An appraisal for use in reliability analysis of the test and data collection procedures in the manufacture of an inertial guidance system
Behavior of first gimbal stabilization loop for reliability analysis of inertial guidance system
GIMBAL↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Ion trajectories in a space-charge wave on a relativistic electron beam
The motion of an ion in a space-charge wave on a strongly magnetized electron beam is investigated. The motions of the ion perpendicular and parallel to the beam direction are coupled by a nonlinear term in the ion Hamiltonian that is proportional to the wave amplitude, and this coupling causes the motion of the ion to deviate significantly from that of a linear harmonic oscillator in certain resonant regions of phase space. A sequence of canonical transformations is employed to investigate the motion of the ion in these regions. It is determined that wave amplitudes that are too small to trap beam electrons are too small to cause these resonances to overlap. When this overlap does not occur, the motion is found not to be discernibly ergodic in any three-dimensional subspace of the energy hypersurface because there exists a third constant of the motion in addition to the total energy and angular momentum. The numerically integrated ion trajectories are studied using surface-of-section techniques in order to verify these findings. It is found that the third constant of the motion constrains an ion initially trapped in a potential well of the wave to remain trapped in this well. It is concluded that ergodic behavior poses no threat at attempts at collective ion acceleration in space-charge waves on an electron beam.
Russell, D. A.↗
Description of Selected Algorithms and Implementation Details of a Concept-Demonstration Aircraft VOrtex Spacing System (AVOSS)
A ground-based system has been developed to demonstrate the feasibility of automating the process of collecting relevant weather data, predicting wake vortex behavior from a data base of aircraft, prescribing safe wake vortex spacing criteria, estimating system benefit, and comparing predicted and observed wake vortex behavior. This report describes many of the system algorithms, features, limitations, and lessons learned, as well as suggested system improvements. The system has demonstrated concept feasibility and the potential for airport benefit. Significant opportunities exist however for improved system robustness and optimization. A condensed version of the development lab book is provided along with samples of key input and output file types. This report is intended to document the technical development process and system architecture, and to augment archived internal documents that provide detailed descriptions of software and file formats.
Hinton, David A.↗
Nemesis Autonomous Test System
A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.
Barltrop, Kevin J.↗
AEGIS: Autonomous Entity Global Intelligence System for Urban Air Mobility
This paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.
Artificial Intelligence↗
Thermal Modeling and Analysis of the Hurricane Imaging Radiometer (HIRad)
The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.
Mauro, Stephanie↗
Modeling and Analysis of the Hurricane Imaging Radiometer (HIRAD)
The Hurricane Imaging Radiometer (HIRad) is a payload carried by an unmanned aerial vehicle (UAV) at altitudes up to 60,000 ft with the purpose of measuring ocean surface wind speeds and near ocean surface rain rates in hurricanes. The payload includes several components that must maintain steady temperatures throughout the flight. Minimizing the temperature drift of these components allows for accurate data collection and conclusions to be drawn concerning the behavior of hurricanes. HIRad has flown on several different UAVs over the past two years during the fall hurricane season. Based on the data from the 2011 flight, a Thermal Desktop model was created to simulate the payload and reproduce the temperatures. Using this model, recommendations were made to reduce the temperature drift through the use of heaters controlled by resistance temperature detector (RTD) sensors. The suggestions made were implemented for the 2012 hurricane season and further data was collected. The implementation of the heaters reduced the temperature drift for a portion of the flight, but after a period of time, the temperatures rose. With this new flight data, the thermal model was updated and correlated. Detailed analysis was conducted to determine a more effective way to reduce the temperature drift. The final recommendations made were to adjust the set temperatures of the heaters for 2013 flights and implement hardware changes for flights beyond 2013.
