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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards

This viewgraph presentation reviews the principles of establishing and verifying the traceability of remote sensing measurements to national and international scales. Doing this allows comparisons to be made independent of time or locale, and improves understanding of instrument performance, provides confidence in the accuracy of the measurements, improves measurement accuracy and helps contractors understand and meet agency requirements, protecting contractor and customer.

Fraser, Gerald T.↗
NASA NTRS2014

Improved Traceability of Mission Concept to Requirements Using Model Based Systems Engineering

Model Based Systems Engineering (MBSE) has recently been gaining significant support as a means to improve the traditional document-based systems engineering (DBSE) approach to engineering complex systems. In the spacecraft design domain, there are many perceived and propose benefits of an MBSE approach, but little analysis has been presented to determine the tangible benefits of such an approach (e.g. time and cost saved, increased product quality). This thesis presents direct examples of how developing a small satellite system model can improve traceability of the mission concept to its requirements. A comparison of the processes and approaches for MBSE and DBSE is made using the NASA Ames Research Center SporeSat CubeSat mission as a case study. A model of the SporeSat mission is built using the Systems Modeling Language standard and No Magics MagicDraw modeling tool. The model incorporates mission concept and requirement information from the missions original DBSE design efforts. Active dependency relationships are modeled to analyze the completeness and consistency of the requirements to the mission concept. Overall experience and methodology are presented for both the MBSE and original DBSE design efforts of SporeSat.

Systems Engineering↗
NASA NTRS2014

Improved Traceability of a Small Satellite Mission Concept to Requirements Using Model Based System Engineering

Model Based Systems Engineering (MBSE) has recently been gaining significant support as a means to improve the "traditional" document-based systems engineering (DBSE) approach to engineering complex systems. In the spacecraft design domain, there are many perceived and propose benefits of an MBSE approach, but little analysis has been presented to determine the tangible benefits of such an approach (e.g. time and cost saved, increased product quality). This paper presents direct examples of how developing a small satellite system model can improve traceability of the mission concept to its requirements. A comparison of the processes and approaches for MBSE and DBSE is made using the NASA Ames Research Center SporeSat CubeSat mission as a case study. A model of the SporeSat mission is built using the Systems Modeling Language standard and No Magic's MagicDraw modeling tool. The model incorporates mission concept and requirement information from the mission's original DBSE design efforts. Active dependency relationships are modeled to demonstrate the completeness and consistency of the requirements to the mission concept. Anecdotal information and process-duration metrics are presented for both the MBSE and original DBSE design efforts of SporeSat.

Model Based Systems Engineering↗
NASA NTRS2018

Carbon-Nitrogen Coupling Under Three Schemes of Model Representation: A Traceability Analysis

The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C-N coupling differs greatly among models, and how these diverse representations of C-N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varied with three different commonly used schemes of C-N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C-N models (i.e., TECO-CN, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, down regulation of photosynthesis, and the pathways of N import. We incorporated the three C-N coupling schemes into the C-only version of the TECO model and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all three of the C-N schemes caused significant reductions in steady-state C storage capacity compared with the C-only version with magnitudes of −23%, −30%, and −54% for SM1, SM2, and SM3, respectively. This reduced C storage capacity was mainly derived from the combined effects of decreases in net primary productivity (NPP; −29%, −15%, and −45%) and changes in mean C residence time (MRT; 9%, −17%, and −17%) for SM1, SM2, and SM3, respectively. The differences in NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue C : N ratio, down regulation of photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant vs. microbe competition strategy and the plant N uptake, combined with the flexible C : N ratio in vegetation and soils, led to a notable spread in MRT. These results highlight the fact that the diverse assumptions on N processes represented by different C-N coupled models could cause additional uncertainty for land surface models. Understanding their difference can help us improve the capability of models to predict future biogeochemical cycles of terrestrial ecosystems.

