Search NASA⌕ Search

SEARCH · Search NASA

Results for “Cloud Architectures”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12

CONSTELL: NASA's Satellite Constellation Model

The CONSTELL program represents an initial effort by the orbital debris modeling group at NASA/JSC to address the particular issues and problems raised by the presence of LEO satellite constellations. It was designed to help NASA better understand the potential orbital debris consequences of having satellite constellations operating in the future in LEO. However, it could also be used by constellation planners to evaluate architecture or design alternatives that might lessen debris consequences for their constellation or lessen the debris effects on other users of space. CONSTELL is designed to perform debris environment projections rapidly so it can support parametric assessments involving either the constellations themselves or the background environment which represents non-constellation users of the space. The projections need to be calculated quickly because a number of projections are often required to adequately span the parameter space of interest. To this end CONSTELL uses the outputs of other NASA debris environment models as inputs, thus doing away with the need for time consuming upfront calculations. Specifically, CONSTELL uses EVOLVE or ORDEM96 debris spatial density results as its background environment, debris cloud snapshot templates to simulate debris cloud propagation, and time dependent orbit profiles of the intact non- functional constellation spacecraft and upper stages. In this paper the environmental consequences of the deployment of particular LEO satellite constellations using the CONSTELL model will be evaluated. Constellations that will undergo a parametric assessment will reflect realistic parameter values. Among other results the increase in loss rate of non-constellation spacecraft, the number of collisions involving constellation elements, and the replacement rate of constellation satellites as a result of debris impact will be presented.

Theall, Jeffrey R.↗

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗

NASA Tech Briefs, October 2006

Topics covered include: Protein Sensors Based on Optical Ring Resonators; Phase Sensor for Aligning a Segmented Telescope Mirror; Control Software for Advanced Video Guidance Sensor; Generating Control Commands From Gestures Sensed by EMG; Multiple-Flat-Panel System Displays Multidimensional Data; 3D X-Ray Luggage-Screening System; Probe Station and Near-Field Scanner for Testing Antennas; Photodetector Arrays for Multicolor Visible/Infrared Imaging; Semiconductor Bolometers Give Background-Limited Performance; Multichannel X-Band Dielectric-Resonator Oscillator; Automatic Alignment of Displacement-Measuring Interferometer; Earth Observing System Data Gateway; Power User Interface; Mercury Shopping Cart Interface; Cassini Archive Tracking System; Architecture Adaptive Computing Environment; Computing Fault Displacements from Surface Deformations; Oxygen-Permeable, Hydrophobic Membranes of Silanized alpha-Al2O3; SiC Composite Turbine Vanes; Retaining Device for the Interior Structure of a Spacecraft Payload; Tool for Torquing Circular Electrical-Connector Collars; System for Continuous Deaeration of Hydraulic Oil; Solar-Powered Cooler and Heater for an Automobile Interior; Improved Oxygen-Beam Texturing of Glucose-Monitoring Optics; Tool for Two Types of Friction Stir Welding; Stationary Apparatus Would Apply Forces of Walking to Feet; Instrument Would Detect and Collect Biological Aerosols; Boundary Condition for Modeling Semiconductor Nanostructures; Miniature Distillation Column for Producing LOX From Air; Even Illumination from Fiber-Optic-Coupled Laser Diodes; Optically Driven Deformable Mirrors; Algorithm for Automated Detection of Edges of Clouds; Exploiting Quantum Resonance to Solve Combinatorial Problems; Hybrid Terrain Database; On Release of Microbe-Laden Particles from Mars Landers; A Concept for Run-Time Support of the Chapel Language; Thermoelectric Inhomogeneities in (Ag(sub 1-y)SbTe2)(sub x)(PbTe)(sub 1-x); and Spacecraft Escape Capsule.

Source record↗

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗

Datacasting V3.0

Datacasting V3.0 provides an RSSbased feed mechanism for publishing the availability of Earth science data records in real time. It also provides a utility for subscribing to these feeds and sifting through all the items in an automatic manner to identify and download the data records that are required for a specific application. Datacasting is a method by which multiple data providers can publish the availability of new Earth science data and users download those files that meet a predefined need; for example, to only download data files related to a specific earthquake or region on the globe. Datacasting is a server-client architecture. The server-side software is used by data providers to create and publish the metadata about recently available data according to the Datacasting RSS (Really Simple Syndication) specification. The client software subscribes to the Datacasting RSS and other RSS-based feeds. By configuring filters associated with feeds, data consumers can use the client to identify and automatically download files that meet a specific need. On the client side, a Datacasting feed reader monitors the server for new feeds. The feed reader will be tuned by the user, via a graphical user interface (GUI), to examine the content of the feeds and initiate a data pull after some criteria are satisfied. The criteria might be, for example, to download sea surface temperature data for a particular region that has cloud cover less than 50% and during daylight hours. After the granule is downloaded to the client, the user will have the ability to visualize the data in the GUI. Based on the popular concept of podcasting, which gives listeners the capability to download only those MP3 files that match their preference, Earth science Datacasting will give users a method to download only the Earth science data files that are required for a particular application.