Mauro, Stephanie↗
Ionizing Radiation Effects on Hole Collection Backside-Illuminated P-Type Deep-Trench Pinned Photo-MOS Pixels under Image Acquisition
Dark current degradation, origins, and annealing behavior after x-ray irradiation are studied in a P-type, hole collecting, backside-illuminated image sensor currently being developed at STMicroelectronics and based on deep-trenched photo-MOS pixels. Different biasing conditions during irradiation, i.e. grounded or biased and sequenced, are compared. The dark current increase with total ionizing dose (TID) and the dark current annealing behavior seem to be driven by the backside interface between the P-epitaxy of the pixels and the ONO stack. Despite still being under development, this pixel architecture already exhibits both very good electro-optical performance and a better radiation hardness than pinned photodiode-based CMOS Image sensors that benefit from the same advanced CIS processing technologies. At high total dose range, the photogate challenges custom Radiation-Hardened-by-Design photodiodes by exhibiting a comparable radiation tolerance while bringing new features such as high-resolution or Correlated Double Sampling.
CMOS Image Sensor (CIS)↗
Measurement realities of current collection in dynamic space plasma environments
Theories which describe currents collected by conducting and non-conducting bodies immersed in plasmas have many of their concepts based upon the fundamentals of sheath-potential distributions and charged-particle behavior in superimposed electric and magnetic fields. Those current-collecting bodies (or electrodes) may be Langmuir probes, electric field detectors, aperture plates on ion mass spectrometers and retarding potential analyzers, or spacecraft and their rigid and tethered appendages. Often the models are incomplete in representing the conditions under which the current-voltage characteristics of the electrode and its system are to be measured. In such cases, the experimenter must carefully take into account magnetic field effects and particle anisotropies, perturbations caused by the current collection process itself and contamination on electrode surfaces, the complexities of non-Maxwellian plasma distributions, and the temporal variability of the local plasma density, temperature, composition and fields. This set of variables is by no means all-inclusive, but it represents a collection of circumstances guaranteed to accompany experiments involving energetic particle beams, plasma discharges, chemical releases, wave injection and various events of controlled and uncontrolled spacecraft charging. Here, an attempt is made to synopsize these diagnostic challenges and frame them within a perspective that focuses on the physics under investigation and the requirements on the parameters to be measured. Examples include laboratory and spaceborne applications, with specific interest in dynamic and unstable plasma environments.
Szuszczewicz, Edward P.↗
Collectives for the Optimal Combination of Imperfect Objects
In this letter we summarize some recent theoretical work on the design of collectives, i.e., of systems containing many agents, each of which can be viewed as trying to maximize an associated private utility, where there is also a world utility rating the behavior of that overall system that the designer of the collective wishes to optimize. We then apply algorithms based on that work on a recently suggested testbed for such optimization problems. This is the problem of finding the combination of imperfect nano-scale objects that results in the best aggregate object. We present experimental results showing that these algorithms outperform conventional methods by more than an order of magnitude in this domain.
Tumer, Kagan↗
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↗
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↗
Probabilistic composite analysis
Formal procedures are described which are used to computationally simulate the probabilistic behavior of composite structures. The computational simulation starts with the uncertainties associated with all aspects of a composite structure (constituents, fabrication, assembling, etc.) and encompasses all aspects of composite behavior (micromechanics, macromechanics, combined stress failure, laminate theory, structural response, and tailoring) optimization. Typical cases are included to illustrate the formal procedure for computational simulation. The collective results of the sample cases demonstrate that uncertainties in composite behavior and structural response can be probabilistically quantified.
Chamis, C. C.↗
Massive parallelism in the future of science
Massive parallelism appears in three domains of action of concern to scientists, where it produces collective action that is not possible from any individual agent's behavior. In the domain of data parallelism, computers comprising very large numbers of processing agents, one for each data item in the result will be designed. These agents collectively can solve problems thousands of times faster than current supercomputers. In the domain of distributed parallelism, computations comprising large numbers of resource attached to the world network will be designed. The network will support computations far beyond the power of any one machine. In the domain of people parallelism collaborations among large groups of scientists around the world who participate in projects that endure well past the sojourns of individuals within them will be designed. Computing and telecommunications technology will support the large, long projects that will characterize big science by the turn of the century. Scientists must become masters in these three domains during the coming decade.