Du, Zhenggang↗
NASA NTRS2019

SI-Traceable Calibration of Satellite Microwave Radiometers

Background (what): SI (International System of Units)-traceable Microwave Radiometer calibration; Motivation (why): NWP (Numerical Weather Prediction), FCDR (Fundamental Climate Data Record); Technology (how): NIST (National Inst. of Standards and Technology) blackbody target for ; Standards: Status & Future Plans. Development of a National Standard for Microwave Brightness Temperature (TB) at NIST.

MW Radiometer TB Calibration↗
NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

An interesting benefit of applying Model-Based Systems Engineering (MBSE) is that the rigor and coordination intrinsic to MBSE forces us to apply Systems Engineering to our own traditional activities, processes, and products, which results in richer, more expressive models, more powerful reasoning, and a clearer and more effective Systems Engineering (SE) process. Our MBSE frameworks and languages contain semantic richness sufficient to describe our systems at any particular point in time, often with an emphasis on the description of the system at major milestones. This is unarguably a real asset. However, when we apply MBSE in service of missions that are in development, rapidly evolving, of a larger scale, and where interpersonal communication is a critical part of the design process, we discover that our frameworks and languages are still not quite rich enough to enable us to ask the kinds of questions and get the kinds of answers we want in order to address the concerns of day to day work. This paper will discuss some patterns and tools we have developed to help address some of the not-always-explicit SE concerns that we have identified through our MBSE work. Particularly, this paper will discuss flexible yet practical methods for defining and capturing maturity, workflow, and agreement traceability within our system models, extensible ways to perform and track model audits, and ways to report and interact with this knowledge in the context of MBSE applied to support NASA’s Europa Project.

Jackson, Maddalena↗
NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

An interesting benefit of applying Model-Based Systems Engineering (MBSE) is that the rigor and coordination intrinsic to MBSE forces us to apply Systems Engineering to our own traditional activities, processes, and products, which results in richer, more expressive models, more powerful reasoning, and a clearer and more effective Systems Engineering (SE) process. Our MBSE frameworks and languages contain semantic richness sufficient to describe our systems at any particular point in time, often with an emphasis on the description of the system at major milestones. This is unarguably a real asset. However, when we apply MBSE in service of missions that are in development, rapidly evolving, of a larger scale, and where interpersonal communication is a critical part of the design process, we discover that our frameworks and languages are still not quite rich enough to enable us to ask the kinds of questions and get the kinds of answers we want in order to address the concerns of day to day work. This paper will discuss some patterns and tools we have developed to help address some of the not-always-explicit SE concerns that we have identified through our MBSE work. Particularly, this paper will discuss flexible yet practical methods for defining and capturing maturity, workflow, and agreement traceability within our system models, extensible ways to perform and track model audits, and ways to report and interact with this knowledge in the context of MBSE applied to support NASA’s Europa Project

Jackson, Maddalena↗
NASA NTRS2021

Origins Space Telescope Science Drivers to Design Traceability

The Origins Space Telescope (Origins) concept is designed to investigate the creation and dispersal of elements essential to life, the formation of planetary systems, and the transport of water to habitable worlds and the atmospheres of exoplanets around nearby K- and M-dwarfs to identify potentially habitable—and even inhabited—worlds. These science priorities are aligned with NASA’s three major astrophysics science goals: How does the Universe work? How did we get here? and Are we alone? We briefly describe the science case that arose from the astronomical community and the science traceability matrix for Origins. The science trace-ability matrix prescribes the design of Origins and demonstrates that it will address the key science questions motivated by the science case.

Infrared↗
NASA NTRS2021

Origins Space Telescope Science Drivers to Design Traceability

The Origins Space Telescope (Origins) concept is designed to investigate the creationand dispersal of elements essential to life, the formation of planetary systems, and the transportof water to habitable worlds and the atmospheres of exoplanets around nearby K- and M-dwarfsto identify potentially habitable—and even inhabited—worlds. These science priorities arealigned with NASA’s three major astrophysics science goals: How does the Universe work?How did we get here? and Are we alone? We briefly describe the science case that arose fromthe astronomical community and the science traceability matrix for Origins. The science trace-ability matrix prescribes the design of Origins and demonstrates that it will address the keyscience questions motivated by the science case.

