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The goal of the Solar System Visualization (SSV) project is to re-explore the planets using the data from previous National Aeronautics and Space Administration (NASA) planetary missions on and public information.
The relationship between actual and predicted re-entry maximum dynamic pressure is characterized using a probability density function and a cumulative distribution function derived from sounding rocket flight data. This paper explores the properties of this distribution and demonstrates applications of this data with observed sounding rocket re-entry body damage characteristics to assess probabilities of sustaining various levels of heating damage. The results from this paper effectively bridge the gap existing in sounding rocket reentry analysis between the known damage level/flight environment relationships and the predicted flight environment.
We have made a statistical study of the spatial distribution of low frequency waves (approx. 0.01-0.1 Hz) in the region upstream of the pre-dawn to dawn side bow shock (-50 Re less than X less than 15 Re) using both GEOTAIL and international sun earth explorer 3 (ISEE-3) magnetometer data. We have found that the wave amplitude dependence on D and X(sub s), where D is the distance from the bow shock and X(sub s) the x-coordinate position of shock foot point of the IMF, can be described by a functional form of A exp (X(sub s)/L(sub X)-D/L(sub D), with the characteristic attenuation distances, L(sub X) = 62 +/- 12 Re and L(sub D) = 59 +/- 38 Re.
The Swift Observatory is a Middle-Class Explorer (MIDEX) mission that is a rapidly re-pointing spacecraft with immediate data distribution capability to the astronomical community. Its primary objectives are to characterize and determine the origin of Gamma Ray Bursts (GRBs) and to use the collected data on GRB phenomena in order to probe the universe and gain insight into the physics of black hole formation and early universe. The main components of the spacecraft are the Burst Alert Telescope (BAT), Ultraviolet and Optical Telescope (UVOT), X-Ray Telescope (XRT), and Optical Bench (OB) instruments coupled with the Swift spacecraft (S/C) bus. The Swift Observatory will be tested at the Space Environment Simulation (SES) chamber at the Goddard Space Flight Center from May to June 2004 in order to characterize its thermal behavior in a vacuum environment. In order to simulate the independent thermal zones required by the BAT, XRT, UVOT, and OB instruments, the spacecraft is mounted on a chariot structure capable of maintaining adiabatic interfaces and enclosed in a modified, four section MSX fixture in order to accommodate the strategic placement of seven cryopanels (on four circuits), four heater panels, and a radiation source burst simulator mechanism. There are additionally 55 heater circuits on the spacecraft. To mitigate possible migration of silicone contaminants from BAT to the XRT and UVOT instruments, a contamination enclosure is to be fabricated around the BAT at the uppermost section of the MSX fixture. This paper discuses the test requirements and implemented thermal vacuum test configuration for the Swift Observatory.
The Orion Multi-Purpose Crew Vehicle (MPCV) Reaction Control System (RCS) is critical to guide the vehicle along the desired trajectory during re-‐entry. However, this system has a significant impact on the convective heating environment to the spacecraft. Heating augmentation from the jet interaction (JI) drives thermal protection system (TPS) material selection and thickness requirements for the spacecraft. This paper describes the heating environment from the RCS on the afterbody of the Orion MPCV during Orion's first flight test, Exploration Flight Test 1 (EFT-1). These jet plumes interact with the wake of the crew capsule and cause an increase in the convective heating environment. Not only is there widespread influence from the jet banks, there may also be very localized effects. The firing history during EFT-1 will be summarized to assess which jet bank interaction was measured during flight. Heating augmentation factors derived from the reconstructed flight data will be presented. Furthermore, flight instrumentation across the afterbody provides the highest spatial resolution of the region of influence of the individual jet banks of any spacecraft yet flown. This distribution of heating augmentation across the afterbody will be derived from the flight data. Additionally, trends with possible correlating parameters will be investigated to assist future designs and ground testing programs. Finally, the challenges of measuring JI, applying this data to future flights and lessons learned will be discussed.
This project was designed to use archival data from the International Ultraviolet Explorer (IUE) to measure the flux from the shock breakout from SN 1987A, emitted in the first few hours of the event, reflected from neighboring dust clouds in the form of a light echo. Such hot flux from the initial moments of a SN had never been observed before, and this offers a unique opportunity to measure it in reflection. Gilmozzi (1991, in "SN 1987A and Other Supernova") had reported such a detection of using IUE from observations made in 1988 and 1989. Our analysis of these data showed that this detection was very weak, and subject to changes comparable to the size of the signal according to various reasonable means of extracting the spectra. In fact, the features reported by Gilmozzi (1998 in "Ultraviolet Astrophysics Beyond IUE") are not found in a conventional NEWSIPS extraction or extended-source extraction of these data. The 1995 and 1996 IUE data collected for Crotts and Gilmozzi shows a more robust signal (about 20sigma,) from a cloud that echoes brighter in the optical than the 1988 feature. This shows a well-detected signal of about 5 x 10(exp -16) ergs per s/sq cm/Angstroms/arc sq sec at 1300 Angstroms, with a declining slope to redder wavelengths. This surface brightness, while detected with confidence in both large and small SVVT apertures, is still fainter than the reflected flux expected for most models of shock breakout. We have also taken the opportunity to recover the echo signal from exposures taken on Astro-1 and Astro-2 flights of the Ultraviolet Imaging Telescope (UIT) while pointed towards SN 1987A. These data yield a complementary result on the reflected flux, an upper limit of about 5 x 10(exp -17) ergs per s/sq cm/Angstroms/arc sq sec, spread over fainter optically echoing clouds, and over the same band where the signal was detected by IUE. While this appears in contradiction with the IUE result, it is made consistent by the corresponding strengths of the echoes in the optical.
