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NASA NTRS2015

Characterization of an In-Situ Ground Terminal via a Geostationary Satellite

In 2015, the Space Communications and Navigation (SCaN) Testbed project completed an S-Band ground station located at the NASA Glenn Research Center in Cleveland, Ohio. This S-Band ground station was developed to create a fully characterized and controllable dynamic link environment when testing novel communication techniques for Software Defined Radios and Cognitive Communication Systems. In order to provide a useful environment for potential experimenters, it was necessary to characterize various RF devices at both the component level in the laboratory and at the system level after integration. This paper will discuss some of the laboratory testing of the ground station components, with a particular focus emphasis on the near-field measurements of the antenna. It will then describe the methodology for characterizing the installed ground station at the system level via a Tracking and Data Relay Satellite (TDRS), with specific focus given to the characterization of the ground station antenna pattern, where the max TDRS transmit power limited the validity of the non-noise floor received power data to the antenna main lobe region. Finally, the paper compares the results of each test as well as provides lessons learned from this type of testing methodology.

Radiation Pattern Analysis↗

NASA NTRS1992

Long Duration Exposure Facility (LDEF) optical systems SIG summary and database

The main objectives of the Long Duration Exposure Facility (LDEF) Optical Systems Special Investigative Group (SIG) Discipline are to develop a database of experimental findings on LDEF optical systems and elements hardware, and provide an optical system overview. Unlike the electrical and mechanical disciplines, the optics effort relies primarily on the testing of hardware at the various principal investigator's laboratories, since minimal testing of optical hardware was done at Boeing. This is because all space-exposed optics hardware are part of other individual experiments. At this time, all optical systems and elements testing by experiment investigator teams is not complete, and in some cases has hardly begun. Most experiment results to date, document observations and measurements that 'show what happened'. Still to come from many principal investigators is a critical analysis to explain 'why it happened' and future design implications. The original optical system related concerns and the lessons learned at a preliminary stage in the Optical Systems Investigations are summarized. The design of the Optical Experiments Database and how to acquire and use the database to review the LDEF results are described.

Bohnhoff-Hlavacek, Gail↗

NASA NTRS1998

Plants and somatic embryos in space: what have we learned?

Space provides a unique environment that can affect the interplay between cell cycle controls and environment and can thus modify the processes of cell division, development and growth. It is proposed that the chromosomal and nuclear abnormalities frequently encountered in cells of various plants exposed to space are due to a combination of factors including the biological status of the systems and the way in which they are grown, exposed to, and ultimately, the way in which they experience multiple stresses. The extent to which space-specific changes become manifest is dependent on the extent of pre-existing stresses in the system. This has become evident in a variety of plant species grown in space but has been particularly amenable to study using in vitro systems, especially in developing embryoids. The following observations allow us to harmonize disparate results from a variety of space experiments:- (a) the more completely developed a system, the less likely it is to show cell stress during growth; the less morphologically complex, the greater the vulnerability; (b) the size/"packaging" of the genome (karyotype) are significant experimental variables; plants with larger genomes (e.g. polyploids) seem to be more space-stress tolerant; (c) a single space-associated stress is inadequate to produce a significant adverse response unless the stress is severe, or a biological parameter necessary to 'amplify' it exists. On this view, an appropriate "stress match" with other non-equilibrium determinants, much like a 'tug of war', can result in genomic variations in space. All this emphasizes that fastidiously-controlled growing environments must be devised if one is to resolve the matter of direct versus indirect effects of space. Better understanding of the novel physico-chemical equilibrium phenomena associated with space will allow those interested in space cell and developmental biology to pick and choose procedures best suited to their exploitation for specific objectives.

Flight Experiment↗

NASA NTRS2002

Boosting with Averaged Weight Vectors

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. Some researchers have pointed out the intuition that it is probably better to construct a distribution that is orthogonal to the mistake vectors of all the previous base models, but that this is not always possible. We present an algorithm that attempts to come as close as possible to this goal in an efficient manner. We present experimental results demonstrating significant improvement over AdaBoost and the Totally Corrective boosting algorithm, which also attempts to satisfy this goal.

