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NASA NTRSDate not supplied

TechEdSat 7, 10, 13, 15: Exo-brake Experiments on the ISS, First Virgin Orbit, and First Firefly-Alpha Test Flights

The TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has been studying cube satellite re-entry technologies with a focus on Exo-Brake drag device research. An exo-atmospheric braking device, the Exo-Brake uses the tenuous upper atmosphere acting in a free molecular flow regime to enable controllable adjustment of the drag profile of the host spacecraft, currently enabling rapid disposal of small spacecraft after mission conclusion, and eventual targeted de-orbit to a desired entry point at the Von Karman line, or approximately 100km in altitude. The TES-7 mission, flown on the first successful Virgin Orbit flight January 17, 2021, was injected into a 500km, 61-degree inclination orbit, with an expected orbital lifetime of approximately ten years. After successful deployment of a unique hydrogen gas-cell inflation design ‘disposal’-type Mylar Exo-Brake intended to rapidly de-orbit the spacecraft, the maximum expected orbital lifetime of the TES-7 spacecraft was successfully reduced from ten years to 1.3 years, an 87% reduction in orbit lifetime via a passive, fuel-less system. The subsequent TES-13 mission utilized a new, less complex ‘disposal’-type Exo-Brake design made from collapsible rigid struts rather than inflated struts to reduce the engineering, construction, and safety complexities introduced by the prior hydrogen gas inflation design. The rigid strut Exo-Brake design is stowed via rotational compression and deploys using only stored spring energy once released via electronic actuator. This new rotation compression storage design has become the new Exo-Brake design standard used on TES missions thanks to its simplicity. Following on the success of this new Exo-Brake design demonstrated by TES-13, the design was further refined to enabled active manipulation of the Exo-Brake effective drag area though a winch-like system. Both the TES-10 and TES-15 spacecraft were equipped with geometrically similar Exo-Brake devices capable of such active drag modulation, with the intent of studying the operational impact of having such devices on cube satellite missions. To increase the survival time of the Exo-Brake at low altitudes and thus gain more experiment time and better guidance capability, TES-15 was equipped with a modulated Exo-Brake constructed from 3M™ Nextel™ 440 ceramic oxide fiber, the same fabric material used in the Space Shuttle TPS system, rather than Mylar. After launching on Alpha Flight 2 October 1st, 2022, Firefly’s first successful launch, TES-15 met the same modulation restrictions as TES-10, with modulation prohibited above 200km to avoid possible collision with other spacecraft. In this case, a low deployment orbit caused an extremely short mission life, which, coupled with delayed spacecraft identification, exacerbated collision concerns and caused performance data of the high-temperature Exo-Brake design to be inconclusive. As such, the Nano Orbital Workshop has taken a new approach of only conducting modulated Exo-Brake research on missions deploying below ISS altitude to avoid collision avoidance restrictions, and is working to improve spacecraft identification and location reporting techniques to increase experiment durations. Upcoming TES mission will therefore use one of two classes of Exo-Brake depending on their target altitude, as to be described in the submission.

Marcus Murbach↗
NASA NTRS2006

CCSDS SLE Service Management: Real-World Use Cases

The purpose of this paper is to illustrate how the standard SLE-SM services can be applied to the operations of current TT&C providers. It begins with a brief overview of the scope and operating environment of the SLE-SM services, then describes several use cases in which SLE-SM services can be applied to existing operational situations. The paper also addresses how SLE-SM services can be adopted in an evolutionary fashion. The paper concludes with a brief identification of additions to SLE-SM that are under consideration to make SLE-SM applicable to an even broader range of network operations concepts, policies, and procedures.

