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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Probabilistic Surface Characterization for Safe Landing Hazard Detection and Avoidance (HDA)

Apparatuses, systems, computer programs and methods for performing hazard detection and avoidance for landing vehicles are provided. Hazard assessment takes into consideration the geometry of the lander. Safety probabilities are computed for a plurality of pixels in a digital elevation map. The safety probabilities are combined for pixels associated with one or more aim points and orientations. A worst case probability value is assigned to each of the one or more aim points and orientations.

Ivanov, Tonislav I.↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors Mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗
NASA NTRS2022

Validation of the Mars 2020 Fault Protection Design: Navigating the Infinity of the Off-Nominal

On July 30th 2020, the Mars 2020 mission successfully launched out of Cape Canaveral, Florida, passed through the Earth’s shadow, and began its short cruise to Mars. Less than seven months later, the Perseverance rover touched down safely in Jezero Crater to begin its ambitious mission that includes looking for signs of ancient life and collecting samples for future return to Earth. Getting to the successful landing, or “Tango Delta Nominal,” could not have been achieved without also considering the off-nominal. One of the teams supporting this ambitious mission is the fault protection (FP) team. This team is tasked with assessing the various failures, or faults, that could prevent mission success and with ensuring that the autonomous behaviors built into the software and hardware can detect faults and recover the vehicle to a safe state. As part of its charter, the FP team designed a test campaign to provide confidence in the system’s robustness to off-nominal scenarios across all of Mars 2020’s mission phases. The greatest challenge associated with designing such a validation campaign was reducing the infinite number of anomalous scenarios into a finite test suite. In addition, the tests needed to be executed efficiently in order to utilize the team’s limited test venue access, but still needed to maintain a level of rigor that guaranteed confidence in the test outcomes. Given that each test scenario generated massive amounts of data, the team also developed methods for quickly ascertaining whether the autonomous fault protection behaviors maintained vehicle safety in the presence of an anomaly. This paper summarizes the processes that the Mars 2020 fault protection team employed to execute its off-nominal validation campaign. It captures both the methods of generating a suite of off-nominal tests, as well as reducing it to a subset that can be realistically executed within schedule and resource constraints. It also describes the various processes and philosophies that the team utilized to execute the tests efficiently, including creating a standardized procedure template, keeping the test cases modular so that they could be easily interchanged, and capturing common fault injections in a change-controlled database. Finally, it will describe the tools and processes for assessing the test data, focusing in particular on a tool that evaluated vehicle state using “secondary” sources of data to validate that the software had truly configured the spacecraft to the expected safe state.

Morantz, Chaz↗
NASA NTRSDate not supplied

Experimental Testing of Data Fusion in A Distributed Ground-Based Sensing Network for Advanced Air Mobility

Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles(UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with two independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10%increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by one of the two radars.

Federica Vitiello↗
NASA NTRS2025

Demonstration of Data Processing and Fusion from Distributed Radars for AAM Surveillance

Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles (UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with three independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10% increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by two of the radars.

Federica Vitiello↗
NASA NTRS1994

Integrating O/S models during conceptual design, part 3

Space vehicles, such as the Space Shuttle, require intensive ground support prior to, during, and after each mission. Maintenance is a significant part of that ground support. All space vehicles require scheduled maintenance to ensure operability and performance. In addition, components of any vehicle are not one-hundred percent reliable so they exhibit random failures. Once detected, a failure initiates unscheduled maintenance on the vehicle. Maintenance decreases the number of missions which can be completed by keeping vehicles out of service so that the time between the completion of one mission and the start of the next is increased. Maintenance also requires resources such as people, facilities, tooling, and spare parts. Assessing the mission capability and resource requirements of any new space vehicle, in addition to performance specification, is necessary to predict the life cycle cost and success of the vehicle. Maintenance and logistics support has been modeled by computer simulation to estimate mission capability and resource requirements for evaluation of proposed space vehicles. The simulation was written with Simulation Language for Alternative Modeling II (SLAM II) for execution on a personal computer. For either one or a fleet of space vehicles, the model simulates the preflight maintenance checks, the mission and return to earth, and the post flight maintenance in preparation to be sent back into space. THe model enables prediction of the number of missions possible and vehicle turn-time (the time between completion of one mission and the start of the next) given estimated values for component reliability and maintainability. The model also facilitates study of the manpower and vehicle requirements for the proposed vehicle to meet its desired mission rate. This is the 3rd part of a 3 part technical report.

Ebeling, Charles E.↗
NASA NTRS2011

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Gay, Robert S.↗
NASA NTRS2011

Implementation of Pin Point Landing Vision Components in an FPGA System

Pin-point landing is required to enable missions to land close, typically within 10 meters, to scientifically important targets in generally hazardous terrain. In Pin Point Landing both high accuracy and high speed estimation of position and orientation is needed to provide input to the control system to safely choose and navigate to a safe landing site. A proposed algorithm called VISion aided Inertial NAVigation (VISINAV) has shown that the accuracy requirements can be met. [2][3] VISINAV was shown in software only, and was expected to use FPGA enhancements in the future to improve the computational speed needed for pin point landing during Entry Descent and Landing (EDL). Homography, feature detection and spatial correlation are computationally intensive parts of VISINAV. Homography aligns the map image with the descent image so that small correlation windows can be used, and feature detection provides regions that spatial correlation can track from frame to frame in order to estimate vehicle motion. On MER the image Homography, Feature Detection and Correlation would take approximately 650ms tracking 75 features between frames. We implemented Homography, Feature detection and Correlation on a Virtex 4 LX160 FPGA to run in under 25ms while tracking 500 features to improve algorithm reliability and throughput.

aircraft landing guidance↗
NASA NTRS2016

An Inertial Dual-State State Estimator for Precision Planetary Landing with Hazard Detection and Avoidance

