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NASA NTRS2004

Global Failure Modes in Composite Structures for High Altitudes

This report summarizes the accomplishments under the referenced grant. The work described was started under the guidance and supervision of the late Dr. James Stames as the technical contact. It was aimed at investigating the development of analysis tools to deal with the problem of rupture in reinforced structural skin of future composites-based aircraft. It was of particular interest to assess methods by which failure features reminiscent of cracks in metallic structures would develop and propagate in fiber reinforced structures in interaction with the reinforcing frame. To eventually achieve that goal it was necessary to first understand the stress or strain distribution at the front of such features so that interactions between such features and reinforcing agents could be assessed computationally. Thus the major emphasis here was on the assessment of damage front and methods on how to assess or characterize it. During the conduct of this research program Dr. Stames changed to a different NASA- internal assignment, which divorced him of the direct supervision of this grant. A student who was approximately % into the completion of his Ph.D. research needed to finish this work, and NASA funds were made available under Dr. Damodar Ambur, the successor Branch Manager for Dr. James Starnes, for the completion of this work. The current grant was the thus a new and fmal support increment for completion of the started research. Final reports for previous funding have been completed and submitted. Because of the interconnection of this last phase of the investigation with previous work it is deemed useful to make the Ph.D. thesis by Luis Gonzales the body of this report.

Knauss, W. G.↗
NASA NTRS2004

Education, Technology, and Media: A Peak into My Summer Internship at NASA Glenn Research Center in Cleveland, Ohio

My name is James Moon and I am a senor at Tennessee State University where my major is Aeronautical and Industrial Technology with a concentration in industrial electronics. I am currently serving my internship in the Engineering and Technical Services Directorate at the Glenn Research Center (GRC). The Engineering and Technical Service Directorate provides the services and infrastructure for the Glenn Research Center to take research concepts to reality. They provide a full range of integrated services including engineering, advanced prototyping and testing, facility management, and information technology for NASA, industry, and academia. Engineering and Technical Services contains the core knowledge in Information Technology (IT). This includes data systems and analysis, inter and intranet based systems design and data security. Including the design and development of embedded real-time sohare applications for flight and supporting ground systems, Engineering and Technical Services provide a wide range of IT services and products specific to the Glenn Research Center research and engineering community.

Moon, James↗
NASA NTRS2007

Lessons Learned from Seven Space Shuttle Missions

Much can be learned from well-written descriptions of the technical and organizational factors that lead to an accident. Subsequent analysis by third parties of investigation reports and associated evidence collected during the investigations can lead to additional insight. Much can also be learned from documented close calls that do not result in loss of life or a spacecraft, such as the Mars Exploration Rover Spirit software anomaly, the SOHO mission interruption, and the NEAR burn anomaly. Seven space shuttle incidents fall into the latter category: Rendezvous Target Failure On STS-41B; Rendezvous Radar Anomaly and Trajectory Dispersion-STS-32 ;Rendezvous Lambert Targeting Anomaly-STS-49; Rendezvous Lambert Targeting Anomaly-STS-51; Zero Doppler Steering Maneuver Anomaly-STS-59; Excessive Propellant Consumption During Rendezvous-STS-69; Global Positioning System Receiver and Associated Shuttle Flight Software Anomalies-STS-91 Procedural work-arounds or software changes prevented them from threatening mission success. Extensive investigations, which included the independent recreation of the anomalies by multiple Shuttle Program organizations, were the key to determining the cause, accurately assessing risk, and identifying software and software process improvements. Lessons learned from these incidents not only validated long-standing operational best practices, but serve to promote discussion and mentoring among Program personnel and are applicable to future space flight programs.

Goodman, John↗
NASA NTRS2013

Microbiological Sampling Methods and Sanitation of Edible Plants Grown on ISS

Pathogenic microbes on the surfaces of salad crops and growth chambers pose a threat to the health of crew on International Space Station. For astronauts to safely consume spacegrown vegetables produced in NASA's new vegetable production unit, VEGGIE, three technical challenges must be overcome: real-time sampling, microbiological analysis, and sanitation. Raphanus sativus cultivar Cherry Bomb II and Latuca sativa cultivar Outredgeous, two saled crops to be grown in VEGGIE, were inoculated with Salmonella enterica serovar Typhimurium (S. Typhimurium), a bacterium known to cause food-borne illness~ Tape- and swab-based sampling techniques were optimized for use in microgravity and assessed for effectiveness in recovery of bacteria from crop surfaces: Rapid pathogen detection and molecular analyses were performed via quantitative real-time polymerase chain reactiop using LightCycler® 480 and RAZOR® EX, a scaled-down instrument that is undergoing evaluation and testing for future flight hardware. These methods were compared with conventional, culture-based methods for the recovery of S. Typhimurium colonies. A sterile wipe saturated with a citric acid-based, food-grade sanitizer was applied to two different surface materials used in VEGGIE flight hardware that had been contaminated with the bacterium Pseudomonas aeruginosa,. another known human pathogen. To sanitize surfaces, wipes were saturated with either the sanitizer or sterile deionized water and applied to each surface. Colony forming units of P. aeruginosa grown on tryptic soy agar plates were enumerated from surface samples after sanitization treatments. Depending on the VEGGIE hardware material, 2- to 4.5-log10 reductions in colony-forming units were observed after sanitization. The difference in recovery of S. Typhimurium between tape- and swab- based sampling techniques was insignificant. RAZOR® EX rapidly detected S. Typhimurium present in both raw culture and extracted DNA samples.