Consultative Committee for Space Data Systems (CCS↗
NASA NTRS2014

Evolution of the JPSS Ground Project Calibration and Validation System

The Joint Polar Satellite System (JPSS) is the National Oceanic and Atmospheric Administrations (NOAA) next-generation operational Earth observation Program that acquires and distributes global environmental data from multiple polar-orbiting satellites. The JPSS Program plays a critical role to NOAAs mission to understand and predict changes in weather, climate, oceans, coasts, and space environments, which supports the Nation’s economy and protection of lives and property. The National Aerospace and Atmospheric Administration (NASA) is acquiring and implementing the JPSS, comprised of flight and ground systems on behalf of NOAA. The JPSS satellites are planned to fly in the afternoon orbit and will provide operational continuity of satellite-based observations and products for NOAA Polar-orbiting Operational Environmental Satellites (POES) and the Suomi National Polar-orbiting Partnership (SNPP) satellite. To support the JPSS Calibration and Validation (CalVal) node Government Resource for Algorithm Verification, Independent Test, and Evaluation (GRAVITE) services facilitate: Algorithm Integration and Checkout, Algorithm and Product Operational Tuning, Instrument Calibration, Product Validation, Algorithm Investigation, and Data Quality Support and Monitoring. GRAVITE is a mature, deployed system that currently supports the SNPP Mission and has been in operations since SNPP launch. This paper discusses the major re-architecture for Block 2.0 that incorporates SNPP lessons learned, architecture of the system, and demonstrates how GRAVITE has evolved as a system with increased performance. It is now a robust, stable, reliable, maintainable, scalable, and secure system that supports development, test, and production strings, replaces proprietary and custom software, uses open source software, and is compliant with NASA and NOAA standards.

Data Processing↗
NASA NTRS1995

How far away is plug 'n' play? Assessing the near-term potential of sonification and auditory display

The commercial music industry offers a broad range of plug 'n' play hardware and software scaled to music professionals and scaled to a broad consumer market. The principles of sound synthesis utilized in these products are relevant to application in virtual environments (VE). However, the closed architectures used in commercial music synthesizers are prohibitive to low-level control during real-time rendering, and the algorithms and sounds themselves are not standardized from product to product. To bring sound into VE requires a new generation of open architectures designed for human-controlled performance from interfaces embedded in immersive environments. This presentation addresses the state of the sonic arts in scientific computing and VE, analyzes research challenges facing sound computation, and offers suggestions regarding tools we might expect to become available during the next few years. A list of classes of audio functionality in VE includes sonification -- the use of sound to represent data from numerical models; 3D auditory display (spatialization and localization, also called externalization); navigation cues for positional orientation and for finding items or regions inside large spaces; voice recognition for controlling the computer; external communications between users in different spaces; and feedback to the user concerning his own actions or the state of the application interface. To effectively convey this considerable variety of signals, we apply principles of acoustic design to ensure the messages are neither confusing nor competing. We approach the design of auditory experience through a comprehensive structure for messages, and message interplay we refer to as an Automated Sound Environment. Our research addresses real-time sound synthesis, real-time signal processing and localization, interactive control of high-dimensional systems, and synchronization of sound and graphics.

Bargar, Robin↗
NASA NTRS2006

Coloration Determination of Spectral Darkening Occurring on a Broadband Earth Observing Radiometer: Application to Clouds and the Earth's Radiant Energy System (CERES)

It is estimated that in order to best detect real changes in the Earth s climate system, space based instrumentation measuring the Earth Radiation Budget (ERB) must remain calibrated with a stability of 0.3% per decade. Such stability is beyond the specified accuracy of existing ERB programs such as the Clouds and the Earth s Radiant Energy System (CERES, using three broadband radiometric scanning channels: the shortwave 0.3 - 5microns, total 0.3. > 100microns, and window 8 - 12microns). It has been shown that when in low earth orbit, optical response to blue/UV radiance can be reduced significantly due to UV hardened contaminants deposited on the surface of the optics. Since typical onboard calibration lamps do not emit sufficient energy in the blue/UV region, this darkening is not directly measurable using standard internal calibration techniques. This paper describes a study using a model of contaminant deposition and darkening, in conjunction with in-flight vicarious calibration techniques, to derive the spectral shape of darkening to which a broadband instrument is subjected. Ultimately the model uses the reflectivity of Deep Convective Clouds as a stability metric. The results of the model when applied to the CERES instruments on board the EOS Terra satellite are shown. Given comprehensive validation of the model, these results will allow the CERES spectral responses to be updated accordingly prior to any forthcoming data release in an attempt to reach the optimum stability target that the climate community requires.

