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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.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Hybrid Monte Carlo Tree Search Approach

We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem is motivated by Strategic deconfliction under the FAA’s “Unmanned Aircraft Systems Traffic Management” Concept of Operations. MRWPDP falls under a class of vehicle routing and scheduling problems, and as such is NP-Hard. In MRWPDP, a graph network is given which consists of depots, drop-off sites, and multiple routes connecting the two. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is to optimally schedule the departure time and assign routes to a known set of vehicles at the depot. We propose a heuristic solution to the problem by borrowing techniques from Mixed Integer Linear Programming (MILP), Constraint Programming, and Monte Carlo Tree Search (MCTS). The resulting hybrid framework is MCTS with Bound-and-Prune (BP) and rapid simulated updates (U), or MCTS-BP-U. This approach is able to quickly provide a feasible solution for MRWPDP, even for large problem instances up to 1000 vehicles. We provide a MILP formulation of MRWPDP and compare its performance against MCTS-BP-U in terms of solution quality. An agent-based model simulation is conducted as a final step to validate the efficacy of our approach.

air traffic scheduling↗
NASA NTRSDate not supplied

An Initial Electric Motor Rotor Vibration Model

Ongoing integration of outrunner brushless electric motors into drones and Advanced Air Mobility aircraft presents a need for accurate acoustic predictions derived from prediction of the motor rotor vibrations. In outrunner motors, the external rotor vibrates and drives the acoustic field. At resonant frequencies of the rotor, the rotor displacements will be the largest. The vibrations can lead to acoustic tones that are relevant to the overall acoustic design of these types of vehicles. This study performs a finite element analysis to assess the modes in two electric motor rotors. This model is then refined into a simple parameterized geometry that can make the same predictions for motors of the same class. A parametric sweep and least-squares-curve fit to the finite element analysis data results in a series of simple curves that can predict the mode shapes and frequencies that are likely to appear in this class of motors without the need for any simulation. The simulations are validated by comparison to acoustic and experimental modal-analysis data.

Electric Motor Noise↗
NASA NTRSDate not supplied

Post-Severe Thunderstorm Damage Assessment from March 2-3, 2020, Nashville, TN, Using Synthetic Aperture Radar Observations

Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.

Hannah G Pankratz↗
NASA NTRSDate not supplied

An Initial Electric Motor Rotor Vibration Model

Ongoing integration of outrunner brushless electric motors into drones and Advanced Air Mobility aircraft presents a need for accurate acoustic predictions derived from prediction of the motor rotor vibrations. In outrunner motors, the external rotor vibrates and drives the acoustic field. At resonant frequencies of the rotor, the rotor displacements will be the largest. The vibrations can lead to acoustic tones that are relevant to the overall acoustic design of these types of vehicles. This study performs a finite element analysis to assess the modes in two electric motor rotors. This model is then refined into a simple parameterized geometry that can make the same predictions for motors of the same class. A parametric sweep and least-squares-curve fit to the finite element analysis data results in a series of simple curves that can predict the mode shapes and frequencies that are likely to appear in this class of motors without the need for any simulation. The simulations are validated by comparison to acoustic and experimental modal-analysis data.

Electric Motor Noise↗
NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modeling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modeling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modeling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modeling languages

Hazard Assessment↗
NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗
NASA NTRSDate not supplied

An Investigation of the Dragonfly Mission Aeroshell/Parachute Dynamics through Subscale Drop Tests

The Dragonfly mission will place a rotorcraft/lander on Titan by 2034. The entry, descent, and landing system of the Dragonfly mission includes two parachutes: a drogue and a main. To provide needed data, subscale drop tests were used to conduct an experimental investigation of the aeroshell/parachute dynamics. The drop tests used a Disk-Gap-Band drogue parachute and two types of Ringslot main parachutes. All tests used a representative aeroshell which included the heatshield. All models were geometrically scaled to 16.7 percent. The model aeroshell had a diameter of 0.75 m. The model parachute nominal diameters were 0.9 m for the drogue and 2.78 m for the main. The aeroshell’s mass properties were dynamically scaled to simulate flight at an altitude of 4 km at Titan. This dynamic scaling allowed the conversion of model test results to full-scale Titan conditions. Onboard instrumentation on the aeroshell provided data on the rotation rates, from which the Euler angles were determined. Tests were conducted by lifting the models with a drone to an altitude of 350 m and dropping them inverted. Key results from these tests were: 1) the models were able to recover from the extreme inverted initial condition and settle to low-amplitude oscillations; 2) ninety nine percent of the time the oscillation amplitudes observed with the drogue parachute were 11.4 degrees or less; 3) ninety nine percent of the time the oscillation amplitudes observed with the 20 percent porosity main parachute were 15.4 degrees or less.