Parrish, Charles H. II↗
NASA NTRS2015

Defining Operational Space Suit Requirements for Commercial Orbital Spaceflight

As the commercial spaceflight industry transitions from suborbital brevity to orbital outposts, spacewalking will become a major consideration for tourists, scientists, and hardware providers. The challenge exists to develop a space suit designed for the orbital commercial spaceflight industry. The unique needs and requirements of this industry will drive space suit designs and costs that are unlike any existing product. Commercial space tourists will pay for the experience of a lifetime, while scientists may not be able to rely on robotics for all operations and external hardware repairs. This study was aimed at defining space suit operational and functional needs across the spectrum of spacewalk elements, identifying technical design drivers and establishing appropriate options. Recommendations from the analysis are offered for consideration

Alpert, Brian K.↗
NASA NTRS2019

eddy Users Manual

eddy is a collection of tools - nonlinear solvers, meshing, post-processing, visualization, optimization, etc. - for performing scale-resolving simulations of multi-physics applications. The framework is designed to enable advanced R&D on a variety of topics by leveraging a mature capability for scale resolving simulations, and simultaneously be an appropriate tool for application analysis and support. Currently, eddy is at a relatively low technical readiness level (TRL), and users and developers should maintain appropriate expectations. The technical details behind eddy are outlined in several publications which can be consulted for more information [1–10]. The solvers are built around an unstructured high-order capability, and heavily utilize the tensor product sum-factorization approach for efficiency. The unsteady formulation utilizes a fully implicit space-time approach with a matrix-free Newton- Krylov method. A primitive steady-state solver is available for testing purposes, but is not expected to converge for all but simple verification cases. The Navier-Stokes fluid solvers do not support either RANS or hybrid-RANS capability, only LES and wall-modeled LES approaches. All of the solvers within eddy support three modes of operation: a primal solve of the full nonlinear problem, and two linearization approaches of the primal solve - the ad joint and the tangent solution. Details on how to select and use these three modes are outlined in Sec. 3.

Murman, Scott M.↗
NASA NTRS1999

Remembering Apollo 11: The 30th Anniversary Data Archive CD-ROM

On July 20, 1969, the human race accomplished its single greatest technological achievement of all time when a human first set foot on another celestial body. Six hours after landing at 4:17 p.m. Eastern Standard Time (with less than thirty seconds of fuel remaining), Neil A. Armstrong took the "small step" into our greater future when he stepped off the Lunar Module, named Eagle, onto the surface of the Moon, from which he could look up and see Earth in the heavens as no one had done before him. He was shortly joined by Edwin "Buzz" Aldrin, and the two astronauts spent twenty-one hours on the lunar surface and returned forty-six pounds of lunar rocks. After their historic walks on the Moon, they successfully docked with Michael Collins, patiently orbiting the cold but no longer lifeless Moon alone in the Command module Columbia. This CR-ROM is intended as a collection of hard to find technical data and other interesting information about the Apollo 11 mission, as well as the apollo program in general. It includes basic overviews, such as a retrospective analysis, an annotated bibliography, and history of the lunar-orbit rendezvous concept. It also contains technical data, such as mission operations reports, press kits, and news references for all of the Apollo missions, the Apollo spacecraft, and the Saturn V launch vehicle. Rounding out this CD-ROM are extensive histories of the lunar Orbiter program (the robotic predecessor to Apollo, biographies of the Apollo astronauts and other key individuals, and interesting audio-visual materials, such as video and audio clips, photo galleries, and blueprint-like diagrams of the Apollo spacecraft.

Edgar M Cortright↗
NASA NTRS1983

Interactive analysis of a large aperture Earth observations satellite

A system level design and analysis has been conducted on an Earth Observation Satellite (EOS) system using the Interactive Design and Evaluation of Advanced Spacecraft (IDEAS) computer-aided design and analysis program. The IDEAS program consists of about 40 user-friendly technical modules and an interactive graphics display. The reflector support system and feed mast of the EOS spacecraft are constructed with box-truss structural concept, a lattice configuration which can be packaged for delivery in a single Shuttle flight and deployed in orbit. The deployed spacecraft consists of a 120-m by 60-m parabolic focal axis. The spacecraft was modeled for structural, thermal, and control systems analysis and structural elements were designed. On-orbit dynamic and thermal loading analyses were conducted; spacecraft weights and developmental and first unit costs were determined.