Matthews, Grant↗
NASA NTRS2000

Global Geophysical Fluids Center of IERS

The Global Geophysical Fluids Center (GGFC) and its seven Special Bureaus (SB, for Atmosphere, Oceans, Tides, Hydrology, Mantle, Core and Gravity/Geocenter) were establishes by the International Earth Rotation Service in 1998, to support global geodynamic research. Mass transports in the geophysical fluids of the Earth system will cause observable geodynamic effects on a broad time scale.These include (1) variations in the solid Earth's rotation (in length-of-day and polar motion/nutation) via the conservation of angular momentum and effected by torques at the fluid-solid Earth interface; (2) changes in the global gravitational field according to Newton's gravitational law; and (3) motion in the center of mass of the solid Earth relative to that of the whole Earth ("geocenter") via the conservation of linear momentum. These minute signals have become observable by space geodetic techniques, primarily VLBI, SLR, GPS, and DORIS, and new exciting data will be available by space gravity, altimetry, SAR, and magnetic missions. In this sense the precise space geodetic techniques have become effective means of remote sensing of global mass transports. The GGFC and its SBs have the responsibility of supporting, facilitating, and providing services to the worldwide research community in the related research areas. We compute, analyze, compare, archive, and disseminate the time series of the angular momenta and the related torques, gravitational coefficients, and geocenter shift for all geophysical fluids, based on global observational data, and/or products from state-of-the-art models some of which assimilate such data. The computed quantities, algorithm and data formats are standardized. This paper reviews our activities, reports the status, and looks forward into the future.

Chao, Benjamin F.↗
NASA NTRS2011

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. Building upon the success of the U.S.-Japan Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration (NASA) of the United States and the Japan Aerospace and Exploration Agency (JAXA) will deploy in 2013 a GPM "Core" satellite carrying a KulKa-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Imager (GMI) to establish a new reference standard for precipitation measurements from space. The combined active/passive sensor measurements will also be used to provide common database for precipitation retrievals from constellation sensors. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer- 2 (AMSR-2) on the GCOM-Wl satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha-Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites, and (8) a microwave imager under planning for the Defense Weather Satellite System (DWSS).

Hou, Arthur Y.↗
NASA NTRS2011

NASA Tech Briefs, February 2011

Topics covered include: Multi-Segment Radius Measurement Using an Absolute Distance Meter Through a Null Assembly; Fiber-Optic Magnetic-Field-Strength Measurement System for Lightning Detection; Photocatalytic Active Radiation Measurements and Use; Computer Generated Hologram System for Wavefront Measurement System Calibration; Non-Contact Thermal Properties Measurement with Low-Power Laser and IR Camera System; SpaceCube 2.0: An Advanced Hybrid Onboard Data Processor; CMOS Imager Has Better Cross-Talk and Full-Well Performance; High-Performance Wireless Telemetry; Telemetry-Based Ranging; JWST Wavefront Control Toolbox; Java Image I/O for VICAR, PDS, and ISIS; X-Band Acquisition Aid Software; Antimicrobial-Coated Granules for Disinfecting Water; Range 7 Scanner Integration with PaR Robot Scanning System; Methods of Antimicrobial Coating of Diverse Materials; High-Operating-Temperature Barrier Infrared Detector with Tailorable Cutoff Wavelength; A Model of Reduced Kinetics for Alkane Oxidation Using Constituents and Species for N-Heptane; Thermally Conductive Tape Based on Carbon Nanotube Arrays; Two Catalysts for Selective Oxidation of Contaminant Gases; Nanoscale Metal Oxide Semiconductors for Gas Sensing; Lightweight, Ultra-High-Temperature, CMC-Lined Carbon/Carbon Structures; Sample Acquisition and Handling System from a Remote Platform; Improved Rare-Earth Emitter Hollow Cathode; High-Temperature Smart Structures for Engine Noise Reduction and Performance Enhancement; Cryogenic Scan Mechanism for Fourier Transform Spectrometer; Piezoelectric Rotary Tube Motor; Thermoelectric Energy Conversion Technology for High-Altitude Airships; Combustor Computations for CO2-Neutral Aviation; Use of Dynamic Distortion to Predict and Alleviate Loss of Control; Cycle Time Reduction in Trapped Mercury Ion Atomic Frequency Standards; and A (201)Hg+ Comagnetometer for (199)Hg+ Trapped Ion Space Atomic Clocks.

