Results for “drones” — Page 16

NASA NTRS2022

Going to Extremes: Architecting Holographic Microscopes for Extreme Environments

Bacterial life exists on earth in extreme environments. These are environments described by large temperature excur- sions, large pressure excursions, and large radiation excursions from the nominal conditions near sea level which are largely populated by humans. These extreme locales represent such places as the ocean ice, the briny pools of Death Valley and deep mines, the acidic hot springs of the High Sierra or Yellowstone, or even the clouds of our upper atmosphere. The preponderance of bacterial life in these extreme environments here on earth suggests that bacterial life might likely exist in the extreme environments of our own solar system, such as the icy moons of Europa or Enceladus. Thus, architecting instruments for detect- ing life in these extreme environments on earth builds confidence that we can architect such instruments for flight missions. In this paper, we discuss our experience with designing digital holo- graphic microscope instruments to enable detection of bacteria in several extreme environments. In particular we discuss three different instruments. The first is our field instrument which is a small, portable instrument for examination of remote sites. The second is submersible instrument which enables exploration of deep aquatic environments and is deployed on a ocean-going drone. The third is a balloon-borne instrument to examine the bacterial content of the upper atmosphere. We will provide a review of each instrument and discuss aspects of instrument engineering for each particular application.

Ramirez, Alex↗

NASA NTRSDate not supplied

Jet Noise Flyover and Scale Model Tests

Renewed interest in commercial supersonic flight has rekindled the need for accurate jet-noise predictions as this source is believed to dominate at aircraft takeoff conditions. The current study compares scale-model data acquired in the NASA Aero-Acoustic Propulsion Laboratory with data obtained using a well-instrumented Learjet 25 in a flyover test completed in September 2022. The flight test included 73 flyovers with engine conditions ranging from 1.5 to 2.0 engine pressure ratios and flight Mach numbers between 0.24 and 0.27. Acoustic data were acquired with an 800-ft linear ground plate microphone array. Wind speed data were acquired up to 1000-ft altitude with a ground-based LiDAR system. Layered ambient temperature, pressure, and humidity were acquired with a weather drone. A 6% increase in the physical scale factor for the scale-model data was found to reasonably align the peak frequencies of the scale-model and flight data and resulted in peak levels for the scale model being roughly 0.7 dB above those for the flight data at NPR = 1.56 and roughly 1 dB below those for the flight data at NPR = 1.91 in the peak jet-noise direction. Comparisons with the SAE ARP876 model were poor especially at emission angles greater than, or equal to, 110° and at high frequencies.

jet noise, supersonic transport↗

NASA NTRSDate not supplied

Jet Noise Flyover and Scale Model Tests

Renewed interest in commercial supersonic flight has rekindled the need for accurate jet-noise predictions as this source is believed to dominate at aircraft takeoff conditions. The current study compares scale-model data acquired in the NASA Aero-Acoustic Propulsion Laboratory with data obtained using a well-instrumented Learjet 25D in a flyover test completed in September 2022. The flight test included 73 flyovers with engine conditions ranging from 1.5 to 2.0 engine pressure ratios and flight Mach numbers between 0.24 and 0.27. Acoustic data were acquired with an 800-ft linear ground plate microphone array. Wind speed data were acquired up to 1000-sft altitude with a ground-based LiDAR system. Layered ambient temperature, pressure, and humidity were acquired with a weather drone. A 6% increase in the physical scale factor for the scale-model data was found to reasonably align the peak frequencies of the scale-model and flight data and resulted in peak levels for the scale model being roughly 0.7 dB above those for the flight data at NPR = 1.56 and roughly 1 dB below those for the flight data at NPR = 1.91 in the peak jet-noise direction. Comparisons with the SAE ARP876 model were poor especially at emission angles greater than, or equal to, 110° and at high frequencies.

Acoustics, jet noise, supersonic transport↗

NASA NTRSDate not supplied

Jet Noise Flyover and Scale Model Tests

Renewed interest in commercial supersonic flight has rekindled the need for accurate jet-noise predictions as this source is believed to dominate at aircraft takeoff conditions. The current study compares scale-model data acquired in the NASA Aero-Acoustic Propulsion Laboratory with data obtained using a well-instrumented Learjet 25 in a flyover test completed in September 2022. The flight test included 73 flyovers with engine conditions ranging from 1.5 to 2.0 engine pressure ratios and flight Mach numbers between 0.24 and 0.27. Acoustic data were acquired with an 800-ft linear ground plate microphone array. Wind speed data were acquired up to 1000-ft altitude with a ground-based LiDAR system. Layered ambient temperature, pressure, and humidity were acquired with a weather drone. A 6% increase in the physical scale factor for the scale-model data was found to reasonably align the peak frequencies of the scale-model and flight data and resulted in peak levels for the scale model being roughly 0.7 dB above those for the flight data at NPR = 1.56 and roughly 1 dB below those for the flight data at NPR = 1.91 in the peak jet-noise direction. Comparisons with the SAE ARP876 model were poor especially at emission angles greater than, or equal to, 110° and at high frequencies.

Acoustics, jet noise, supersonic transport↗

NASA NTRSDate not supplied

An Overview of the NASA Lift+Cruise eVTOL Crash Test

Introduction – NASA RVLT Project Impact Dynamics / Crash Safety Task - Task Objective: “To improve the crashworthiness and impact safety of Urban Air Mobility (UAM) vehicle and provide data to simplify the certification process. Efforts will include development of validated computational models of these vehicles, as well as other impacting bodies such as birds and drones. Efforts will also focus on developing and evaluating energy absorbing and crush properties of emerging and non-traditional composite materials and processes. Finally, occupant protection will be addressed using computational models and physical assets as it pertains to all rotorcraft environments.” - Problem Statement: “There currently is a lack of data for requirements regarding the crashworthy performance of UAM vehicles and impact loads generated by a bird strike. To address this technology gap, NASA will develop test guidelines, adopt modeling methodologies demonstrating capability for ‘certification by analysis’, acquire vehicle and occupant data on full-scale representative vehicles, and provide data/guidance to consensus standards organizations and the UAM community.” - 4 Main focus points - The investigation of occupant injury using physical and computational assets - The development of energy absorbing technology - The generation of data from sub- and full-scale crash test data - The execution of advanced finite element modelling techniques

evtol↗

NASA NTRSDate not supplied

Demonstration of Lidar Sensors for Precision Safe Landing on Planetary Bodies

Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.

Precision Landing↗

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

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↗