SEARCH · Engineering Papers

Results for “cloud vision”

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.

SourceResearch connectionFrom yearThrough year

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

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

NASA NTRS2010

4-D Cloud Water Content Fields Derived from Operational Satellite Data

In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.

Smith, William L., Jr.↗

NASA NTRS2002

Visions of our Planet's Atmosphere, Land & Oceans

The NASA/NOAA Electronic Theater presents Earth science observations and visualizations in a historical perspective. Fly in from outer space to South Africa, Cape Town and Johannesburg using NASA Terra MODIS data, Landsat data and 1m IKONOS "Spy Satellite" data. Zoom in to any place South Africa using Earth Viewer 3D from Keyhole Inc. and Landsat data at 30 m resolution. Go back to the early weather satellite images from the 1960s and see them contrasted with the latest US and international global satellite weather movies including hurricanes & "tornadoes". See the latest visualizations of spectacular images from NASA/NOAA remote sensing missions like Terra, GOES, TRMM, SeaWiFS, Landsat 7 including 1 - min GOES rapid scan image sequences of Nov 9th 2001 Midwest tornadic thunderstorms and have them explained. See how High-Definition Television (HDTV) is revolutionizing the way we present science to the public. See dust storms and flooding in Africa and smoke plumes from fires in Mexico. See visualizations featured on the covers of Newsweek, TIME, National Geographic, Popular Science & on National & International Network TV. New computer software tools allow us to roam & zoom through massive global images e.g. Landsat tours of the US, and Africa, showing desert and mountain geology as well as seasonal changes in vegetation. See animations of the north and south polar ice packs and with icebergs on the coasts of Greenland and off the coast of Antarctica. Spectacular new visualizations of the global land, atmosphere & oceans are shown. Listen to the pulse of our planet. See how land vegetation, ocean plankton, clouds and temperatures respond to the sun & seasons. See vortexes and currents in the global oceans that bring up the nutrients to feed tiny algae and draw the fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate changes. See the city lights, fishing fleets, gas flares and biomass burning of the Earth at night observed by the "night-vision" DMSP military satellite. The presentation will be made using the latest HDTV and video projection technology that is now done from a laptop computer through an entirely digital path.

Hasler, Arthur F.↗

NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗

NASA NTRSDate not supplied

DRF: A Software Architecture for a Data Marketplace to Support Advanced Air Mobility

Advanced Air Mobility is a new aviation vision, where unmanned aerial systems will trans- port passengers and cargo across urban and rural areas. Critical to the realization of this vision is the development of a digital marketplace, which allows service providers and consumers operating in the airspace ecosystem to securely exchange data and reasoning insights. In this paper, we present the architecture of a decentralized data marketplace that connects data and reasoning service providers to vehicles and other service consumers along the cloud-to-edge continuum. We also present two example use cases to demonstrate the value of our approach.

Autonomy↗

NASA NTRSDate not supplied

Machine Learning Airport Surface Model

Future needs of the National Airspace System require decision support tools to adopt a service-oriented architecture in alignment with the FAA’s vision for an Info-Centric NAS. To achieve this, many existing systems will need to undergo a digital transformation from a monolithic decision support tool to a service-oriented architecture where individual services are exposed through well defined Application Programming Interfaces (APIs). To enable this transformation, NASA has developed the Digital Information Platform as a cloud based foundation for development of aviation services with a special focus towards Artificial Intelligence and Machine Learning (ML) services. This paper describes the work required for the transformation of NASA’s legacy surface management system to a real-time ML based decision support system deployed in the cloud. Details of the Machine Learning Operations (MLOps) infrastructure and best practices are described which enabled the end-toend lifecycle management of ML within an integrated software system. Validation results are provided from an operational field evaluation where performance was benchmarked against the legacy approach.

Jeremy Coupe↗

NASA NTRS2008

Triple F - A Comet Nucleus Sample Return Mission

The Triple F (Fresh From the Fridge) mission, a Comet Nucleus Sample Return, has been proposed to ESA s Cosmic Vision program. A sample return from a comet enables us to reach the ultimate goal of cometary research. Since comets are the least processed bodies in the solar system, the proposal goes far beyond cometary science topics (like the explanation of cometary activity) and delivers invaluable information about the formation of the solar system and the interstellar molecular cloud from which it formed. The proposed mission would extract three samples of the upper 50 cm from three locations on a cometary nucleus and return them cooled to Earth for analysis in the laboratory. The simple mission concept with a touch-and-go sampling by a single spacecraft was proposed as an M-class mission in collaboration with the Russian space agency ROSCOSMOS.

