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NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
NASA NTRS1989

[NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 2:] Technical communications in aeronautics: Results of an exploratory study. An analysis of managers' and nonmanagers' responses

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that aerospace managers and nonmanagers have different technical communications practices. Five assumptions were established for the analysis. Aerospace managers and nonmanagers were found to have different technical communications practices for three of the five assumptions tested. Although aerospace managers and nonmanagers were found to have different technical communications practices, the evidence was neither conclusive nor compelling that the presumption of difference in practices could be attributed to the duties performed by aerospace managers and nonmanagers.

Pinelli, Thomas E.↗
NASA NTRS2010

Louisiana Air Quality - Using ASTER, Landsat 5, and MODIS to Assess the Impact of Sugar Cane and Marsh Burning Practices on Local Air Quality

Biomass burning is an event that occurs globally and encompasses both human-initiated and naturally-occurring fires. It is estimated that 3 billion metric tons of biomass are burned every year worldwide (Curtis 2002). Societies have used these burning techniques for cooking and heating, clearing land for agricultural use, and removing excess biomass from grazing and croplands (Levine 1991). Our study focuses on the state of Louisiana and its commonly occurring methods of sugarcane and marsh biomass burning (LSU Ag.Center 2000; Nyman and Chabreck 1995). Over the centuries, the sugarcane industry in this state has steadily grown to surpass all other agriculture commodities. To promote efficiency within this large industry, burning excess biomass takes place throughout the harvesting period (LSU Ag.Center 2000). In addition to sugarcane, Louisiana contains 30% of the total coastal marsh of the United States (LSU Ag.Center 2000). The periodic burning of such marshes is an ecologically important management tool that is practiced throughout the Atlantic and Gulf Coasts (Nyman and Chabreck 1995). In most biomass burning instances, the leading by-product is particulate matter that is less than 10 microns in diameter (PM10). Through past research, this fine material has been shown to have negative health effects on surrounding populations (Boopathy2001). While burning guidelines have been set into place by the Louisiana Department of Agriculture and Forestry (LDAF) to reduce health effects, the guidelines are voluntary (LDAF 2000). To help quantify emission estimates, we will focus on Iberia Parish for sugarcane burning and Cameron Parish for marsh burning. Through analysis of ASTER, Landsat 5 TM, and MODIS data, our goal is to determine the amount and location of land area burned for the years 2008 and 2009 due to these practices. With emissions algorithms from Seiler and Crutzen, 1980, total acreage burned can be used to estimate emissions. This information will help to document the impact of these smoke plumes on local populations for the improvement of biomass burning policies in Louisiana.

Clark, Robert↗
NASA NTRS2017

Digital Model-Based Engineering: Expectations, Prerequisites, and Challenges of Infusion

Digital model-based engineering (DMbE) is the use of digital artifacts, digital environments, and digital tools in the performance of engineering functions. DMbE is intended to allow an organization to progress from documentation-based engineering methods to digital methods that may provide greater flexibility, agility, and efficiency. The term 'DMbE' was developed as part of an effort by the Model-Based Systems Engineering (MBSE) Infusion Task team to identify what government organizations might expect in the course of moving to or infusing MBSE into their organizations. The Task team was established by the Interagency Working Group on Engineering Complex Systems, an informal collaboration among government systems engineering organizations. This Technical Memorandum (TM) discusses the work of the MBSE Infusion Task team to date. The Task team identified prerequisites, expectations, initial challenges, and recommendations for areas of study to pursue, as well as examples of efforts already in progress. The team identified the following five expectations associated with DMbE infusion, discussed further in this TM: (1) Informed decision making through increased transparency, and greater insight. (2) Enhanced communication. (3) Increased understanding for greater flexibility/adaptability in design. (4) Increased confidence that the capability will perform as expected. (5) Increased efficiency. The team identified the following seven challenges an organization might encounter when looking to infuse DMbE: (1) Assessing value added to the organization. Not all DMbE practices will be applicable to every situation in every organization, and not all implementations will have positive results. (2) Overcoming organizational and cultural hurdles. (3) Adopting contractual practices and technical data management. (4) Redefining configuration management. The DMbE environment changes the range of configuration information to be managed to include performance and design models, database objects, as well as more traditional book-form objects and formats. (5) Developing information technology (IT) infrastructure. Approaches to implementing critical, enabling IT infrastructure capabilities must be flexible, reconfigurable, and updatable. (6) Ensuring security of the single source of truth (7) Potential overreliance on quantitative data over qualitative data. Executable/ computational models and simulations generally incorporate and generate quantitative vice qualitative data. The Task team also developed several recommendations for government, academia, and industry, as discussed in this TM. The Task team recommends continuing beyond this initial work to further develop the means of implementing DMbE and to look for opportunities to collaborate and share best practices.