Source record↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS2023

A Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts: Fifth Revision

This document describes an algorithm for the generation of a four dimensional trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. The algorithm calculates the altitude, speed, along path distance, and along path time for each of these waypoints. Wind data at each of these waypoints are also used for the calculation of ground speed and turn radius. This revision of the algorithm now accommodates linear decelerations between two speed-constrained waypoints. While this modification may appear trivial, the calculation of the deceleration rate cannot be accomplished using a closed-form solution. An iterative solution was developed that allowed for the variability of path distance due to speed influence on turn radii, Mach-CAS transition altitude, and the impact of wind on ground speed in calculating an accurate deceleration value.

Aircraft Operations↗
NASA NTRS1993

Space Shuttle main engine OPAD: The search for a hardware enhanced plume

The process of applying spectroscopy to the Space Shuttle Main Engine (SSME) for plume diagnostics, as it exists today, originated at Marshall Space Flight Center in Huntsville, Alabama, and its implementation was assured largely through the efforts of Sverdrup AEDC, in Tullahoma, Tennessee. This team continues to lead and guide efforts in the plume diagnostics arena. The process, Optical Plume Anomaly Detection (OPAD), formed the basis for various activities in the development of ground-based systems as well as the development of in-flight plume spectroscopy. OPAD currently provides and will continue to provide valuable information relative to future systems definitions, instrumentation development, code validation, and data diagnostic processing. OPAD is based on the detection of anomalous atomic and molecular species in the SSME plume using two complete, stand-alone optical spectrometers. To-date OPAD has acquired data on 44 test firings of the SSME at the Technology Test Bed (TTB) at MSFC. The purpose of this paper will be to provide an introduction to the OPAD system by discussing the process of obtaining data as well as the methods of examining and interpreting the data. It will encompass such issues as selection of instrumentation correlation of data to nominal engine operation, investigation of SSME component erosion via OPAD spectral data, necessity and benefits of plume seeding, application of artificial intelligence (AI) techniques to data analysis, and the present status of efforts to quantify specie erosion utilizing standard plume and chemistry codes as well as radiative models currently under development.

Powers, W. T.↗
NASA NTRS2004

Modelling and Analysis of a Regenerative Fuel Cell Propulsion System for a High Altitude Long Endurance UAV

In the search to bridge current gaps in surveillance and communication technologies, a new type of, aircraft is currently undergoing design. The idea of a High Altitude Long Endurance (HALE) aircraft is already a few decades old, but has only recently become realizable. A relay and collector of information at altitudes of 65,000 feet and higher could greatly improve standards of data exchange, homeland security, and research of the air, land and sea. NASA, as a major force in propulsion research, is exploring methods of powering an autonomous aircraft for days, weeks, or even months without refueling. Such a task requires not only high energy density, but also the ability to make use of renewable energy sources to regenerate power. Hydrogen is one of the most energy dense fuels available. Fuel cells make use of hydrogen by harnessing the energy released as it combines with oxygen to produce electricity and water. Fuel cells are envisioned to occupy future propulsion systems in cooperation with solar cells where the photovoltaic arrays harness sunlight into power which can electrolize the water byproduct into reusable hydrogen and oxygen. Modeling this type of system requires adequate assumptions of support hardware and daily transients in operation. The performance of a regenerative fuel cell propulsion system lies in the flight characteristics (altitude, density, temperature, latitude, etc.). Each subsystem is defined by many parameters which can be varied across wide ranges. Statistical and probabilistic analyses bring forward a wealth of information that can be utilized in the design process. This is necessary since the required technologies are relatively young and barely, if yet, capable. Once the modeling is complete, a design space exploration of this highly constrained scenario can be utilized to find the optimal design. The model will become an interactive environment with which experiments and tests can be run. When linked

Simpson, Mike B.↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRS2004

Epoxy Adhesives for Stator Magnet Assembly in Stirling Radioisotope Generators (SRG)

