is a crucial source of clean and reliable energy worldwide, and its importance will continue to grow. To enhance the environmental sustainability of hydropower development and operations, it is essential to predict the strike injury rates of fish passing through turbines accurately and cost-effectively. However, conventional experiments involving a large number of live fish are still commonly conducted in practice, and previous attempts mainly focused on the dose-response relationships at an individual level or relative comparisons of biological characterization between different conditions. Thus, this study proposes two novel strike metrics based on velocity and pressure (M V and M P ) measured by the cutting-edge Sensor Fish (SF) technology, designed to quantify the biological effects of strikes and collisions between fish and rigid hydraulic structures. SF measurements and 48-hour survival rates of live fish from a study conducted at the Ice Harbor Dam on the Snake River were used to establish thresholds for the two proposed metrics. These metrics and thresholds were then applied to three additional SF studies conducted at hydropower facilities featuring Kaplan turbines within the Columbia River basin. Results from the application of the strike metrics indicate that the estimated survival rates derived from both metrics agree well with previously reported live fish survival rates. Furthermore, the study identified specific passage regions with elevated risks of fish mortality due to strikes and collisions. Overall, the proposed strike metrics present a reliable and cost-effective approach to reducing reliance on live fish and lowering costs in fish passage studies.

Biological characterization↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Resilience Metrics Framework for Solar Photovoltaics

This presentation was given at the Photovoltaic Specialist Conference (PVSC) 54 in New Orleans, Louisiana. Photovoltaic (PV) systems are routinely exposed to extreme weather, including wind and hail storms. Historically, most systems have proven to be resilient to such events, but some storms have damaged PV systems, leading to physical and financial loss. Storm hardening measures and specific system attributes can reduce this risk. This work introduces a set of resilience metrics and a framework for quantifying, comparing, and predicting PV system resilience. The framework is divided into two parts: 1) predictive, attribute metrics based on site and component characteristics, and 2) impact metrics that assess post-storm performance. Metrics are weighted and aggregated, producing hazard-specific resilience scores. We derive damage functions from storm-impacted PV systems, establishing a baseline against which post-storm performance can be compared. This damage was widely variable across hail and wind intensities, and field hail damage was less than predicted by laboratory tests, suggesting that system features - in addition to storm conditions - influence damage likelihood. Finally, the metrics framework is demonstrated using three case studies of storm damaged PV systems. Although additional data are needed to create attribute specific damage functions and establish metric weights, this study presents a methodology for evaluating PV resilience and contributes new damage functions to the literature.

14 SOLAR ENERGY↗
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↗
DOE OSTI2026

Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics

Reliable operation of burning plasma tokamaks will require robust control strategies to avoid macroscopic instability limits such as the L-mode and H-mode density limits (LDL, HDL). In this work, we explore closed-loop avoidance of these phenomena at DIII-D using machine-learned risk metrics. Feedback control is implemented via the ‘DL Supervisor’ scheme, which regulates the chosen risk metric by reducing the density target or increasing NBI heating in real-time. Using the LDL 25 risk metric, the LDL is reproducibly suppressed. We also introduce an HDL risk metric in this study, HDL 25 , which reduces the False Positive Rate by 2x compared to the Greenwald fraction. Applying this scaling to a plasma current ramp-down, we successfully avoid an HDL-driven H/L back-transition. These experiments constitute the first demonstration of real-time DL avoidance using machine-learned risk metrics. These instability metrics outline a path to safer high-density operation, more reliable ramp-down scenarios, and improved off-normal control for next-step devices such as ITER and SPARC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1981

Handbook of aircraft noise metrics

Information is presented on 22 noise metrics that are associated with the measurement and prediction of the effects of aircraft noise. Some of the instantaneous frequency weighted sound level measures, such as A-weighted sound level, are used to provide multiple assessment of the aircraft noise level. Other multiple event metrics, such as day-night average sound level, were designed to relate sound levels measured over a period of time to subjective responses in an effort to determine compatible land uses and aid in community planning. The various measures are divided into: (1) instantaneous sound level metrics; (2) duration corrected single event metrics; (3) multiple event metrics; and (4) speech communication metrics. The scope of each measure is examined in terms of its: definition, purpose, background, relationship to other measures, calculation method, example, equipment, references, and standards.

Bennett, R. L.↗
NASA NTRS1984

The independence of software metrics taken at different life-cycle stages

Over the past few years a large number of software metrics have been proposed and, in varying degrees, a number of these metrics have been subjected to empirical validation which demonstrated the utility of the metrics in the software development process. Attempts to classify these metrics and to determine if the metrics in these different classes appear to be measuring distinct attributes of the software product are studied. Statistical analysis is used to determine the degree of relationship among the metrics.