Bingham, Andrew W.↗

A Science-Driven Mission Concept to An Exoplanet

A concept for a science-driven robotic mission to an exoplanet was developed by a team of scientists and engineers from NASA and academia. The concept and scope were based on key mission and science requirements designed to address the question: “What makes a flight mission to an exoplanet compelling, in terms of science return, compared to what we will be able to learn in the next few decades with large near-Earth telescopes or other remote sensing techniques such as a telescope at the Solar Gravity Lens Focus?” By thinking systematically through mission and science goals and objectives, key requirements were developed that would drive technology developments in all necessary aspects, not just on propulsion. Unique science measurements would be performed en route to the exoplanet, including exploring the environment in the outer regions of our solar system, the Oort Cloud, the local interstellar medium, and the astrospheric environment around the host star. One of the key mission science objectives, and one that addresses why a mission to an exoplanet is compelling, was to confirm and characterize life. This objective is fundamental and drives the need for a precursor exoplanet characterization program to search for Earth-centric biosignatures and also drives key aspects of the mission concept. The team concluded that a direct confirmation of life would require in situ observations and measurements which cannot be performed on a fast (~10% of the speed of light) flyby; thus, the mission would require a method to slow down, orbit, or send a probe to the exoplanet’s surface. This capability drives a trade between interstellar travel velocity, trip duration, and propulsion architecture, as well as a high level of onboard autonomy, including adaptive science data collection, on-board data processing, and analysis. This paper describes our mission concept, the key requirements, and open trades.

Bennett, Gary↗

Zero Trust and Identity Access Management in Support of Service-Based Urban Air Mobility Applications

Urban Air Mobility environments will contain of a collection of service-based services, which will be typically hosted within cloud infrastructures. The underlying data for these UAM services will need to be secured. One approach to securing these UAM services would be to leverage the Zero Trust framework, that focuses on securing services and associated data, instead of securing the network. An early step in moving towards a Zero Trust framework is to standardize identity access manage support for an ever-widening set of services, where users must explicitly be granted access to each service.

UAM↗

Solar Flight on Mars and Venus

Solar powered aircraft are of interest for exploring both Mars and Venus. The thin atmosphere of Mars presents a difficult environment for flying. It is clear that a new approach is needed. By making a totally solar airplane, we can eliminate many of the heavy components, and make an airplane that can fly without fuel. Using high efficiency solar cells, we can succeed with an airplane design that can fly for up to 6 hours in near-equatorial regions of Mars (4 hours of level flight, plus two hours of slow descent), and potentially fly for many days in the polar regions. By designing an airplane for a single day flight. In particular, this change means that we no longer have to cope with the weight of the energy storage system that made previous solar powered airplanes for Mars impractical). The new airplane concept is designed to fly only under the optimal conditions: near equatorial flight, at the subsolar point, near noon. We baseline an 8 kg airplane, with 2 kg margin. Science instruments will be selected with the primary criterion of low mass. Solar-powered aircraft are also quite interesting for the exploration of Venus. Venus provides several advantages for flying a solar-powered aircraft. At the top of the cloud level, the solar intensity is comparable to or greater than terrestrial solar intensities. The atmospheric pressure makes flight much easier than on planets such as Mars. The atmospheric pressure on Venus is presented. From an altitude of approximately 45 km (pressure = 2 bar), to approximately 60 km (pressure = 0.2 bar), terrestrial airplane experience can be easily applied to a Venus airplane design. At these flight altitudes, the temperature varies from 80 C at 45 km, decreasing to -35 C at 60 km. Also, the slow rotation of Venus allows an airplane to be designed for flight within continuous sunlight, eliminating the need for energy storage for nighttime flight. These factors make Venus a prime choice for a long-duration solar-powered aircraft. Fleets of solar-powered aircraft could provide an architecture for efficient and low-cost comprehensive coverage for a variety of scientific missions. Exploratory planetary mapping and atmospheric sampling can lead to a greater understanding of the greenhouse effect not only on Venus but on Earth as well.

Landis, Geoffrey A.↗

Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs).Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the “single-column approximation” which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.

