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403 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Daily, Monthly (Lunar), Yearly, Decadal, Quarter-Century, Half-Century, and Centurial Shoreline Change Rates at the Kennedy Space Center, Cape Canaveral, Florida, USA

The Kennedy Space Center (KSC) is North America’s premier spaceport and provides crucial access to space for both government and private entities. The National Aeronautics and Space Administration (NASA) facilities are clustered along a 12 km active shoreline at Cape Canaveral, Florida, USA, with the entire NASA and US Space Force station (SF) shoreline stretching over 32 km (Figure 1). Several Launch Complexes (LC’s) including the modern heavy lift facilities are only a few tens of meters from the active shoreline and are imminently threatened by continued sea level rise and ongoing coastal erosion. The space center was built on the northern part of Cape Canaveral. Images collected in 1943 show that Apollo Heavy Lift Launch Complexes were built in swales on ground that was at or below sea level with Launch Complex 39B constructed on a paleo inlet (Figures 2 and 3). Today the Heavy Lift Launch Complexes are ~220 meters from the shoreline with the area between the complexes retreating at the highest rates along the cape (Figure 4). To better understand the intersection of environmental concerns, limited budgets for shoreline protection, and sheltering critical infrastructure; NASA, The University of Florida, United States Geological Survey (USGS), National Park Service (NPS) and contractors have and continue to conduct numerous studies to characterize and understand the shoreline evolution and rates of change at KSC. This project aims to better understand and visualize the temporal and spatial variability of change along the NASA shoreline beyond a simple point to point solution.

Richard MacKenzie III↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRSDate not supplied

Communication and Social Science in the Satellite Needs Working Group (SNWG) Assessment

Every two years, NASA conducts an in-depth assessment of the satellite Earth observation data needs of U.S. federal civilian agencies submitted through the Satellite Needs Working Group (SNWG) survey. The SNWG assessment occurs at the nexus of science and people: in the 2022 assessment, over 100 scientists across NASA, NOAA, and USGS were organized to interview over 165 end users at 29 agencies about their unique satellite needs, brainstorm a range of solutions to meet those needs, and communicate back to agencies about resources available for meeting their needs. The innovative approaches to communication, organization, and team make-up that will be described in this talk are vital to the success of the SNWG assessment. As the first major step in evaluating a satellite need, the tri-agency assessment team interviews the agency SMEs who submitted the survey to understand how satellite data could help inform their decision-making process or enable them to fulfill their key responsibilities. In preparation, NASA utilizes social scientists to provide training to all assessment participants on how to hold a discovery-centered interview, including starting with a purpose, creating a welcoming space, exploring all aspects and edges of the need, and brainstorming possible solutions to meet the need. After the interviews, assessment participants propose and review solutions across all thematic areas, seeking those that will help multiple agencies. During the selection process, agencies expected to benefit from a new solution have an opportunity to provide feedback on the proposed activity and are invited to co-design the solution with NASA, should it be implemented. The organization of needs and solutions takes place in Asana, a workflow management tool adapted for the SNWG assessment, and the Report Generation Tool (RGT) enables assessment teams to collaboratively write reports that are returned to each agency with information on current and upcoming resources that help meet their needs.

Katrina Virts↗
NASA NTRSDate not supplied

Medium-Fidelity CFD Modeling of Multicopter Wakes for Airborne Sensor Measurements

A steady-state multicopter simulation capability leveraging automated Cartesian grid generation and a blade element source term rotor model was used to investigate multi-rotor aerodynamics in and out of ground effect. Simulations demonstrated that this model is able to predict reasonably accurate thrust in ground effect for both single and multiple propeller cases. The method yielded accurate lift and drag predictions for a commercial quadcopter when compared to recent wind tunnel test data. This aircraft was then simulated in a variety of flight conditions, including both hover and edgewise forward flight, to determine if generalized guidelines for airborne sensor placement could be developed. Velocity perturbation magnitudes showed large regions of affected air upstream of the vehicle at low speeds, which contracted as the vehicle’s speed was increased. Placing the sensors more than one characteristic length ahead of or above the aircraft reduces errors from rotor-induced flow at higher speeds. For low-speed forward flight, the entrainment of flow into the propeller wakes introduces significant flow turning above the aircraft, suggesting that a forward location, with more moderate perturbations, could be advantageous for these conditions.

ARMD↗
NASA NTRSDate not supplied

Preparing for Europa Clipper Jupiter Observations: Multi-Wavelength Zonal Winds During the New Horizons and Cassini Flybys

We present Jovian zonal wind speeds measured during the Cassini and New Horizons Jupiter flybys in 2000 and 2007, respectively. We performed our cloud tracking wind measurements using an automated, two-dimensional correlation imaging velocimetry technique. We analyzed all LORRI panchromatic images from the New Horizons Jupiter flyby dataset. This LORRI measurement documents the state of Jupiter’s zonal mean wind speed in 2007 and extends the historical record of Jupiter’s winds. Among the Cassini ISS images, we processed the CL1CL2, CB2, UV3, BL1, BL2, GRN, RED, IR1, IR2, IR3, IR4, MT2, and MT3 filters. Our Cassini measurements provide valuable context to understand the altitudes probed by LORRI. Comparing the panchromatic LORRI measurements against past wind measurements using images captured with various narrow and wide-band camera filters is not straightforward. Because the Cassini ISS CL1CL2 “clear” filter’s performance is similar to that of LORRI, comparing CL1CL2 winds against LORRI results will help determine if the New Horizons measurements represent Jupiter’s cloud-top zonal wind speeds or if they are sensitive to different altitudes. In addition to placing our New Horizons measurements in altitudinal context, the Cassini ISS's IR4, IR2, RED, GRN, and BL1 filters are similar to those on Europa Clipper EIS camera. Wind measurements performed using those ISS filters will enable comparison to future missions, including anticipated observations to be taken by Europa Clipper

Jupiter↗