Oza, Nikunj C.↗

NASA NTRS1996

Overview of HATP Experimental Aerodynamics Data for the Baseline F/A-18 Configuration

Determining the baseline aerodynamics of the F/A-18 was one of the major objectives of the High-Angle-of-Attack Technology Program (HATP). This paper will review the key data bases that have contributed to our knowledge of the baseline aerodynamics and the improvements in test techniques that have resulted from the experimental program. Photographs are given highlighting the forebody and leading-edge-extension (LEX) vortices. Other data representing the impact of Mach and Reynolds numbers on the forebody and LEX vortices will also be detailed. The level of agreement between different tunnels and between tunnels and flight will be illustrated using pressures, forces, and moments measured on a 0.06-scale model tested in the Langley 7- by 10-Foot High Speed Tunnel, a 0.16-scale model in the Langley 30- by 60-Foot Tunnel, a full-scale vehicle in the Ames 80- by 120-Foot Wind Tunnel, and the flight F/A-18 High Alpha Research Vehicle (HARV). Next, creative use of wind tunnel resources that accelerated the validation of the computational fluid dynamics (CFD) codes will be described. Lastly, lessons learned, deliverables, and program conclusions are presented.

Hall, Robert M.↗

NASA NTRS2002

Measurement of Meteor Impact Experiments Using Three-Component Particle Image Velocimetry

The study of hypervelocity impacts has been aggressively pursued for more than 30 years at Ames as a way to simulate meteoritic impacts. Development of experimental methods coupled with new perspectives over this time has greatly improved the understanding of the basic physics and phenomenology of the impact process. These fundamental discoveries have led to novel methods for identifying impact craters and features in craters on both Earth and other planetary bodies. Work done at the Ames Vertical Gun Range led to the description of the mechanics of the Chicxualub crater (a.k.a. K-T crater) on the Yucatan Peninsula, widely considered to be the smoking gun impact that brought an end to the dinosaur era. This is the first attempt in the world to apply three-component particle image velocimetry (3-D PIV) to measure the trajectory of the entire ejecta curtain simultaneously with the fluid structure resulting from impact dynamics. The science learned in these experiments will build understanding in the entire impact process by simultaneously measuring both ejecta and atmospheric mechanics.

Heineck, James T.↗

NASA NTRS2015

Assessment of Slat Noise Predictions for 30P30N High-Lift Configuration From BANC-III Workshop

This paper presents a summary of the computational predictions and measurement data contributed to Category 7 of the 3rd AIAA Workshop on Benchmark Problems for Airframe Noise Computations (BANC-III), which was held in Atlanta, GA, on June 14-15, 2014. Category 7 represents the first slat-noise configuration to be investigated under the BANC series of workshops, namely, the 30P30N two-dimensional high-lift model (with a slat contour that was slightly modified to enable unsteady pressure measurements) at an angle of attack that is relevant to approach conditions. Originally developed for a CFD challenge workshop to assess computational fluid dynamics techniques for steady high-lift predictions, the 30P30N configurations has provided a valuable opportunity for the airframe noise community to collectively assess and advance the computational and experimental techniques for slat noise. The contributed solutions are compared with each other as well as with the initial measurements that became available just prior to the BANC-III Workshop. Specific features of a number of computational solutions on the finer grids compare reasonably well with the initial measurements from FSU and JAXA facilities and/or with each other. However, no single solution (or a subset of solutions) could be identified as clearly superior to the remaining solutions. Grid sensitivity studies presented by multiple BANC-III participants demonstrated a relatively consistent trend of reduced surface pressure fluctuations, higher levels of turbulent kinetic energy in the flow, and lower levels of both narrow band peaks and the broadband component of unsteady pressure spectra in the nearfield and farfield. The lessons learned from the BANC-III contributions have been used to identify improvements to the problem statement for future Category-7 investigations.

Choudhari, Meelan↗

NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.