Aman Gupta↗

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗

Enhancements and Evolution of the Real Time Mission Monitor

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. RTMM has proven extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, mission scientists, instrument scientists and program managers alike appreciate the contributions that RTMM makes to their flight projects. We have received numerous plaudits from a wide variety of scientists who used RTMM during recent field campaigns including the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) missions, the 2007-2008 NOAA-NASA Aerosonde Hurricane flights and the 2008 Soil Moisture Active-Passive Validation Experiment (SMAP-VEX). Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated for altitude, latitude, longitude, flight leg distance, cumulative distance, flight leg time, cumulative time, and satellite overpass intersections. The resultant flight plan is then generated in KML and quickly posted to the Google Earth-based RTMM for interested scientists to view the planned flight track and then compare it to the actual real time flight progress. A description of the system architecture, components, and applications along with reviews and animations of RTMM during the field campaigns, plus planned enhancements and future opportunities will be presented.

Goodman, Michael↗

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water

Adams, Ian S.↗

Light, plants, and power for life support on Mars

Regardless of how well other growing conditions are optimized, crop yields will be limited by the available light up to saturation irradiances. Considering the various factors of clouds on Earth, dust storms on Mars, thickness of atmosphere, and relative orbits, there is roughly 2/3 as much light averaged annually on Mars as on Earth. On Mars, however, crops must be grown under controlled conditions (greenhouse or growth rooms). Because there presently exists no material that can safely be pressurized, insulated, and resist hazards of puncture and deterioration to create life support systems on Mars while allowing for sufficient natural light penetration as well, artificial light will have to be supplied. If high irradiance is provided for long daily photoperiods, the growing area can be reduced by a factor of 3-4 relative to the most efficient irradiance for cereal crops such as wheat and rice, and perhaps for some other crops. Only a small penalty in required energy will be incurred by such optimization. To obtain maximum yields, crops must be chosen that can utilize high irradiances. Factors that increase ability to convert high light into increased productivity include canopy architecture, high-yield index (harvest index), and long-day or day-neutral flowering and tuberization responses. Prototype life support systems such as Bios-3 in Siberia or the Mars on Earth Project need to be undertaken to test and further refine systems and parameters.

Non-NASA Center↗

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗

Asteroid Belts in Debris Disk Twins: Vega and Fomalhaut

Vega and Fomalhaut are similar in terms of mass, ages, and global debris disk properties; therefore, they are often referred to as debris disk twins. We present Spitzer 10-35 micrometers spectroscopic data centered at both stars and identify warm, unresolved excess emission in the close vicinity of Vega for the first time. The properties of the warm excess in Vega are further characterized with ancillary photometry in the mid-infrared and resolved images in the far-infrared and submillimeter wavelengths. The Vega warm excess shares many similar properties with the one found around Fomalhaut. The emission shortward of approximately 30 micrometers from both warm components is well described as a blackbody emission of approximately 170 K. Interestingly, two other systems, Eri and HR 8799, also show such an unresolved warm dust using the same approach. These warm components may be analogous to the solar system s zodiacal dust cloud, but of far greater mass (fractional luminosity of approximately 10(exp-5) to 10(exp-6) compared to 10(exp-8) to 10(exp-7). The dust temperature and tentative detections in the submillimeter suggest that the warm excess arises from dust associated with a planetesimal ring located near the water-frost line and presumably created by processes occurring at similar locations in other debris systems as well. We also review the properties of the 2 micrometers hot excess around Vega and Fomalhaut, showing that the dust responsible for the hot excess is not spatially associated with the dust we detected in the warm belt.We suggest it may arise from hot nano grains trapped in the magnetic field of the star. Finally, the separation between the warm and cold belt is rather large with an orbital ratio greater than or approximately 10 in all four systems. In light of the current upper limits on the masses of planetary objects and the large gap, we discuss the possible implications for their underlying planetary architecture and suggest that multiple, low-mass planets likely reside between the two belts in Vega and Fomalhaut.

Su, Kate Y. L.↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA Tech Briefs, June 2008