Kueppers, Michael↗

NASA NTRS2008

Triple F - A Comet Nucleus Sample Return Mission

The Triple F (Fresh From the Fridge) mission, a Comet Nucleus Sample Return, has been proposed to ESA's Cosmic Vision program. A sample return from a comet enables us to reach the ultimate goal of cometary research. Since comets are the least processed bodies in the solar system, the proposal goes far beyond cometary science topics (like the explanation of cometary activity) and delivers invaluable information about the formation of the solar system and the interstellar molecular cloud from which it formed. The proposed mission would extract three sample cores of the upper 50 cm from three locations on a cometary nucleus and return them cooled to Earth for analysis in the laboratory. The simple mission concept with a touch-andgo sampling by a single spacecraft was proposed as an M-class mission in collaboration with the Russian space agency ROSCOSMOS.

Kueppers, Michael↗

NASA NTRS2007

Plume Impingement to the Lunar Surface: A Challenging Problem for DSMC

The President's Vision for Space Exploration calls for the return of human exploration of the Moon. The plans are ambitious and call for the creation of a lunar outpost. Lunar Landers will therefore be required to land near predeployed hardware, and the dust storm created by the Lunar Lander's plume impingement to the lunar surface presents a hazard. Knowledge of the number density, size distribution, and velocity of the grains in the dust cloud entrained into the flow is needing to develop mitigation strategies. An initial step to acquire such knowledge is simulating the associated plume impingement flow field. The following paper presents results from a loosely coupled continuum flow solver/Direct Simulation Monte Carlo (DSMC) technique for simulating the plume impingement of the Apollo Lunar module on the lunar surface. These cases were chosen for initial study to allow for comparison with available Apollo video. The relatively high engine thrust and the desire to simulate interesting cases near touchdown result in flow that is nearly entirely continuum. The DSMC region of the flow field was simulated using NASA's DSMC Analysis Code (DAC) and must begin upstream of the impingement shock for the loosely coupled technique to succeed. It was therefore impossible to achieve mean free path resolution with a reasonable number of molecules (say 100 million) as is shown. In order to mitigate accuracy and performance issues when using such large cells, advanced techniques such as collision limiting and nearest neighbor collisions were employed. The final paper will assess the benefits and shortcomings of such techniques. In addition, the effects of plume orientation, plume altitude, and lunar topography, such as craters, on the flow field, the surface pressure distribution, and the surface shear stress distribution are presented.

Lumpkin, Forrest↗

NASA NTRS2020

Possible Climate Histories of Venus Type Worlds

There are two well-known scenarios for Venus’ climate evolution. In one Venus had a long-lived magma ocean phase in its first 100Myr with a steam and CO2 dominated atmosphere. The faint young sun with its high XUV flux would cause photodissociation of the steam atmosphere and hydrodynamic escape would cause most of the hydrogen to escape & left-over oxygen would be absorbed by the magma ocean. Hence Venus would have started out hot and dry and the high D/H ratio measured by Pioneer Venus would be from this period of water loss. The other scenario is that Venus’ magma ocean lifetime would have been roughly the same length of time as Earth’s (~1Myr) and water would have condensed on its surface in its early history and had a short period of habitability before increasing solar insolation through time drove it into a runaway greenhouse. However, results from 2016 showed that if Venus remains in the slowly rotating climate dynamics regime (as seen in exoplanet related climate studies) its cloud albedo feedback would have kept it temperate for possibly billions of years. The only way to confirm which one of these scenarios occurred for Venus is to visit it and make the necessary measurements of noble and volatile gases. But exoplanet observations of young exo-Venus type worlds around young F,G,K dwarf stars may constrain which scenario is more probable for a population of such planets. We present a vision of Venus’ climate history that places it and its exoVenus cousins in an ‘Optimistic Venus Zone’ for ~3 billion years within the conventionally named ‘Venus Zone’ and hence encourage the exoplanet communit as possible habitable environments.

