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NASA NTRS2024

Lessons Learned from Particulate Characterization Laboratory Anomalies

The White Sands Test Facility chemistry laboratory provides quality control for cleanroom operations including cleanliness verification of aerospace hardware by particulate counts and non-volatile residue determinations, particulate counts for liquid hypergolic propellants, gaseous helium and nitrogen propellant pressurizing agents used for ground support equipment and flight test article valve actuation, gaseous oxygen primarily used for component testing, and deionized water for refurbished propellant hardware flushing. Cleanliness verification includes particulate counts and non-volatile residue determinations to industry standard, NASA, and program specifications and levels. Particulate counts are typically to customer-specified specifications and levels including JPR 5322.1H (2016) Levels 50 and 100, Orion (MPCV 70156. Revision H (2018)) Level 100, RPTSTD-8070-0001 Revision 3 (2022), and IEST-STD-CC1246E (2013) Levels 50 and 100. The laboratory issues high pressure filter holders containing membrane filters to test operations personnel, who collect samples by flowing the required volumes of fluid through the filter holder, and the filter holder is returned to the lab for counting. A passing particulate count is required before testing may proceed. Rapid data reduction and issuing of reports indicating a pass or fail of the particulate specification are required. Corrective action and resampling invariably occurs if a sample fails. Consequently, the laboratory must maintain the highest degree of reliability to facilitate quality data used to decide if testing may proceed. Experience and continual improvements have enabled reliability. However, anomalies attributed to lab processes and hardware including filter holders, membranes, and Petri dishes have been encountered. This paper presents a summary of problems, solutions, successes, and lessons learned from particle counting experience for over 35 years.

Lessons Learned↗

NASA NTRSDate not supplied

From Regolith to Living Off the Land: Formulating a Data Model to Catalog Lunar Construction Materials

Artemis Program objectives for sustainable, long-term presence on the Moon and more distant planetary surfaces will require learning to “Live off the Land”, relying on in-situ resource utilization to produce infrastructure and building materials from lunar regolith, icy subsurface deposits, and residual waste materials. Meeting demand for consumables while scaling development with resources found within the landing zone will require detailed data on the geology and environment of the lunar surface. Lunar infrastructure development will generate vast amounts of new engineering data regarding availability of processed feedstocks and their performance in building materials. Lunar engineering data accessible to program partners, research institutions and industry may help situate processes and specifications within the in-situ GIS context. Lunar missions to date have generated geological and ice favorability maps of the lunar surface, and recent technology studies have tested automated construction systems and novel material formulations using regolith simulants and binders. Current discussions focus on identifying key feedstocks, quantities required for nominal mission scenarios and infrastructure plans, and mapping the value chain from regolith to feedstock to consumables and construction materials.

lunar construction↗

NASA NTRSDate not supplied

From Regolith to Living Off the Land: Formulating a Data Model to Catalog Lunar Construction Materials

lunar construction↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗

NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗

NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗

NASA NTRS2020

Global Aerodynamic Modeling Using Automated Local Model Networks in Real Time

A novel method is presented for automated real-time global aerodynamic modeling using local model networks, known as Smoothed Partitioning with Localized Trees in Real Time (SPLITR), as part of NASA’s Learn-to-Fly technology development initiative. The global nonlinear aerodynamics are partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global flight envelope and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local aerodynamics in each region, so that it can be updated independently with non-contiguous data in the range of each cell over time. A weighted superposition of these piecewise local models across the flight envelope forms a global nonlinear model that also accurately captures the local aerodynamics. The SPLITR approach is demonstrated using both simulation and flight data, and the results are analyzed in terms of model predictive capabilities as well as interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear aerodynamic behavior, produce an accurate model, and provide valuable physical insight into the local and global aerodynamics.

Weinstein, Rose↗

NASA NTRS2023

Gateway Program Safety and Mission Assurance Integration - The Future of Safe Deep Space Human Exploration

As a foundational element of the National Aeronautics and Space Administration (NASA) Artemis Campaign, the Gateway is an incrementally built cislunar spacecraft that will serve as a platform for deep space human exploration, science, and technology demonstration. The Gateway will be a unifying catalyst for partners around the world to establish sustained deep space scientific investigations, lunar surface access, and missions to Mars. As human exploration moves farther from Earth, spacecraft designs must optimize mass and volume while minimizing human and spacecraft risk. To accomplish this, the Gateway Program Safety and Mission Assurance (S&MA) office develops, implements, and ensures compliance with requirements, in concert with the accurate characterization and transparent communication of residual hazard risks, for integrated safety, reliability and maintainability, and quality assurance. This paper will detail the evolution of the Gateway Program S&MA integration functions, and provide its current status and lessons learned for future human spaceflight programs.