Margaret Meixner↗
NASA NTRSDate not supplied

CLARREO Pathfinder as a SI-traceable Reference for Satellite Intercalibration

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) consists of an Earthviewing reflected solar (RS) spectrometer that will measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The high-accuracy CPF measurements will provide an in-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has been tasked to develop a state-of-the-art approach to calibrate the shortwave channel (300-5000 nm) of the Clouds and the Earth’s Radiant Energy System (CERES) instrument and the reflective solar bands (RSB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, both onboard the NOAA-20 satellite, against the CPF benchmark measurements. The aimed intercalibration methodology uncertainty for both the target instruments is also 0.3%. To meet this stringent intercalibration accuracy, the CPF team has developed methods for mitigating the impacts of spatial, spectral, and angular differences between the intercalibration footprints from the CPF and target instruments. To further alleviate uncertainty, the CPF team will employ Polarization Distribution Models (PDMs) to characterize the polarization state of the Earth-reflected radiance as a function of the intercalibration footprint scene type, solar and viewing geometry, and wavelength. The PDMs will assist in identifying low-polarized scene radiances for meticulously intercalibrating the polarization sensitive VIIRS instrument against the significantly-less polarization-sensitive CPF instrument. This paper will highlight the CPF mission overview, the details of the CPF intercalibration approach, and additional outcomes of the CPF intercalibration studies that may benefit the broader remote sensing community.community.

Hyperspectral↗
NASA NTRS1990

Cloud field reflectance variations traceable to finite cloud effects

Variations in cloud field reflectances resulting from the organization of clouds within a cloud field are examined using a cloud field optical simulator. The problem of changing spot size is studied as a function of view angle for a fixed field of view detector. Also, problems associated with the assumption of isotropy are discussed and the effect of cloud interactions within cloud fields is investigated. The results show that radiance variation increases when the photodiodes measure radiances from portions of cloud fields which move in and out of the fields of view as the position of the photodiode changes. In addition, the experiments reveal several factors that are important in the determination of accurate reflectances.

Alberta, Timothy L.↗
NASA NTRS2012

Recent Surface Reflectance Measurement Campaigns with Emphasis on Best Practices, SI Traceability and Uncertainty Estimation

A significant problem facing the optical satellite calibration community is limited knowledge of the uncertainties associated with fundamental measurements, such as surface reflectance, used to derive satellite radiometric calibration estimates. In addition, it is difficult to compare the capabilities of calibration teams around the globe, which leads to differences in the estimated calibration of optical satellite sensors. This paper reports on two recent field campaigns that were designed to isolate common uncertainties within and across calibration groups, particularly with respect to ground-based surface reflectance measurements. Initial results from these efforts suggest the uncertainties can be as low as 1.5% to 2.5%. In addition, methods for improving the cross-comparison of calibration teams are suggested that can potentially reduce the differences in the calibration estimates of optical satellite sensors.

reflectance measurements↗
NASA NTRS2016

Calibration/Validation Error Budgets, Uncertainties, Traceability and Their Importance to Imaging Spectrometry

Knowledge of uncertainties and errors are essential for comparisons of remote sensing data across time, space, and spectral domains. Vicarious radiometric calibration is used to demonstrate the need for uncertainty knowledge and to provide an example error budget. The sample error budget serves as an example of the questions and issues that need to be addressed by the calibrationvalidation community as accuracy requirements for imaging spectroscopy data will continue to become more stringent in the future. Error budgets will also be critical to ensure consistency between the range of imaging spectrometers expected to be launched in the next five years.

Thome, K.↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well-formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA's proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA’s proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