Parachutes↗
NASA NTRS2024

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m 3 and a total area of 0.025 km 2 . ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m 3 and 1,160,000 ± 85,000 m 3 , highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Chunli Dai↗
NASA NTRSDate not supplied

An Investigation of the Dragonfly Mission Aeroshell/Parachute Dynamics through Subscale Drop Tests

The Dragonfly mission will place a rotorcraft/lander on Titan by 2034. The entry, descent, and landing system of the Dragonfly mission includes two parachutes: a drogue and a main. To provide needed data, subscale drop tests were used to conduct an experimental investigation of the aeroshell/parachute dynamics. The drop tests used a Disk-Gap-Band drogue parachute and two types of Ringslot main parachutes. All tests used a representative aeroshell which included the heatshield. All models were geometrically scaled to 16.7 percent. The model aeroshell had a diameter of 0.75 m. The model parachute nominal diameters were 0.9 m for the drogue and 2.78 m for the main. The aeroshell’s mass properties were dynamically scaled to simulate flight at an altitude of 4 km at Titan. This dynamic scaling allowed the conversion of model test results to full-scale Titan conditions. Onboard instrumentation on the aeroshell provided data on the rotation rates, from which the Euler angles were determined. Tests were conducted by lifting the models with a drone to an altitude of 350 m and dropping them inverted. Key results from these tests were: 1) the models were able to recover from the extreme inverted initial condition and settle to low-amplitude oscillations; 2) ninety nine percent of the time the oscillation amplitudes observed with the drogue parachute were 11.4 degrees or less; 3) ninety nine percent of the time the oscillation amplitudes observed with the 20 percent porosity main parachute were 15.4 degrees or less.

Parachutes↗
NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide little recommendation on how to represent the function of the system to perform FHA, or rely on readily-available models with little justification in design theory. This paper presents some of the problems with current modelling languages used for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis, as well as desirable principles an FHA-supporting analysis language should embody. It further introduces the Functional Reasoning Design Language (FRDL), a formal modelling language for describing the functional behaviors of a system and their interactions which satisfies these principles. To demonstrate the use of this language, the modelling and hazard analysis of a disaster response drone is presented.

safety analysis↗
NASA NTRS2024

Discovery and Synchronization Service Architectural Notes

This is a short architectural note for a critical component of safely scaling drone operations. The primary motivation for this note is to collect language and terminology around the Discovery and Synchronization Service (DSS) as it is currently implemented for Uncrewed Aircraft System (UAS) Traffic Management. The major driver for this document was frequent miscommunication about DSS. Often there have been multiple informed and invested stakeholders in the same room making statements about “multiple DSSs” and each of them meaning something slightly different.

uas traffic management↗
NASA NTRSDate not supplied

The LuNaMaps Project: Advancing Capabilities for Developing and Validating Digital Elevation Models of Rocky Surfaces from Orbital Data

Both navigation and surface science can benefit from the ability to generate high resolution and accurate maps of the surface of the Moon and other solar system bodies. The primary way these maps are generated is through the use of orbital imagery and ranging data. Traditionally, the process of using orbital imagery and ranging data is tedious and labor-intensive. Additionally, once maps have been built, there has generally been limited effort in developing standards by which to verify the accuracy and quality of the generated maps. The Lunar Navigation Maps (LuNaMaps) project is a NASA Game Changing Development (GCD) project which over the last 4 years has aimed to address these issues both for the Moon and for other rocky solar system bodies. This has been accomplished through development of new and existing capabilities including: a suite of methods and tools to combine all sources of orbital imagery; a benchmark data set as well as basic requirements for high-fidelity simulations of precision landing functions; tools to synthetically enhance map products with lander-scale features for use in the development and testing of hazard detection systems; methods and tools to evaluate the accuracy of developed digital elevation maps (DEMs) and their quality for use in terrain relative navigation scenarios; and tools to realistically render image and lidar data. In this work, we provide an overview of the capabilities developed through LuNaMaps, demonstrating its use for processing existing lunar data, and describing how it can be applied to other use cases. We additionally provide preliminary results showing the application of the developed tools and processes to the generation of elevation maps of the Lunar Surface Proving Grounds (LSPG) lunar analog at Astrobotic’s Mojave testing facility using “orbital imagery” captured by a drone. In this terrestrial demonstration, we have the benefit of being able to compare the results to a ground truth model of the LSPG. We finally describe plans to use the newly created maps in a terrestrial terrain relative navigation demonstration over the LSPG in early 2025.

optical navigation↗
NASA NTRSDate not supplied

Development of a Compact Lidar Sensor for Terrain Relative Navigation and Terrain Hazard Avoidance

A Lidar sensor utilizing linear-mode flash lidar technology and a novel Super-Resolution technique has been developed for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard landing vehicles. Processing algorithms for precision navigation and safe landing location identification take advantage of the uniform fixed pixels property of generated high resolution Digital Elevation Maps to achieve high reliability operation in near real-time. This paper describes the results of drone and helicopter flight tests of a breadboard system, explains the design and capabilities of a recently built compact prototype unit, and proposes a concepts of operation for future landing missions.

3-D Imaging↗
NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗
NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗
NASA NTRSDate not supplied

High Resolution Terrain Sensing Lidar for Precision Navigation and Safe Landing of Space and Aerial Vehicles

A 3-D imaging flash lidar sensor employing a resolution enhancement algorithm is being developed at NASA Langley Research Center for providing Terrain Relative Navigation and Hazard Avoidance capabilities onboard spacecraft landing on the Moon, Mars, and other planetary bodies. This lidar sensor, we refer to as Terrain Sensing Lidar (TSL), is a solution for future missions that require landing at pre-designated sites near high value resources or at areas of high scientific value, while avoiding hazardous terrain features, such as escarpments, craters, slopes, and rocks, or pre-deployed assets. TSL can also benefit terrestrial applications such as autonomous aerial vehicles without reliance on signals from Global Positioning System (GPS). The feasibility of the TSL concept has been shown through a series of drone, fixed-wing aircraft, and helicopter flight tests. A prototype version of the TSL has been recently assembled for conducting another set of flight tests to demonstrate its readiness for upcoming landing missions. This paper describes the TSL, provides its performance parameters, and explains its operational concepts for landing missions.

Precision Navigation↗
DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