Helen Vaccaro↗

NASA NTRS1992

Composite fuselage shell structures research at NASA Langley Research Center

Fuselage structures for transport aircraft represent a significant percentage of both the weight and the cost of these aircraft primary structures. Composite materials offer the potential for reducing both the weight and the cost of transport fuselage structures, but only limited studies of the response and failure of composite fuselage structures have been conducted for transport aircraft. The behavior of these important primary structures must be understood, and the structural mechanics methodology for analyzing and designing these complex stiffened shell structures must be validated in the laboratory. The effects of local gradients and discontinuities on fuselage shell behavior and the effects of local damage on pressure containment must be thoroughly understood before composite fuselage structures can be used for commercial aircraft. This paper describes the research being conducted and planned at NASA LaRC to help understand the critical behavior or composite fuselage structures and to validate the structural mechanics methodology being developed for stiffened composite fuselage shell structure subjected to combined internal pressure and mechanical loads. Stiffened shell and curved stiffened panel designs are currently being developed and analyzed, and these designs will be fabricated and then tested at Langley to study critical fuselage shell behavior and to validate structural analysis and design methodology. The research includes studies of the effects of combined internal pressure and mechanical loads on nonlinear stiffened panel and shell behavior, the effects of cutouts and other gradient-producing discontinuities on composite shell response, and the effects of local damage on pressure containment and residual strength. Scaling laws are being developed that relate full-scale and subscale behavior of composite fuselage shells. Failure mechanisms are being identified and advanced designs will be developed based on what is learned from early results from the LaRC research activities. Results from combined load tests will be used to validate analytical models of critical nonlinear response mechanisms as well as shell scaling laws.

Starnes, James H., Jr.↗

NASA NTRS2009

NASA Composite Materials Development: Lessons Learned and Future Challenges

Composite materials have emerged as the materials of choice for increasing the performance and reducing the weight and cost of military, general aviation, and transport aircraft and space launch vehicles. Major advancements have been made in the ability to design, fabricate, and analyze large complex aerospace structures. The recent efforts by Boeing and Airbus to incorporate composite into primary load carrying structures of large commercial transports and to certify the airworthiness of these structures is evidence of the significant advancements made in understanding and use of these materials in real world aircraft. NASA has been engaged in research on composites since the late 1960 s and has worked to address many development issues with these materials in an effort to ensure safety, improve performance, and improve affordability of air travel for the public good. This research has ranged from synthesis of advanced resin chemistries to development of mathematical analyses tools to reliably predict the response of built-up structures under combined load conditions. The lessons learned from this research are highlighted with specific examples to illustrate the problems encountered and solutions to these problems. Examples include specific technologies related to environmental effects, processing science, fabrication technologies, nondestructive inspection, damage tolerance, micromechanics, structural mechanics, and residual life prediction. The current state of the technology is reviewed and key issues requiring additional research identified. Also, grand challenges to be solved for expanded use of composites in aero structures are identified.

Tenney, Darrel R.↗

NASA NTRS2020

The Spaceport Command and Control System Security Assessor Project

This Summer, I worked as a National Aeronautics and Space Administration (NASA) Internships and Fellowships (NIF) intern under my mentor, Jill Giles within the Software Engineering Branch. Within this project, I worked alongside the Cyber Security branch to identify a list of Commercial Off the Shelf (COTS) software to analyze, research, and gain insight about potential vulnerabilities within the software that could become a threat of attack. After identifying the list of COTS software, my team and I used Microsoft Excel to create a worksheet to easily organize and design a questionnaire about the software. Security reports weregiven to us to identify the software used on the machines in the firing rooms. With these reports, we created a script that would populate the database with the software information to identify potential security weaknesses of COTS software.The goal of the project was to produce a final report, summarizing the most vulnerable launch control system servers and configurations and document vulnerabilities, residual risk, likelihood, and consequence. This project is important for the Cyber Security and Information Technology branches because it will identify security weaknesses and help to mitigate risk. From the Spaceport Command and Control System Security Assessor Project, I learned how to properly identify weaknesses and vulnerabilities within software and how to mitigate the risks within the software. This project also taught me how to create databases using scripts and input files.

Destani Satora Van Arsdalen↗

NASA NTRS2017

Oxygen Compatibility and Challenge Testing of the PLSS Variable Oxygen Regulator (VOR) for the Advanced EMU

The Variable Oxygen Regulator (VOR), a stepper actuated two-stage mechanical regulator, is being developed for the purpose of serving as the Primary Oxygen Regulator (POR) and Secondary Oxygen Regulator (SOR) within the Advanced EMU PLSS, now referred to as the xEMU and xPLSS. Three prototype designs have been fabricated and tested as part of this development. Building upon the lessons learned from the 35 years of Shuttle/ISS EMU Program operation including the fleet-wide EMU Secondary Oxygen Pack (SOP) contamination failure that occurred in 2000, the VOR is being analyzed, designed, and tested for oxygen compatibility with controlled Non-Volatile Residue (NVR) and a representative worst-case hydro-carbon system contamination event (>100mg/sq ft dodecane). This paper discusses the steps taken in testing of VOR 2.0 with for oxygen compatibility and then discusses follow-on design changes implemented in the VOR 3.0 (3rd prototype) as a result.