Denning, Peter J.↗
Summary of Research Issues in Behavior and Performance in Isolated and Confined Extreme (ICE) Environments
The papers presented in this section describe changes in behavior and performance in various isolated and confined extreme (ICE) environments, including Antarctic expeditions and research stations, space simulators and isolation chambers, and submarines. Each of these environments possesses characteristics that are in some way analogous to those found on long-duration space missions. Despite differences in length of mission, characteristics of mission personnel or crew, and characteristics in the physical environment, the various ICE environments described in this collection of papers appear to produce similar changes in behavior and performance. These changes include increased disturbances of mood, increased rates of psychiatric disorder, increased interpersonal tension, and a disruption of circadian rhythms. However, these environments do not inherently produce decrements in performance. Palinkas and colleagues suggest that prolonged exposure to the isolation and confinement in the Antarctic can actually have positive or "salutogenic" effects as well, evidenced by a decrease in mood disturbances and increase in performance measures.
Palinkas, Lawrence A.↗
Analysis of aerodynamic coefficients using gradient data: Spanwise turbulence effects on airplane response
The influence of spanwise turbulence on airplane dynamic behavior is determined. Calculations are based on data collected from test flights with the NASA B-57 research aircraft. The approach is to first compute aerodynamic forces and moments due to a spanwise distribution of angle of attack and airspeed. Secondly, these quantities are incorporated into the equations of motion. Simulation of flights done with the effects of spanwise turbulence included are compared to simulations without any spanwise turbulence. The findings of the study are that the moments developed by turbulence along the span are significant and that more realistic flight simulation can be achieved by including the spanwise turbulence terms.
Ringnes, E. A.↗
A case study demonstration of the soil temperature extrema recovery rates after precipitation cooling at 10-cm soil depth
Since the invention of maximum and minimum thermometers in the 18th century, diurnal temperature extrema have been taken for air worldwide. At some stations, these extrema temperatures were collected at various soil depths also, and the behavior of these temperatures at a 10-cm depth at the Tifton Experimental Station in Georgia is presented. After a precipitation cooling event, the diurnal temperature maxima drop to a minimum value and then start a recovery to higher values (similar to thermal inertia). This recovery represents a measure of response to heating as a function of soil moisture and soil property. Eight different curves were fitted to a wide variety of data sets for different stations and years, and both power and exponential curves were fitted to a wide variety of data sets for different stations and years. Both power and exponential curve fits were consistently found to be statistically accurate least-square fit representations of the raw data recovery values. The predictive procedures used here were multivariate regression analyses, which are applicable to soils at a variety of depths besides the 10-cm depth presented.
Welker, Jean Edward↗
Psychological Support Operations and the ISS One-Year Mission
Since NASA began human presence on the International Space Station (ISS) in November 1998, crews have spent two to seven months onboard. In March 2015 NASA and Russia embarked on a new era of ISS utilization, with two of their crewmembers conducting a one-year mission onboard ISS. The mission has been useful for both research and mission operations to better understand the human, technological, mission management and staffing challenges that may be faced on missions beyond Low Earth Orbit. The work completed during the first 42 ISS missions provided the basis for the pre-flight, in-flight and post-flight work completed by NASA's Space Medicine Operations Division, while our Russian colleagues provided valuable insights from their long-duration mission experiences with missions lasting 10-14 months, which predated the ISS era. Space Medicine's Behavioral Health and Performance Group (BHP) provided pre-flight training, evaluation, and preparation as well as in-flight psychological support for the NASA crewmember. While the BHP team collaboratively planned for this mission with the help of all ISS international partners within the Human Behavior and Performance Working Group to leverage their collective expertise, the US and Russian BHP personnel were responsible for their respective crewmembers. The presentation will summarize the lessons and experience gained within the areas identified by this Working Group as being of primary importance for a one-year mission.
Beven, G.↗