Hale, J. P.↗
NASA NTRS2013

NASA Software Engineering Benchmarking Study

To identify best practices for the improvement of software engineering on projects, NASA's Offices of Chief Engineer (OCE) and Safety and Mission Assurance (OSMA) formed a team led by Heather Rarick and Sally Godfrey to conduct this benchmarking study. The primary goals of the study are to identify best practices that: Improve the management and technical development of software intensive systems; Have a track record of successful deployment by aerospace industries, universities [including research and development (R&D) laboratories], and defense services, as well as NASA's own component Centers; and Identify candidate solutions for NASA's software issues. Beginning in the late fall of 2010, focus topics were chosen and interview questions were developed, based on the NASA top software challenges. Between February 2011 and November 2011, the Benchmark Team interviewed a total of 18 organizations, consisting of five NASA Centers, five industry organizations, four defense services organizations, and four university or university R and D laboratory organizations. A software assurance representative also participated in each of the interviews to focus on assurance and software safety best practices. Interviewees provided a wealth of information on each topic area that included: software policy, software acquisition, software assurance, testing, training, maintaining rigor in small projects, metrics, and use of the Capability Maturity Model Integration (CMMI) framework, as well as a number of special topics that came up in the discussions. NASA's software engineering practices compared favorably with the external organizations in most benchmark areas, but in every topic, there were ways in which NASA could improve its practices. Compared to defense services organizations and some of the industry organizations, one of NASA's notable weaknesses involved communication with contractors regarding its policies and requirements for acquired software. One of NASA's strengths was its software assurance practices, which seemed to rate well in comparison to the other organizational groups and also seemed to include a larger scope of activities. An unexpected benefit of the software benchmarking study was the identification of many opportunities for collaboration in areas including metrics, training, sharing of CMMI experiences and resources such as instructors and CMMI Lead Appraisers, and even sharing of assets such as documented processes. A further unexpected benefit of the study was the feedback on NASA practices that was received from some of the organizations interviewed. From that feedback, other potential areas where NASA could improve were highlighted, such as accuracy of software cost estimation and budgetary practices. The detailed report contains discussion of the practices noted in each of the topic areas, as well as a summary of observations and recommendations from each of the topic areas. The resulting 24 recommendations from the topic areas were then consolidated to eliminate duplication and culled into a set of 14 suggested actionable recommendations. This final set of actionable recommendations, listed below, are items that can be implemented to improve NASA's software engineering practices and to help address many of the items that were listed in the NASA top software engineering issues. 1. Develop and implement standard contract language for software procurements. 2. Advance accurate and trusted software cost estimates for both procured and in-house software and improve the capture of actual cost data to facilitate further improvements. 3. Establish a consistent set of objectives and expectations, specifically types of metrics at the Agency level, so key trends and models can be identified and used to continuously improve software processes and each software development effort. 4. Maintain the CMMI Maturity Level requirement for critical NASA projects and use CMMI to measure organizations developing software for NASA. 5.onsolidate, collect and, if needed, develop common processes principles and other assets across the Agency in order to provide more consistency in software development and acquisition practices and to reduce the overall cost of maintaining or increasing current NASA CMMI maturity levels. 6. Provide additional support for small projects that includes: (a) guidance for appropriate tailoring of requirements for small projects, (b) availability of suitable tools, including support tool set-up and training, and (c) training for small project personnel, assurance personnel and technical authorities on the acceptable options for tailoring requirements and performing assurance on small projects. 7. Develop software training classes for the more experienced software engineers using on-line training, videos, or small separate modules of training that can be accommodated as needed throughout a project. 8. Create guidelines to structure non-classroom training opportunities such as mentoring, peer reviews, lessons learned sessions, and on-the-job training. 9. Develop a set of predictive software defect data and a process for assessing software testing metric data against it. 10. Assess Agency-wide licenses for commonly used software tools. 11. Fill the knowledge gap in common software engineering practices for new hires and co-ops.12. Work through the Science, Technology, Engineering and Mathematics (STEM) program with universities in strengthening education in the use of common software engineering practices and standards. 13. Follow up this benchmark study with a deeper look into what both internal and external organizations perceive as the scope of software assurance, the value they expect to obtain from it, and the shortcomings they experience in the current practice. 14. Continue interactions with external software engineering environment through collaborations, knowledge sharing, and benchmarking.