As NASA seeks to fulfill its goals of exploration and understanding through missions planned to visit the moons of Saturn and beyond, a number of challenges arise from the idea of deep space flight. One of the first problems associated with deep space travel is electrical power production for systems on the spacecraft. Conventional methods such as solar power are not practical because efficiency decreases substantially as the craft moves away from the Sun. The criterion for power generation during deep space missions are very specific, the main points requiring high reliability, low mass, minimal vibration and a long lifespan. A Stirling generator, although fairly old in concept, is considered to be a potential solution for electrical power generation for deep space flight. A Stirling generator works on the same electromagnetic principles of a standard generator, using the linear motion of the alternator through the stationary stator which produces electric induction. The motion of the alternator, however, is produced by the heating and cooling dynamics of pressurized gases. Essentially heating one end and cooling another of a contained gas will cause a periodic expansion and compression of the gas from one side to the other, which a displacer translates into linear mechanical motion. NASA needs to confirm that the materials used in the generator will be able to withstand the rigors of space and the life expectancy of the mission. I am working on the verification of the epoxy adhesives used to bond magnets to the steel lamination stack to complete the stator; in terms of in-service performance and durability under various space environments. Understanding the proper curing conditions, high temperature properties, and degassing problems as well as production difficulties are crucial to the long term success of the generator. system and steel substrate used in the stator. To optimize the curing conditions of the epoxies, modulated differential scanning calorimetry analysis was done as a function of cure time and temperatures. Adhesion bond strength was tested at various temperatures with lap shear samples using Hiperco 50 substrate to ensure that the proper adhesive is being used. To try and solve the problem of bondline thickness, micro glass beads of 0.0017" in diameter were investigated to see if any other physical properties of the epoxy were affected. Efforts will be made to develop a standard, optimized, fabrication process/procedure of sub-scale magnet-stator assemblies for various adhesive performance evaluation studies under simulated generator conditions. Also, accelerated aging testing will be done in a pressurized canister with stator assembly samples for three years to verify if any degassing or thermal degradation of the epoxy occurs. The necessity of verifying the correct epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself. My work involves specimen fabrications, testing, and data analyses of the epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself.

Cater, George M.↗
NASA NTRSDate not supplied

Using Open Standards and NASA Open Source Simulation Tools to Model Artemis Base Camp Mission Timelines

The United States’ National Aeronautics and Space Administration (NASA) has announced that the Artemis Program will return humans to the Moon, establishing a persistent presence with the Artemis Base Camp (ABC), and extend human exploration to Mars. The NASA Exploration Systems Simulations (NExSyS) team at NASA’s Johnson Space Center is using internationally developed simulation interoperability standards and NASA open source simulation tools to support Artemis concept, analysis, designs, development, training, and ultimately operations. The NExSyS team has been tasked to support early ABC architecture and mission analysis using mission time lines developed by the crew operations mission planning team. The NExSyS team is developing a distributed simulation framework with initial Artemis element implementations to model the ABC mission timelines using the international simulation interoperability standard High Level Architecture (HLA), the Simulation Interoperability Standards Organization’s Space Reference Federation Object Model (SpaceFOM), the NASA open source Trick Simulation Environment, and another NASA open source interface package called TrickHLA. The ABC architecture is composed of a number of key surface elements and resources. Some examples of modeled elements (also known as entities) are landers, habitats, rovers, logistics carriers, and astronauts. Some examples of modeled transferable and consumable resources are power, water, oxygen, nitrogen, scientific samples, and food. These entities and resources are modeled in a collection of individual simulations called Federates. A coordinated collection of interoperable federates is called a Federation and when these federates are tied together in a coordinated simulation run, it is referred to as a Federation Execution. The federates communicate through HLA using data exchange formats defined by a collection of machine readable files called Federation Object Models (FOMs). These FOM files are based on extensions to the SpaceFOM. This enables the instantiation and sharing of objects and interactions between federates in the federation. These provide for entity and resource tracking, object transfer, and data collection. Federate interactions are used to trigger events and notify federates of entity or resource transfers. For the initial implementation, the constituent federates are Trick-based simulations that use TrickHLA to provide the required HLA-base interoperability. These Trick-based simulations provide the required modeling for the individual Artemis elements along with the associated element resources. These federates provide a means to explore traverses between surface elements and exploration sites as scheduled in a mission timeline and explore the affects traverse times have on the overall mission timeline. The mission time lines are modeled using a Trick input file event handling capabilities. Each timeline operation is handled as individual simulation events, and triggered based on previous event status, time of operation, and simulated task completions. In addition, the ABC Federation can be used to perform Monte Carlo analysis. The Monte Carlo tool can vary the inputs, timings, and malfunctions to show how various contingencies in the mission can affect the mission timeline.