Wright, R. L.↗
NASA NTRSDate not supplied

An Interim Set of TNT Curves for LOX/LNG Explosions

Space launch companies are actively developing, or in some cases have already developed, new vehicles that use large quantities of liquid oxygen (LOX) and liquid natural gas (LNG) propellants. This propellant mixture currently lacks sound/verified LOX/LNG explosive safety standards for use in explosive siting and flight safety analysis. In the U.S., multiple government and commercial organizations have conducted limited testing, or are preparing to conduct tests, intended to provide a sound technical basis for explosive standards applicable to space launch vehicle ground and flight operations. The limited testing and analysis conducted so far indicates the potential to produce extremely energetic explosions due to the miscibility of LOX/LNG (methane), a unique feature relative to previously used propellant mixtures such as hydrogen/oxygen or kerosene/oxygen. MOX is a homogenous mixture of methane and oxygen that is possible because of methane’s 100% miscibility in LOX. Preliminary testing by N2L, Inc. described in this paper shows that it is possible to form MOX with a variety of mixing methods, and that MOX is a sensitive high explosive capable of producing overpressures greater than comparable masses of C-4. The limited amount and quality of large-scale LOX/LNG explosive test data, and the potential formation of high-explosive MOX, create significant unknowns in the determination of hazard areas (cleared of the public) for ground operations (such as a static fire test) and flights of launch vehicles with LOX/LNG propellant. This paper will review the methods used by NASA to develop and validate the LOX/LH2 blast model using large-scale explosive test programs such as Project PYRO, the Hydrogen-Oxygen Vertical Impact (HOVI), and the Large-Scale Hydrogen-Oxygen Explosion (LSHOE) tests. This paper will describe how the same process was used to prepare an interim LOX/LNG blast model that applies to various LV failure scenarios and conservatively accounts for potential MOX formation. This paper also summarizes past and future testing and modeling efforts funded by a consortium of NASA, the US Federal Aviation Administration (FAA), and the US Space Force (USSF). These test programs should be concluded within the next 3 years and are intended to provide empirical data for model verification and validation.

Liquid Oxygen↗
NASA NTRS1983

Compiling the space shuttle wind tunnel data base: An exercise in technical and managerial innovators

Engineers evaluating Space Shuttle flight data and performance results are using a massive data base of wind tunnel test data. A wind tunnel test data base of the magnitude attained is a major accomplishment. The Apollo program spawned an automated wind tunnel data analysis system called SADSAC developed by the Chrysler Space Division. An improved version of this system renamed DATAMAN was used by Chrysler to document analyzed wind tunnel data and data bank the test data in standardized formats. These analysis documents, associated computer graphics and standard formatted data were disseminated nationwide to the Shuttle technical community. These outputs became the basis for substantiating and certifying the flight worthiness of the Space Shuttle and for improving future designs. As an aid to future programs this paper documents the lessons learned in compiling the massive wind tunnel test data base for developing the Space Shuttle. In particular, innovative managerial and technical concepts evolved in the course of conceiving and developing this successful DATAMAN system and the methods and organization for applying the system are presented.

Kemp, N. D.↗
NASA NTRS2021

NASA Engineering and Safety Center Technical Bulletin No. 21-03-1: Best Practices for the Elemental Profiling of High-Purity Hydrazine

Trace contaminants in high-purity hydrazine (HPH) propellant impact a wide variety of commercial, Department of Defense (DoD), and NASA missions. Depending on thruster design, elemental contaminants must be kept at extremely low levels and are verified as such by routine analysis. A number of these contaminants have recently undergone an assessment to shed light on their quantities present following changes in the HPH supply chain. A round robin analysis utilizing four separate laboratories resulted in unacceptably high variability in the quantification of these contaminants. The principal objective of this technical bulletin is to signal the availability of a new analysis methodology which yields accurate and repeatable quantification by providing best practices for both quantitation methodology and strategies for avoiding sample contamination during analysis.

High-Purity Hydrazine↗
NASA NTRS2014

A Framework for Assessment of Aviation Safety Technology Portfolios

The programs within NASA's Aeronautics Research Mission Directorate (ARMD) conduct research and development to improve the national air transportation system so that Americans can travel as safely as possible. NASA aviation safety systems analysis personnel support various levels of ARMD management in their fulfillment of system analysis and technology prioritization as defined in the agency's program and project requirements. This paper provides a framework for the assessment of aviation safety research and technology portfolios that includes metrics such as projected impact on current and future safety, technical development risk and implementation risk. The paper also contains methods for presenting portfolio analysis and aviation safety Bayesian Belief Network (BBN) output results to management using bubble charts and quantitative decision analysis techniques.

Jones, Sharon M.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS1973

Adaptive on-line classification of multi-spectral scanner data

A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

Fromm, F. R.↗