Rarick, Heather L.↗
NASA NTRS2020

IV&V Assurance Case Design for Artemis II

As human-rated missions like those in NASA’s Artemis program continue to grow in both size and complexity, and the role of software in achieving mission objectives expands dramatically, NASA’s Independent Verification and Validation (IV&V) Teams face evolving challenges in assuring the safety and performance of the safety- and mission-critical embedded software that is essential to landing astronauts on the surface of the Moon by 2024. Key among these challenges is IV&V’s desire to present a cohesive, integrated assurance statement to its stakeholders that encapsulates and summarizes our assurance positions across the integrated Artemis systems and their combined role in support of a safe and successful flight. In order to meet this challenge, the IV&V Teams have begun a transition to using formal assurance case concepts and documentation in the Goal Structuring Notation (GSN) to build an argument in support of software assurance. IV&V recognizes significant benefits to the logical argumentation structure provided by assurance cases and GSN over our current practices for documenting and managing assurance claims. In order to reap these benefits, IV&V is integrating the use of assurance case concepts with our paradigm of follow-the-risk capability based assurance. Because of this, assurance cases created and used by IV&V are distinct from the sort of assurance case created by a development project or embedded software assurance organization. IV&V’s assurance cases depend much less upon standards and regulations, and more on evidence captured by IV&V regarding the environment, requirements, design, and implementation. IV&V constructs an independent network of claims based on an independent decomposition of arguments. Based upon the risk posture of these claims and their associated software and software artifacts, IV&V then develops and executes engineering analyses and testing, which provide evidence to either support or refute the claim. This emerging risk-informed assurance case methodology is being put into practice as IV&V plans for support of the Artemis II mission, the first flight of the Orion capsule and Space Launch System with astronauts on board.

Gerek Whitman↗
NASA NTRS2020

IV&V Assurance Case Design for Artemis II

As human-rated missions like those in NASA's Artemis program continue to grow in both size and complexity, and the role of software in achieving mission objectives expands dramatically, NASA's Independent Verification and Validation (IV&V) Teams face evolving challenges in assuring the safety and performance of the safety- and mission-critical embedded software that is essential to landing astronauts on the surface of the Moon by 2024. Key among these challenges is IV&V's desire to present a cohesive, integrated assurance statement to its stakeholders that encapsulates and summarizes our assurance positions across the integrated Artemis systems and their combined role in support of a safe and successful flight. In order to meet this challenge, the IV&V Teams have begun a transition to using formal assurance case concepts and documentation in the Goal Structuring Notation (GSN) to build an argument in support of software assurance. IV&V recognizes significant benefits to the logical argumentation structure provided by assurance cases and GSN over our current practices for documenting and managing assurance claims. In order to reap these benefits, IV&V is integrating the use of assurance case concepts with our paradigm of follow-the-risk capability based assurance. Because of this, assurance cases created and used by IV&V are distinct from the sort of assurance case created by a development project or embedded software assurance organization. IV&V's assurance cases depend much less upon standards and regulations, and more on evidence captured by IV&V regarding the environment, requirements, design, and implementation. IV&V constructs an independent network of claims based on an independent decomposition of arguments. Based upon the risk posture of these claims and their associated software and software artifacts, IV&V then develops and executes engineering analyses and testing, which provide evidence to either support or refute the claim. This emerging risk-informed assurance case methodology is being put into practice as IV&V plans for support of the Artemis II mission, the first flight of the Orion capsule and Space Launch System with astronauts on board.