Kafura, D.↗
NASA NTRS1983

Metric analysis and data validation across FORTRAN projects

The desire to predict the effort in developing or explaining the quality of software has led to the proposal of several metrics. As a step toward validating these metrics, the Software Engineering Laboratory (SEL) has analyzed the software science metrics, cyclomatic complexity, and various standard program measures for their relation to effort (including design through acceptance testing), development errors (both discrete and weighted according to the amount of time to locate and fix), and one another. The data investigated are collected from a project FORTRAN environment and examined across several projects at once, within individual projects and by reporting accuracy checks demonstrating the need to validate a database. When the data comes from individual programmers or certain validated projects, the metrics' correlations with actual effort seem to be strongest. For modules developed entirely by individual programmers, the validity ratios induce a statistically significant ordering of several of the metrics' correlations. When comparing the strongest correlations, neither software science's E metric cyclomatic complexity not source lines of code appears to relate convincingly better with effort than the others.

Basili, Victor R.↗
NASA NTRS1990

Fighter agility metrics, research, and test

Proposed new metrics to assess fighter aircraft agility are collected and analyzed. A framework for classification of these new agility metrics is developed and applied. A completed set of transient agility metrics is evaluated with a high fidelity, nonlinear F-18 simulation provided by the NASA Dryden Flight Research Center. Test techniques and data reduction methods are proposed. A method of providing cuing information to the pilot during flight test is discussed. The sensitivity of longitudinal and lateral agility metrics to deviations from the pilot cues is studied in detail. The metrics are shown to be largely insensitive to reasonable deviations from the nominal test pilot commands. Instrumentation required to quantify agility via flight test is also considered. With one exception, each of the proposed new metrics may be measured with instrumentation currently available. Simulation documentation and user instructions are provided in an appendix.

Liefer, Randall K.↗
NASA NTRS1995

A Validation of Object-Oriented Design Metrics

This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of Object-Oriented (00) design metrics introduced by [Chidamber and Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of fault-prone classes. This study is complementary to [Lieand Henry, 1993] where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known 00 analysis/design method and the C++ programming language. Based on experimental results, the advantages and drawbacks of these 00 metrics are discussed and suggestions for improvement are provided. Several of Chidamber and Kemerer's 00 metrics appear to be adequate to predict class fault-proneness during the early phases of the life-cycle. We also showed that they are, on our data set, better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.

Basili, Victor R.↗
NASA NTRS1997

A Validation of Object-Oriented Design Metrics as Quality Indicators

This paper presents the results of a study in which we empirically investigated the suits of object-oriented (00) design metrics introduced in another work. More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known 00 analysis/design method and the C++ programming language. Based on empirical and quantitative analysis, the advantages and drawbacks of these 00 metrics are discussed. Several of Chidamber and Kamerer's 00 metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than 'traditional' code metrics, which can only be collected at a later phase of the software development processes.

Basili, Victor R.↗
NASA NTRS1998

Testing, Requirements, and Metrics

The criticality of correct, complete, testable requirements is a fundamental tenet of software engineering. Also critical is complete requirements based testing of the final product. Modern tools for managing requirements allow new metrics to be used in support of both of these critical processes. Using these tools, potential problems with the quality of the requirements and the test plan can be identified early in the life cycle. Some of these quality factors include: ambiguous or incomplete requirements, poorly designed requirements databases, excessive or insufficient test cases, and incomplete linkage of tests to requirements. This paper discusses how metrics can be used to evaluate the quality of the requirements and test to avoid problems later. Requirements management and requirements based testing have always been critical in the implementation of high quality software systems. Recently, automated tools have become available to support requirements management. At NASA's Goddard Space Flight Center (GSFC), automated requirements management tools are being used on several large projects. The use of these tools opens the door to innovative uses of metrics in characterizing test plan quality and assessing overall testing risks. In support of these projects, the Software Assurance Technology Center (SATC) is working to develop and apply a metrics program that utilizes the information now available through the application of requirements management tools. Metrics based on this information provides real-time insight into the testing of requirements and these metrics assist the Project Quality Office in its testing oversight role. This paper discusses three facets of the SATC's efforts to evaluate the quality of the requirements and test plan early in the life cycle, thus preventing costly errors and time delays later.

Rosenberg, Linda↗
NASA NTRS2001

A Sensor-Independent Gust Hazard Metric

A procedure for calculating an intuitive hazard metric for gust effects on airplanes is described. The hazard metric is for use by pilots and is intended to replace subjective pilot reports (PIREPs) of the turbulence level. The hazard metric is composed of three numbers: the first describes the average airplane response to the turbulence, the second describes the positive peak airplane response to the gusts, and the third describes the negative peak airplane response to the gusts. The hazard metric is derived from any time history of vertical gust measurements and is thus independent of the sensor making the gust measurements. The metric is demonstrated for one simulated airplane encountering different types of gusts including those derived from flight data recorder measurements of actual accidents. The simulated airplane responses to the gusts compare favorably with the hazard metric.

Stewart, Eric C.↗