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 data re-analysis and re-use via Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). To address the challenges posed by gaining new knowledge from a vast and diverse amount of biological, health and environmental data in space, the NASA Open Science Data Repository (OSDR - osdr.nasa.gov/bio) plays a crucial role in curating and openly publishing biological data from space-related experiments. Its design incorporates successes and lessons from NASA GeneLab, encompassing not only high-throughput sequencing data but also physiological, phenotypic, and telemetry data. The OSDR makes space biological data FAIR (findable, accessible, interoperable, reusable), and facilitates effective data ingestion, dissemination, and Open Science collaborations. The OSDR also has the capability to integrate human astronaut data with state-of-the-art security and accessibility procedures. We will discuss here several strategies that NASA’s Biological and Physical Science Division have put in place to maximize the return on investment for spaceflight bioscience data.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
The MGS Orbiter is carrying the high-precision Mars Orbiter Laser Altimeter (MOLA) which, when combined with telemetered latitude data, provides a tie between inertial space and Mars-fixed coordinates to an accuracy of 100 m in latitude/longitude and 10 m in radius (1 sigma), orders of magnitude more accurate than previous global geodetic/ cartographic control data. Over the 2 year MGS mission lifetime, it is expected that over 30,000 MOLA Global Cartographic Control Points will be produced to form the basis for new and re-derived map and geodetic products, key to the analysis of existing and evolving MGS data as well as future Mars exploration.
The MGS Orbiter is carrying the high-precision Mars Orbiter Laser Altimeter (MOLA) which, when combined with precision reconstructed orbital data and telemetered attitude data, provides a tie between inertial space and Mars-fixed coordinates to an accuracy of 100 m in latitude / longitude and 10 m in radius (1 sigma), orders of magnitude more accurate than previous global geodetic/ cartographic control data. Over the 2 year MGS mission lifetime, it is expected that over 30,000 MOLA Global Cartographic Control Points will be produced to form the basis for new and re-derived map and geodetic products, key to the analysis of existing and evolving MGS data as well as future Mars exploration. Additional information is contained in the original extended abstract.
NASA has increasingly relied on high-performance computing (HPC) re- sources for computational modeling, simulation, and data analysis to meet the science and engineering goals of its missions in space exploration, aeronautics, and Earth and space science. The NASA Advanced Supercomputing (NAS) Division at Ames Research Center in Silicon Valley, Calif., hosts NASA’s premier supercomputing resources, integral to achieving and enhancing the success of the agency’s missions. NAS provides a balanced environment, funded under the High-End Computing Capability (HECC) project, comprised of world-class supercomputers, including its flagship distributed-memory cluster, Pleiades; high-speed networking; and massive data storage facilities, along with multi-disciplinary support teams for user support, code porting and optimization, and large-scale data analysis and scientific visualization. However, as scientists have increased the fidelity of their simulations and engineers are conducting larger parameter-space studies, the requirements for supercomputing resources have been growing by leaps and bounds. With the facility housing the HECC systems reaching its power and cooling capacity, NAS undertook a prototype project to investigate an alternative approach for housing supercomputers. Modular supercomputing, or container-based computing, is an innovative concept for expanding NASA’s HPC capabilities. With modular supercomputing, additional containers—similar to portable storage pods—can be connected together as needed to accommodate the agency’s ever-increasing demand for computing resources. In addition, taking advantage of the local weather permits the use of cooling technologies that would additionally save energy and reduce annual water usage. The first stage of NASA’s Modular Supercomputing Facility (MSF) prototype, which resulted in a 1,000 square-foot module on a concrete pad with room for 16 compute racks, was completed in Fall 2016 and an SGI (now HPE) computer system, named Electra, was deployed there in early 2017. Cooling is performed via an evaporative system built into the module, and preliminary experience shows a Power Usage Effectiveness (PUE) measurement of 1.03. Electra achieved over a petaflop on the LINPACK benchmark, sufficient to rank number 96 on the November 2016 TOP500 list [14]. The system consists of 1,152 InfiniBand-connected Intel Xeon Broadwell-based nodes. Its users access their files on a facility-wide file system shared by all HECC compute assets via Mellanox MetroX InfiniBand extenders, which connect the Electra fabric to Lustre routers in the primary facility over fiber-optic links about 900 feet long. The MSF prototype has exceeded expectations and is serving as a blueprint for future expansions. In the remainder of this chapter, we detail how modular data center technology can be used to expand an existing compute resource. We begin by describing NASA’s requirements for supercomputing and how resources were provided prior to the integration of the Electra module-based system.