Way, M. J.↗

NASA NTRSDate not supplied

Possible climate histories of Venus type worlds

There are two well-known scenarios for Venus’ climate evolution. In one Venus had a long-lived magma ocean phase in its first 100Myr with a steam and CO2dominated atmosphere[1]. The faint young sun with its high XUV flux would cause photo-dissociation of the steam atmosphere and hydrodynamic escape would cause most of the hydrogen to escape and the left over oxygen would be absorbed by the magma ocean. Hence Venus would have started out hot and dry and the high D/H ratio measured by Pioneer Venus [2] would be from this period of water loss. The other scenario is that Venus’ magma ocean lifetime would have been roughly the same length of time as Earth’s (~1Myr) and water would have condensed on its surface in its early history. As long as Venus remained in the slowly rotating climate dynamics regime [3,4] its cloud albedo feedback would have kept it temperate for possibly billions of years. The only way to confirm which one of these scenarios occurred for Venus is to visit it and make the necessary measurements of noble and volatile gases [5].But exoplanet observations of young exo-Venus type worlds around young F,G,K dwarf stars may constrain whether both scenarios are equally probable for a population of such planets. We present a vision of Venus’ climate history that places it and its exo-Venus cousins in an ‘Optimistic Venus Zone’ within the conventionally named ‘Venus Zone’ [6] and hence encourage the exoplanet community to seek out these worlds as possible habitable environments.

Venus’ climate evolution↗

NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗

NASA NTRS2013

Spinoff 2012

Topics covered include: Water Treatment Technologies Inspire Healthy Beverages; Dietary Formulas Fortify Antioxidant Supplements; Rovers Pave the Way for Hospital Robots; Dry Electrodes Facilitate Remote Health Monitoring; Telescope Innovations Improve Speed, Accuracy of Eye Surgery; Superconductors Enable Lower Cost MRI Systems; Anti-Icing Formulas Prevent Train Delays; Shuttle Repair Tools Automate Vehicle Maintenance; Pressure-Sensitive Paints Advance Rotorcraft Design Testing; Speech Recognition Interfaces Improve Flight Safety; Polymers Advance Heat Management Materials for Vehicles; Wireless Sensors Pinpoint Rotorcraft Troubles; Ultrasonic Detectors Safely Identify Dangerous, Costly Leaks; Detectors Ensure Function, Safety of Aircraft Wiring; Emergency Systems Save Tens of Thousands of Lives; Oxygen Assessments Ensure Safer Medical Devices; Collaborative Platforms Aid Emergency Decision Making; Space-Inspired Trailers Encourage Exploration on Earth; Ultra-Thin Coatings Beautify Art; Spacesuit Materials Add Comfort to Undergarments; Gigapixel Images Connect Sports Teams with Fans; Satellite Maps Deliver More Realistic Gaming; Elemental Scanning Devices Authenticate Works of Art; Microradiometers Reveal Ocean Health, Climate Change; Sensors Enable Plants to Text Message Farmers; Efficient Cells Cut the Cost of Solar Power; Shuttle Topography Data Inform Solar Power Analysis; Photocatalytic Solutions Create Self-Cleaning Surfaces; Concentrators Enhance Solar Power Systems; Innovative Coatings Potentially Lower Facility Maintenance Costs; Simulation Packages Expand Aircraft Design Options; Web Solutions Inspire Cloud Computing Software; Behavior Prediction Tools Strengthen Nanoelectronics; Power Converters Secure Electronics in Harsh Environments; Diagnostics Tools Identify Faults Prior to Failure; Archiving Innovations Preserve Essential Historical Records; Meter Designs Reduce Operation Costs for Industry; Commercial Platforms Allow Affordable Space Research; Fiber Optics Deliver Real-Time Structural Monitoring; Camera Systems Rapidly Scan Large Structures; Terahertz Lasers Reveal Information for 3D Images; Thin Films Protect Electronics from Heat and Radiation; Interferometers Sharpen Measurements for Better Telescopes; and Vision Systems Illuminate Industrial Processes.