Topics covered include: Charge-Control Unit for Testing Lithium-Ion Cells; Measuring Positions of Objects Using Two or More Cameras; Lidar System for Airborne Measurement of Clouds and Aerosols; Radiation-Insensitive Inverse Majority Gates; Reduced-Order Kalman Filtering for Processing Relative Measurements; Spaceborne Processor Array; Instrumentation System Diagnoses a Thermocouple; Chromatic Modulator for a High-Resolution CCD or APS; Commercial Product Activation Using RFID; Cup Cylindrical Waveguide Antenna; Aerobraking Maneuver (ABM) Report Generator; ABM Drag_Pass Report Generator; Transformation of OODT CAS to Perform Larger Tasks; Visualization Component of Vehicle Health Decision Support System; Mars Reconnaissance Orbiter Uplink Analysis Tool; Problem Reporting System; G-Guidance Interface Design for Small Body Mission Simulation; DSN Scheduling Engine; Replacement Sequence of Events Generator; Force-Control Algorithm for Surface Sampling; Tool for Merging Proposals Into DSN Schedules; Micromachined Slits for Imaging Spectrometers; Fabricating Nanodots Using Lift-Off of a Nanopore Template; Making Complex Electrically Conductive Patterns on Cloth; Special Polymer/Carbon Composite Films for Detecting SO2; Nickel-Based Superalloy Resists Embrittlement by Hydrogen; Chemical Passivation of Li+-Conducting Solid Electrolytes; Organic/Inorganic Polymeric Composites for Heat-Transfer Reduction; Composite Cathodes for Dual-Rate Li-Ion Batteries; Improved Descent-Rate Limiting Mechanism; Alignment-Insensitive Lower-Cost Telescope Architecture; Micro-Resistojet for Small Satellites; Using Piezoelectric Devices to Transmit Power through Walls; Miniature Latching Valve; Apparatus for Sampling Surface Contamination; Novel Species of Non-Spore-Forming Bacteria; Chamber for Aerosol Deposition of Bioparticles; Hyperspectral Sun Photometer for Atmospheric Characterization and Vicarious Calibrations; Dynamic Stability and Gravitational Balancing of Multiple Extended Bodies; Simulation of Stochastic Processes by Coupled ODE-PDE; Cluster Inter-Spacecraft Communications; Genetic Algorithm Optimizes Q-LAW Control Parameters; Low-Impact Mating System for Docking Spacecraft; Non-Destructive Evaluation of Materials via Ultraviolet Spectroscopy; Gold-on-Polymer-Based Sensing Films for Detection of Organic and Inorganic Analytes in the Air; and Quantum-Inspired Maximizer.

Source record↗

Spinoff 2015

Topics covered include: 3D Endoscope to Boost Safety, Cut Cost of Surgery; Audio App Brings a Better Night's Sleep Liquid Cooling Technology Increases Exercise Efficiency; Algae-Derived Dietary Ingredients Nourish Animals; Space Grant Research Launches Rehabilitation Chair; Vision Trainer Teaches Focusing Techniques at Home; Aircraft Geared Architecture Reduces Fuel Cost and Noise; Ubiquitous Supercritical Wing Design Cuts Billions in Fuel Costs; Flight Controller Software Protects Lightweight Flexible Aircraft; Cabin Pressure Monitors Notify Pilots to Save Lives; Ionospheric Mapping Software Ensures Accuracy of Pilots' GPS; Water Mapping Technology Rebuilds Lives in Arid Regions; Shock Absorbers Save Structures and Lives during Earthquakes; Software Facilitates Sharing of Water Quality Data Worldwide; Underwater Adhesives Retrofit Pipelines with Advanced Sensors; Laser Imaging Video Camera Sees through Fire, Fog, Smoke; 3D Lasers Increase Efficiency, Safety of Moving Machines; Air Revitalization System Enables Excursions to the Stratosphere; Magnetic Fluids Deliver Better Speaker Sound Quality; Bioreactor Yields Extracts for Skin Cream; Private Astronaut Training Prepares Commercial Crews of Tomorrow; Activity Monitors Help Users Get Optimum Sun Exposure; LEDs Illuminate Bulbs for Better Sleep, Wake Cycles; Charged Particles Kill Pathogens and Round Up Dust; Balance Devices Train Golfers for a Consistent Swing; Landsat Imagery Enables Global Studies of Surface Trends; Ruggedized Spectrometers Are Built for Tough Jobs; Gas Conversion Systems Reclaim Fuel for Industry; Remote Sensing Technologies Mitigate Drought; Satellite Data Inform Forecasts of Crop Growth; Probes Measure Gases for Environmental Research; Cloud Computing Technologies Facilitate Earth Research; Software Cuts Homebuilding Costs, Increases Energy Efficiency; Portable Planetariums Teach Science; Schedule Analysis Software Saves Time for Project Planners; Sound Modeling Simplifies Vehicle Noise Management; Custom 3D Printers Revolutionize Space Supply Chain; Improved Calibration Shows Images' True Colors; Micromachined Parts Advance Medicine, Astrophysics, and More; Metalworking Techniques Unlock a Unique Alloy; Low-Cost Sensors Deliver Nanometer-Accurate Measurements; Electrical Monitoring Devices Save on Time and Cost; Dry Lubricant Smooths the Way for Space Travel, Industry; and Compact Vapor Chamber Cools Critical Components.

Source record↗