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.
The Orion spacecraft, under NASA's Artemis program, is an integral step in humanity's ambitions of deep space exploration, including our return to the Moon and subsequent mission to Mars. The performance of Orion's heatshield is central to ensuring the safety and success of such missions. This presentation offers a comprehensive overview of the process used in and findings derived from tomography scans of an Artemis 1 heatshield sample executed at the Lawrence Berkeley National Laboratory's Advanced Light Source. These tomography scans captured high-resolution X-Ray images of the heatshield's microstructure, allowing advanced imaging to be employed for 3D image segmentation and visualization of the sample. The Porous Media Analysis (PuMA) software stands central to our analysis, offering robust computational algorithms and methodologies to digitally compute properties of the heatshield material. The presentation discusses the following derived metrics: - Volume fractions, representing the spatial distribution and amounts of various components throughout the heatshield’s depth. - Porosity and crack distributions, offering insights into the void space within the material, critical for understanding the heatshield's structural integrity. - Fiber orientation, detailing the alignment and arrangement of fibers, significant for material orthotropy. - Permeability estimates, quantifying the passage of gas through the material, relevant for re-entry conditions and pyrolysis gasses. One distinctive feature of this study is the comparative evaluation against data obtained from tomography scans of the Exploration Flight Test-1 (EFT-1) heatshield. Such a comparison offers multiple insights, including evaluation of the consistency and repeatability of manufacturing processes as well as understanding of any evolutionary changes in heatshield design or material properties.
Lunar neutron flux data measured by the Lunar Exploration Neutron Detector (LEND) on board NASA's Lunar Reconnaissance Orbiter (LRO) were analyzed for the period 2009-2014.We have re-evaluated the instrument's collimation capability and re-estimated the neutron counting rate measured in the Field of View (FOV) of the LEND collimated detectors, and found it to be 1.070.1counts per second. We derived the spectral density of the neutron flux for various lunar regions using our comprehensive numerical model of orbital measurements. This model takes into account the location of the LEND instrument onboard LRO to calculate the surface leakage neutron flux and its propagation to the instrument detectors. Based on this we have determined the lunar neutron flux at the surface to be approx. 2 neutrons/ [sq cm/ sec] in the epithermal energy range, 0.4e V to 1keV. We have also found variations of the lunar neutron leakage flux with amplitude as large as a factor of two, by using multi-year observations to explore variations in the Galactic Cosmic Ray (GCR) flux during the 23rd-24th solar cycles.
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.
Arc-jet testing is an indispensable tool in elucidating the complex interactions materials undergo when subjected to the extreme thermal and mechanical stresses of atmospheric entry. By replicating these harsh conditions, arc-jets enable researchers to explore critical phenomena such as ablation, melting, and spallation, generating invaluable data that informs the development of sophisticated, physics-based models. These models are pivotal not only in predicting material performance for spacecraft re-entry systems but also in assessing the threat posed by celestial bodies as they encounter Earth’s atmosphere. One of the leading facilities for such investigations is the Hypersonic Materials Environmental Test System (HyMETS) at NASA’s Langley Research Center. A pioneering test campaign conducted at HyMETS focused on unraveling the ablation mechanisms of an ordinary chondrite meteorite (Tamdakht H5) and a terrestrial analog (basalt). These studies unveiled distinct material behaviors: Tamdakht demonstrated a remarkably stable melt flow, with mass loss primarily driven by the volatilization of elements such as iron and sodium, whereas basalt exhibited more aggressive surface degradation due to the rapid decomposition of hydrated minerals, culminating in significant spallation and surface material ejection. Beyond meteorite analysis, HyMETS has also been integral in the exploration of advanced Thermal Protection Systems (TPS) such as Phenolic Impregnated Carbon Ablator (PICA), a material with extensive flight heritage in missions like Stardust, Mars Science Laboratory, Mars 2020, and Osiris-REx. PICA, when coated with NuSil CV-1144-0—a polysiloxane resin designed to prevent particle shedding—undergoes a remarkable transformation upon heating. The resin pyrolyzes to form a thin, oxidation-resistant silicon oxycarbide layer that profoundly influences the material’s thermal response through a sophisticated four-stage process. Initially, the silicon oxycarbide acts as a formidable barrier, impeding reactive interactions with the PICA char and effectively suppressing surface temperature rise and material recession. However, as the heating persists, the protective layer decomposes via carbothermal reduction, exposing the underlying char, which in turn leads to a dramatic spike in surface temperature and accelerated material erosion. In the final stage, equilibrium is reached, with recession rates aligning with those of the virgin material. This intricate understanding of PICA-NuSil behavior under extreme conditions offers valuable insights, enhancing material response models and advancing the development of next-generation TPS for space exploration.