Whitman, Gerek↗
NASA NTRS2010

The Application of Modeling and Simulation in Capacity Management within the ITIL Framework

Tightly integrating modeling and simulation techniques into Information Technology Infrastructure Library (ITIL) practices can be one of the driving factors behind a successful and cost-effective capacity management effort for any Information Technology (IT) system. ITIL is a best practices framework for managing IT infrastructure, development and operations. Translating ITIL theory into operational reality can be a challenge. This paper aims to highlight how to best integrate modeling and simulation into an ITIL implementation. For cases where the project team initially has difficulty gaining consensus on investing in modeling and simulation resources, a clear definition for M&S implementation into the ITIL framework, specifically its role in supporting Capacity Management, is critical to gaining the support required to garner these resources. This implementation should also help to clearly define M&S support to the overall system mission. This paper will describe the development of an integrated modeling approach and how best to tie M&S to definitive goals for evaluating system capacity and performance requirements. Specifically the paper will discuss best practices for implementing modeling and simulation into ITIL. These practices hinge on implementing integrated M&S methods that 1) encompass at least two or more predictive modeling techniques, 2) complement each one's respective strengths and weaknesses to support the validation of predicted results, and 3) are tied to the system's performance and workload monitoring efforts. How to structure two forms of modeling: statistical and simUlation in the development of "As Is" and "To Be" efforts will be used to exemplify the integrated M&S methods. The paper will show how these methods can better support the project's overall capacity management efforts.

Rahmani, Sonya↗
NASA NTRS1972

Remote sensing of wet lands in irrigated areas

The use of airborne remote sensing techniques to: (1) detect drainage problem areas, (2) delineate the problem in terms of areal extent, depth to the water table, and presence of excessive salinity, and (3) evaluate the effectiveness of existing subsurface drainage facilities, is discussed. Experimental results show that remote sensing, as demonstrated in this study and as presently constituted and priced, does not represent a practical alternative as a management tool to presently used visual and conventional photographic methods in the systematic and repetitive detection and delineation of wetlands.

Ham, H. H.↗
NASA NTRS1973

Apollo experience report: Reliability and quality assurance

The reliability of the Apollo spacecraft resulted from the application of proven reliability and quality techniques and from sound management, engineering, and manufacturing practices. Continual assessment of these techniques and practices was made during the program, and, when deficiencies were detected, adjustments were made and the deficiencies were effectively corrected. The most significant practices, deficiencies, adjustments, and experiences during the Apollo Program are described in this report. These experiences can be helpful in establishing an effective base on which to structure an efficient reliability and quality assurance effort for future space-flight programs.

Sperber, K. P.↗
NASA NTRS1975

Geological applications of LANDSAT-1 imagery to the Great Salt Lake area

The ERTS program has been designed as a research and development tool to demonstrate that remote sensing from orbital altitudes is a feasible and practical approach to efficient management of earth resources. From this synoptic view and repetitive coverage provided by ERTS imagery of the Great Salt Lake area, large geological and structural features, trends, and patterns have been identified and mapped. A comparative analysis of lineaments observed in September and December data was conducted, existing mineral locations were plotted, and areas considered prospective for mineralization based on apparent structure-mineralization relationships were defined. The additional information obtained using ERTS data provides an added source of information to aid in the development of more effective mineral exploration programs.

Anderson, A. T.↗
NASA NTRS1975

Annual report to the NASA Administrator by the Aerospace Safety Advisory Panel. Part 2: Space shuttle program. Section 1: Observations and conclusions

The NASA and contractor management systems, including policies, practices, and procedures for the development of critical systems, subsystems and integration of the program elements, were investigated. The technical development status of critical systems, subsystems, and interfaces is presented. Space shuttle elements were qualified as to potential risks and hazards. The elements included the orbiter, external tanks, main engine, solid rocket boosters, and the ground support facilities.

Source record↗
NASA NTRS1988

Snow cover, snowmelt and runoff in the Himalayan River basins

Not withstanding the seasonal vagaries of both rainfall amount and snowcover extent, the Himalayan rivers retain their basic perennial character. However, it is the component of snowmelt yield that accounts for some 60 to 70 percent of the total annual flow volumes from Hamilayan watersheds. On this large hydropotential predominantly depends the temporal performance of hydropower generation and major irrigation projects. The large scale effects of Himalayan snowcover on the hydrologic responses of a few selected catchments in western Himalayas was studied. The antecedent effects of snowcover area on long and short term meltwater yields can best be analyzed by developing appropriate hydrologic models forecasting the pattern of snowmelt as a function of variations in snowcover area. It is hoped that these models would be of practical value in the management of water resources. The predictability of meltwater for the entire snowmelt season was studied, as was the concurrent flow variation in adjacent watersheds, and their hydrologic significance. And the applicability of the Snowmelt-Runoff Model for real time forecast of daily discharges during the major part of the snowmelt season is examined.