Campbell, Colin↗

NASA NTRS1985

Transport of trace species deduced from LIMS observations

One of the critical problems in the middle atmosphere at this time is understanding the interaction between atmospheric motions and photochemistry, and the way in which these two processes maintain the distributions of photochemically active species such as ozone. Recent work has shown that a great deal can be learned by considering the zonally averaged distributions and transports, i.e., working in the latitude-altitude plane. This 2-dimensional approach has been used in the past in Eulerian formation. Recent work (DUNKERTON, 1978) has shown that formulating the transport problems in terms of the residual mean circulation, which has similarity to the Lagrangian mean motion, results in a more physical picture of the way in which the transport takes place. Here, data from the Limb Infrared Monitor of the Stratosphere (LIMB) experiment are used to investigate how well the residual mean formulation works with real data, in the sense of asking how well the predicted ozone changes match those in regions of the lower stratosphere in which chemical changes may be neglected. This allows a test of the approximations that are made, and of the strengths and weaknesses of this approach to transport calculation.

Gille, J. C.↗

NASA NTRS2011

Markov Chain Monte Carlo Bayesian Learning for Neural Networks

Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

Goodrich, Michael S.↗

NASA NTRSDate not supplied

Idea Evolution ... What's Next?

No research project lasts forever. Even the most wildly successful programs will eventually come to an end. The challenge in that moment, when a project ends, is how to take what was learned in that project and apply it to the next research activity you undertake. Although not always possible, information and insights from a project that is underway or coming to a close can be leveraged to identify a new research direction and advance existing tangentially related activities. In this presentation, a common thread will be followed through a series of sequential research projects where lessons learned in each activity were built into the next enabling each new research project to advance further and faster. Initial research focused on lunar dust adhesion mitigation coalesced into two major contributors to adhesion interactions; which were, not surprisingly, surface chemistry and topography. This knowledge was applied to addressing insect residue adhesion mitigation on commercial aircraft leading edge surfaces. Composite epoxy coatings formulated with controlled surface chemistry and topography were evaluated and led to identification of additional relevant considerations: dynamics and surface morphology. Collectively, these considerations were applied to address impact ice adhesion mitigation where further properties were elucidated: surface mechanical properties and durability. Seeking commonality between these research endeavors led to greater understanding of each new research objective and ultimately, identification of robust, viable pathways toward meaningful results.

Adhesion Mitigation↗

NASA NTRS1999

Polar Layered Terrains: Links Between the Martian Volatile and Dust Cycles

The nature, origin and evolution of the polar layered terrains are a major mystery of Martian climate. Almost every aspect of these enigmatic terrains surrounding, and probably underlying, the polar residual ice caps is in contention. Are the polar laminae forming today? Are they inactive or even eroding, being mere relics of the past? Are the north and south polar layered terrains fundamentally different in composition, age or process? Are they a physical record of past, possibly cyclic, climate change and, if so, can we learn to read that record? We know from past Mariner 9 and Viking orbital observations that there are layered terrains at high latitudes, defined by alternating bands of visually lighter and darker material appearing in stacks hundreds of meters thick with individual bands as thin as could then be resolved (tens of meters). In this talk, aspects of the polar layered terrains will be reviewed with emphasis on issues likely to be addressed with data from ongoing and near-term flight missions to Mars. Particular attention will be given to what might be learned from observing the present seasonal cycles of dust, water and carbon dioxide on Mars, in conjunction with in situ data from one site on the south polar layered terrain.

Zurek, R. W.↗

NASA NTRS1981

Solar plages and the interpretation of stellar Ca 2 H and K line variations in late type dwarfs

Approximately a dozen late type dwarf stars have been observed by Wilson (1978) to undergo cyclic variability in Ca II H and K line emission which seems analogous to the solar activity cycle. What might be learned about these stars from solar analogies? The Ca II K index is estimated variation of the Sun viewed as a star, and compared with the observed range of Wilson's stellar observations. Results indicate trends of increasing relative variation H-K(max)/H-K(min) with later spectral type, due to decreasing dilutional contribution of residual photospheric flux to a 1 A band at line center, and of increasing relative variation with decreasing relative time of rise to maximum tau rise/tau reminiscent of the observed solar correlation of a quick rise to sunspot maximum with a strong cycle.

Cook, J. W.↗

NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