Source record↗

NASA NTRS2015

A Vision for an International Multi-Sensor Snow Observing Mission

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

snow↗

NASA NTRSDate not supplied

NASA R&M Efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) Digital Assets

At the intersection of mission, technology, and place is NASA’s need to modernize for a digital-forward future. Digitalization, the process of moving toward digital business, is occurring everywhere and remains an ongoing process across the federal government.”[1] Whereas, Digital Transformation is “employing digitization/digital technologies (e.g., Artificial Intelligence (AI), mobile, cloud, data) to change a process, product, or capability so dramatically (e.g., real-time, intelligent, personalized, anywhere, anytime) that it is unrecognizable compared to its traditional form.” [2] In order to facilitate a digital transformation it is essential for NASA to understand and identify where data exists today and which data are value-needed in the future, understand where there are unfulfilled data needs that limit the advancement of NASA work, and ensure NASA efficiency through Findable, Accessible, Interoperable, and Reusable (FAIR) digital assets in the future. Therefore, NASA’s Reliability & Maintainability (R&M) Enterprise Data Sharing team is working to leverage both Digitization and Digital Transformation to achieve their vision of developing an R&M data discovery framework that enables our community, our partners, and our stakeholders with the ability to efficiently, robustly, and seamlessly access information that enables real-time knowledge and model-based, analytics driven, decision-making impacting R&M. As a result the R&M Enterprise Data Sharing team has conducted a survey of its Reliability, Maintainability, and Availability (RMA) community members to identify data existence (created or used) and where there are corresponding barriers to data acquisition and/or R&M or other issues as shown within this presentation.

Digital Transformation↗

NASA NTRSDate not supplied

Trilateral Task Force – Reliability Analysis Supporting Mission Extension/Post Mission Disposal

Digital Transformation, Reliability Engineering↗

NASA NTRS2021

Applying the Cognitive Space Gateway to Swarm Topologies

NASA's future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize routing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, CubeSat swarm topologies, and cloud services. To test the algorithm in a realistic scenario, the emulated network topology is based on a CubeSat swarm. The swarm may function as a mesh of nodes or as a hub-and-spoke network. An emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. The cloud environment may enable a flexible, lower maintenance approach versus a multi-hop network based in a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

NASA NTRSDate not supplied

Applying the Cognitive Space Gateway to Swarm Topologies

NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

NASA NTRS2016

Optical Extinction Measurements of Dust Density in the GMRO Regolith Test Bin

A regolith simulant test bin was constructed and completed in the Granular Mechanics and Regolith Operations (GMRO) Lab in 2013. This Planetary Regolith Test Bed (PRTB) is a 64 sq m x 1 m deep test bin, is housed in a climate-controlled facility, and contains 120 MT of lunar-regolith simulant, called Black Point-1 or BP-1, from Black Point, AZ. One of the current uses of the test bin is to study the effects of difficult lighting and dust conditions on Telerobotic Perception Systems to better assess and refine regolith operations for asteroid, Mars and polar lunar missions. Low illumination and low angle of incidence lighting pose significant problems to computer vision and human perception. Levitated dust on Asteroids interferes with imaging and degrades depth perception. Dust Storms on Mars pose a significant problem. Due to these factors, the likely performance of telerobotics is poorly understood for future missions. Current space telerobotic systems are only operated in bright lighting and dust-free conditions. This technology development testing will identify: (1) the impact of degraded lighting and environmental dust on computer vision and operator perception, (2) potential methods and procedures for mitigating these impacts, (3) requirements for telerobotic perception systems for asteroid capture, Mars dust storms and lunar regolith ISRU missions. In order to solve some of the Telerobotic Perception system problems, a plume erosion sensor (PES) was developed in the Lunar Regolith Simulant Bin (LRSB), containing 2 MT of JSC-1a lunar simulant. PES is simply a laser and digital camera with a white target. Two modes of operation have been investigated: (1) single laser spot - the brightness of the spot is dependent on the optical extinction due to dust and is thus an indirect measure of particle number density, and (2) side-scatter - the camera images the laser from the side, showing beam entrance into the dust cloud and the boundary between dust and void. Both methods must assume a mean particle size in order to extract a number density. The optical extinction measurement yields the product of the 2nd moment of the particle size distribution and the extinction efficiency Qe. For particle sizes in the range of interest (greater than 1 micrometer), Qe approximately equal to 2. Scaling up of the PES single laser and camera system is underway in the PRTB, where an array of lasers penetrate a con-trolled dust cloud, illuminating multiple targets. Using high speed HD GoPro video cameras, the evolution of the dust cloud and particle size density can be studied in detail.

lunar simulant↗