Dey, B.↗
NASA NTRS1988

Radar scattering and soil moisture

Research is being conducted on microwave scattering from vegetation. The objective is to develop techniques for measuring parameters of the vegetation canopy (such as biomass) needed for understanding global biogeochemical cycles and to develop techniques for correcting microwave measurements of soil moisture for the effects of the vegetation canopy. Measurements of vegetation and soil moisture are important for understanding the environment on a global scale. For example, moisture in the soil is an important, highly variable, element in the global hydrologic cycle. The hydrologic cycle, in turn, is strongly coupled to weather and climate through moisture (and energy) fluxes at the surface. The amount and distribution of vegetation is an important element in biogeochemical cycles; and knowledge of both the vegetation canopy and soil moisture is of practical importance in agricultural management. These theories are examined.

Levine, D. M.↗
NASA NTRS1992

The Global Ocean Observing System

A Global Ocean Observing System (GOOS) should be established now with international coordination (1) to address issues of global change, (2) to implement operational ENSO forecasts, (3) to provide the data required to apply global ocean circulation models, and (4) to extract the greatest value from the one billion dollar investment over the next ten years in ocean remote sensing by the world's space agencies. The objectives of GOOS will focus on climatic and oceanic predictions, on assessing coastal pollution, and in determining the sustainability of living marine resources and ecosystems. GOOS will be a complete system including satellite observations, in situ observations, numerical modeling of ocean processes, and data exchange and management. A series of practical and economic benefits will be derived from the information generated by GOOS. In addition to the marine science community, these benefits will be realized by the energy industries of the world, and by the world's fisheries. The basic oceanic variables that are required to meet the oceanic and predictability objectives of GOOS include wind velocity over the ocean, sea surface temperature and salinity, oceanic profiles of temperature and salinity, surface current, sea level, the extent and thickness of sea ice, the partial pressure of CO2 in surface waters, and the chlorophyll concentration of surface waters. Ocean circulation models and coupled ocean-atmosphere models can be used to evaluate observing system design, to assimilate diverse data sets from in situ and remotely sensed observations, and ultimately to predict future states of the system. The volume of ocean data will increase enormously over the next decade as new satellite systems are launched and as complementary in situ measuring systems are deployed. These data must be transmitted, quality controlled, exchanged, analyzed, and archived with the best state-of-the-art computational methods.

Kester, Dana↗
NASA NTRS1994

Re-engineering NASA's space communications to remain viable in a constrained fiscal environment

Along with the Red and Blue Teams commissioned by the NASA Administrator in 1992, NASA's Associate Administrator for Space Communications commissioned a Blue Team to review the Office of Space Communications (Code O) Core Program and determine how the program could be conducted faster, better, and cheaper. Since there was no corresponding Red Team for the Code O Blue Team, the Blue Team assumed a Red Team independent attitude and challenged the status quo, including current work processes, functional distinctions, interfaces, and information flow, as well as traditional management and system development practices. The Blue Team's unconstrained, non-parochial, and imaginative look at NASA's space communications program produced a simplified representation of the space communications infrastructure that transcends organizational and functional boundaries, in addition to existing systems and facilities. Further, the Blue Team adapted the 'faster, better, cheaper' charter to be relevant to the multi-mission, continuous nature of the space communications program and to serve as a gauge for improving customer services concurrent with achieving more efficient operations and infrastructure life cycle economies. This simplified representation, together with the adapted metrics, offers a future view and process model for reengineering NASA's space communications to remain viable in a constrained fiscal environment. Code O remains firm in its commitment to improve productivity, effectiveness, and efficiency. In October 1992, the Associate Administrator reconstituted the Blue Team as the Code O Success Team (COST) to serve as a catalyst for change. In this paper, the COST presents the chronicle and significance of the simplified representation and adapted metrics, and their application during the FY 1993-1994 activities.

Hornstein, Rhoda Shaller↗