Aircraft propulsion and power↗

NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Aircraft propulsion and power↗

NASA NTRSDate not supplied

Evaluating a Cognitive Extension for the Licklider Transmission Protocol in a Spacecraft Emulation Testbed

In space communications, particularly when involving regions beyond cislunar space, the development of advanced networking solutions is essential to address the challenges posed by limited connectivity, substantial propagation delays, and radio signal variations. This study explores a data-driven approach to the Licklider Transmission Protocol (LTP), specifically focusing on dynamically adjusting the maximum payload size of segments. Prior research has emphasized the potential benefits of dynamically adjusting this parameter, introducing the concept of Cognitive LTP. This paper presents a software implementation of Cognitive LTP (CLTP) within an open-source Delay Tolerant Networking (DTN) framework, specifically the High-rate Delay Tolerant Networking (HDTN), and experimentally evaluates its performance under realistic space conditions. Leveraging the Cognitive Ground Testbed (CGT), developed by NASA GRC for spacecraft communication emulation, this study effectively bridges the gap between theoretical advancements and practical applications. By thoroughly analyzing CLTP’s functionality within the CGT, this research offers insights into the practical implications of adaptive networking strategies, emphasizing the importance of conducting tests in relevant environments for the maturation of space communication technologies.

Delay Tolerant Networking↗

NASA NTRS1994

Catalog of Apollo experiment operations

This catalog reviews Apollo mission reports, preliminary science reports, technical crew debriefings, lunar surface operations plans, and various relevant lunar experiment documents, collecting engineering- and operation-specific information by experiment. It is organized by discrete experimental and equipment items emplaced or operated on the lunar surface or at zero gravity during the Apollo missions. It also attempts to summarize some of the general problems encountered on the surface and provides guidelines for the design of future lunar surface experiments with an eye toward operations. Many of the problems dealt with on the lunar surface originated from just a few novel conditions that manifested themselves in various nasty ways. Low gravity caused cables to stick up and get caught on feet, and also made it easy for instruments to tip over. Dust was a problem and caused abrasion, visibility, and thermal control difficulties. Operating in a pressure suit limited a person's activity, especially in the hands. I hope to capture with this document some of the lessons learned from the Apollo era to make the jobs of future astronauts, principle investigators, engineers, and operators of lunar experiments more productive.

Sullivan, Thomas A.↗

NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗

NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗

NASA NTRSDate not supplied

Flow Reconstruction in A Transonic Turbine Cascade Using Physics-Informed Neural Networks (PINNS)

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. The 2-D flow field in a transonic turbine cascade is reconstructed in three ways: the traditional forward approach (PINN not trained on experimental data), by training the PINN using discrete sets of experimentally measured pressure at midspan, and in the inverse sense where no inlet or outlet pressure boundary conditions are applied. Comparisons between the PINN solutions to measured data are made. This is repeated for three different turbine blades with distinct loading characteristics. Good agreement is shown between a CFD calculation of the CMC7 blade, and the PINN model trained with all data. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. The forward problem results deviate the most from experimental data but show promise. Solutions from the assisted training cases show that the PINN can reconstruct the flow field with acceptable accuracy when trained on measurements along the entire blade. In the inverse case, it is shown that to simultaneously achieve acceptable errors for inlet Mach number and outlet isentropic Mach number, the PINN must be trained on the static pressure data along the entire blade.

Machine Learning↗

NASA NTRS2018

X-57 Maxwell Battery from Cell Level to System Level Design and Testing

The X-57 Maxwell flight demonstrator aircraft is an experimental aircraft designed to demonstrate radically improved aircraft efficiency with a 3.5 times aero-propulsive efficiency gain at a "high-speed cruise" flight condition for comparable general aviation aircraft. These gains are enabled by integrating the design of a new, optimized wing and a new electric propulsion system. There are 14 propulsors in all: 12 high lift motor that are only active during takeoff and climb, and 2 larger motors positioned on the wingtips that operate over the entire mission. The innovative electric propulsion system will have as its primary power a Li-ion battery system. Integrating a battery system into this innovative design poses unique challenges that require careful design considerations across the system. The presentation will cover a breakout of X-57 battery specifications, battery design and lessons learned when designing a high voltage battery system to power electrified aircrafts.