Keaton Craig Dodd↗
NASA NTRS2024

The Mega-MUSCLES Treasury Survey: X-Ray to Infrared Spectral Energy Distributions of a Representative Sample of M Dwarfs

We present 5–1 × 107 Å spectral energy distributions (SEDs) for 12 M dwarf stars covering spectral types M0–M8. Our SEDs are provided for community use as a sequel to the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) survey. The 12 stars include eight known exoplanet hosts and four stars chosen to fill out key parameter space in spectral type and rotation period. The SEDs are constructed from Hubble Space Telescope ultraviolet spectroscopy and XMM Newton, Chandra, and/or Swift X-ray observations, and completed with various model data, including Lyα reconstructions, PHOENIX optical models, APEC coronal models, and differential emission measure models in the currently-unobservable extreme ultraviolet. We provide a complete overview of the Mega-MUSCLES program, including a description of the observations, models, and SED construction. The SEDs are available as MAST high-level science products and we describe the various data products here. We also present ensemble measurements from our sample that are of particular relevance to exoplanet science, including the high-energy fluxes in the habitable zone and the farultraviolet to near-ultraviolet ratio. Combined with MUSCLES, Mega-MUSCLES provides SEDs covering a wide range of M dwarf spectral types and ages such that suitable proxies for any M dwarf planet host of interest may be found in our sample. However, we find that ultraviolet and X-ray fluxes can vary even between stars with similar parameters, such that observations of each exoplanet host star will remain the gold standard for interpreting exoplanet atmosphere observations.

David J. Wilson↗
NASA NTRS2007

Real-Time Wireless Data Acquisition System

Current and future aerospace requirements demand the creation of a new breed of sensing devices, with emphasis on reduced weight, power consumption, and physical size. This new generation of sensors must possess a high degree of intelligence to provide critical data efficiently and in real-time. Intelligence will include self-calibration, self-health assessment, and pre-processing of raw data at the sensor level. Most of these features are already incorporated in the Wireless Sensors Network (SensorNet(TradeMark)), developed by the Instrumentation Group at Kennedy Space Center (KSC). A system based on the SensorNet(TradeMark) architecture consists of data collection point(s) called Central Stations (CS) and intelligent sensors called Remote Stations (RS) where one or more CSs can be accommodated depending on the specific application. The CS's major function is to establish communications with the Remote Stations and to poll each RS for data and health information. The CS also collects, stores and distributes these data to the appropriate systems requiring the information. The system has the ability to perform point-to-point, multi-point and relay mode communications with an autonomous self-diagnosis of each communications link. Upon detection of a communication failure, the system automatically reconfigures to establish new communication paths. These communication paths are automatically and autonomously selected as the best paths by the system based on the existing operating environment. The data acquisition system currently under development at KSC consists of the SensorNet(TradeMark) wireless sensors as the remote stations and the central station called the Radio Frequency Health Node (RFHN). The RFF1N is the central station which remotely communicates with the SensorNet(TradeMark) sensors to control them and to receive data. The system's salient feature is the ability to provide deterministic sensor data with accurate time stamps for both time critical and non-time critical applications. Current wireless standards such as Zigbee(TradeMark) and Bluetooth(Registered TradeMark) do not have these capabilities and can not meet the needs that are provided by the SensorNet technology. Additionally, the system has the ability to automatically reconfigure the wireless communication link to a secondary frequency if interference is encountered and can autonomously search for a sensor that was perceived to be lost using the relay capabilities of the sensors and the secondary frequency. The RFHN and the SensorNet designs are based on modular architectures that allow for future increases in capability and the ability to expand or upgrade with relative ease. The RFHN and SensorNet sensors .can also perform data processing which forms a distributed processing architecture allowing the system to pass along information rather than just sending "raw data points" to the next higher level system. With a relatively small size, weight and power consumption, this system has the potential for both spacecraft and aircraft applications as well as ground applications that require time critical data.

Valencia, Emilio J.↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