Hernandez Lugo, Dionne↗

NASA NTRS1993

Link monitor and control operator assistant: A prototype demonstrating semiautomated monitor and control

This article describes the approach, results, and lessons learned from an applied research project demonstrating how artificial intelligence (AI) technology can be used to improve Deep Space Network operations. Configuring antenna and associated equipment necessary to support a communications link is a time-consuming process. The time spent configuring the equipment is essentially overhead and results in reduced time for actual mission support operations. The NASA Office of Space Communications (Code O) and the NASA Office of Advanced Concepts and Technology (Code C) jointly funded an applied research project to investigate technologies which can be used to reduce configuration time. This resulted in the development and application of AI-based automated operations technology in a prototype system, the Link Monitor and Control Operator Assistant (LMC OA). The LMC OA was tested over the course of three months in a parallel experimental mode on very long baseline interferometry (VLBI) operations at the Goldstone Deep Space Communications Center. The tests demonstrated a 44 percent reduction in pre-calibration time for a VLBI pass on the 70-m antenna. Currently, this technology is being developed further under Research and Technology Operating Plan (RTOP)-72 to demonstrate the applicability of the technology to operations in the entire Deep Space Network.

Lee, L. F.↗

NASA NTRS2010

The International Space Station: A Low-Earth Orbit (LEO) Test Bed for Advancements in Space and Environmental Medicine

Ground-based space analog projects such as the NASA Extreme Environment Mission Operations (NEEMO) can be valuable test beds for evaluation of experimental design and hardware feasibility before actually being implemented on orbit. The International Space Station (ISS) is an closed-system laboratory that orbits 240 miles above the Earth, and is the ultimate extreme environment. Its inhabitants spend hours performing research that spans from fluid physics to human physiology, yielding results that have implications for Earth-based improvements in medicine and health, as well as those that will help facilitate the mitigation of risks to the human body associated with exploration-class space missions. ISS health and medical experiments focus on pre-flight and in-flight prevention, in-flight treatment, and postflight recovery of health problems associated with space flight. Such experiments include those on enhanced medical monitoring, bone and muscle loss prevention, cardiovascular health, immunology, radiation and behavior. Lessons learned from ISS experiments may not only be applicable to other extreme environments that face similar capability limitations, but also serve to enhance standards of care for everyday use on Earth.

Ruttley, Tara M.↗

NASA NTRS2003

Precision Agriculture: Changing the Face of Farming

To a large extent our work has grown out of the remote sensing technology and conceptual framework developed by geologists. For example the drive to look at the physics of reflectance and atmospheric corrections is rooted in work done in the early 1980s by the United States Geological Survey and NASA. Our work on emissivity and thermal behavior of plants pulls on research done using the Thermal Infrared Multispectral Scanner, an instrument originally conceived for geologic applications. Even our ability to geometrically map the airborne imagery onto the globe was explicitly developed because of need to map sediment flow patterns in along the coast of Louisiana. Growing from this base we have learned much in the last few years and believe our integration of geologic remote sensing with the other fields of expertise was a wise investment. Clearly none of the specialties alone could develop, let alone test, the basic approach we are now finding so powerful. This is the path that will ultimately give the information needed by the farmer. We also recognize how small a portion of the total problem has been solved. Having developed the basic logic, built proto-type tools and performed initial tests everything else remains to be done. And problems, scientific and practical, are everywhere. We have not established sensitivities. We have not robustly segregated the contributions of crop residue, soil moisture, shadows, plant and soil to the energy leaving the surface. What we do is extremely expensive and difficult. It is experimental in methodology and uses research oriented tools. We are constantly alive to the practicality of moving our results into commercial applications. We know another airborne instrument will have to be available. Atmospheric parameters will have to be measured automatically. The software will have to be re-written for speed. At times the list of problems seems endless. But the potential is also enormous. Agriculture is a huge portion of our economy. Just a one percent increase in efficiency is a $2,000,000 change. We all depend on farmers, literally for the bread we eat. No other activity of man even has an impact on the land that farming has. If application of precision agriculture can nelp farmers manage their land better, we all may benefit.

Rickman, D.↗