The navigation filter architecture successfully deployed on the Morpheus flight vehicle is presented. The filter was developed as a key element of the NASA Autonomous Landing and Hazard Avoidance Technology (ALHAT) project and over the course of 15 free fights was integrated into the Morpheus vehicle, operations, and flight control loop. Flight testing completed by demonstrating autonomous hazard detection and avoidance, integration of an altimeter, surface relative velocity (velocimeter) and hazard relative navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman flter software, and landing within 2 meters of the vertical testbed GPS-based navigation solution at the safe landing site target. Morpheus followed a trajectory that included an ascent phase followed by a partial descent-to-landing, although the proposed filter architecture is applicable to more general planetary precision entry, descent, and landings. The main new contribution is the incorporation of a sophisticated hazard relative navigation sensor-originally intended to locate safe landing sites-into the navigation system and employed as a navigation sensor. The formulation of a dual-state inertial extended Kalman filter was designed to address the precision planetary landing problem when viewed as a rendezvous problem with an intended landing site. For the required precision navigation system that is capable of navigating along a descent-to-landing trajectory to a precise landing, the impact of attitude errors on the translational state estimation are included in a fully integrated navigation structure in which translation state estimation is combined with attitude state estimation. The map tie errors are estimated as part of the process, thereby creating a dual-state filter implementation. Also, the filter is implemented using inertial states rather than states relative to the target. External measurements include altimeter, velocimeter, star camera, terrain relative navigation sensor, and a hazard relative navigation sensor providing information regarding hazards on a map generated on-the-fly.

Bishop, Robert H.↗
NASA NTRS2008

A Self Contained Method for Safe and Precise Lunar Landing

The return of humans to the Moon will require increased capability beyond that of the previous Apollo missions. Longer stay times and a greater flexibility with regards to landing locations are among the many improvements planned. A descent and landing system that can land the vehicle more accurately than Apollo with a greater ability to detect and avoid hazards is essential to the development of a Lunar Outpost, and also for increasing the number of potentially reachable Lunar Sortie locations. This descent and landing system should allow landings in more challenging terrain and provide more flexibility with regards to mission timing and lighting considerations, while maintaining safety as the top priority. The lunar landing system under development by the ALHAT (Autonomous precision Landing and Hazard detection Avoidance Technology) project is addressing this by providing terrain-relative navigation measurements to enhance global-scale precision, an onboard hazard-detection system to select safe landing locations, and an Autonomous GNC (Guidance, Navigation, and Control) capability to process these measurements and safely direct the vehicle to this landing location. This ALHAT landing system will enable safe and precise lunar landings without requiring lunar infrastructure in the form of navigation aids or a priori identified hazard-free landing locations. The safe landing capability provided by ALHAT uses onboard active sensing to detect hazards that are large enough to be a danger to the vehicle but too small to be detected from orbit, given currently planned orbital terrain resolution limits. Algorithms to interpret raw active sensor terrain data and generate hazard maps as well as identify safe sites and recalculate new trajectories to those sites are included as part of the ALHAT System. These improvements to descent and landing will help contribute to repeated safe and precise landings for a wide variety of terrain on the Moon.

Paschall, Stephen C., II↗
NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗
NASA NTRS2009

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Tejada, Arturo↗
NASA NTRS2011

Parametric Modeling of the Safety Effects of NextGen Terminal Maneuvering Area Conflict Scenarios

The goal of this work was to analytically identify and quantify the issues, challenges, technical hurdles, and pilot-vehicle interface issues associated with conflict detection and resolution (CD&R)in emerging operational concepts for a NextGen terminal aneuvering area, including surface operations. To this end, the work entailed analytical and trade studies focused on modeling the achievable safety benefits of different CD&R strategies and concepts in the current and future airport environment. In addition, crew-vehicle interface and pilot performance enhancements and potential issues were analyzed based on review of envisioned NextGen operations, expected equipage advances, and human factors expertise. The results of perturbation analysis, which quantify the high-level performance impact of changes to key parameters such as median response time and surveillance position error, show that the analytical model developed could be useful in making technology investment decisions.

Rogers, William H.↗
NASA NTRS1976

Control elements for an unmanned Martian roving vehicle

The roving vehicle simulator was operated autonomously under control of the simulated on-board computer. With the microwave radar obstacle sensor mounted and operating, it was able to avoid a student placed in its path and to return to the originally assigned direction when that path was clear. The tactile obstacle sensor was able to detect impassable obstacles while allowing the vehicle to negotiate passable obstacles.

Wehe, R. L.↗
NASA NTRS2017

The Demonstration of a Robotic External Leak Locator on the International Space Station

The International Space Station (ISS) and all currently conceivable future manned spacecraft are susceptible to mission impacts due to fluid/gas leaks to the exterior environment. For example, there is a well-known risk of ammonia leaks from the ISS External Thermal Control System loops and currently no method to locate them. It was, therefore, critical to develop a method for detecting and locating leaks to preserve vehicle health. The Robotic External Leak Locator (RELL) was developed and deployed to the ISS to provide this capability. An on-orbit validation and demonstration was successfully completed in December 2016 and leak locating operations occurred in February 2017. This paper discusses the results of those exercises including measurements of the environment around ISS, detection of the small ammonia leak and implementation of leak locating methodologies.

Naids, Adam↗
NASA NTRS2018

The Demonstration of a Robotic External Leak Locator for the International Space Station

The International Space Station (ISS) and all currently conceivable future manned spacecraft are susceptible to mission impacts due to fluid/gas leaks to the exterior environment. For example, there is a well-known risk of ammonia leaks from the ISS External Active Thermal Control System (EATCS) loops and as of 2016 there was no method to locate them. It was, therefore, critical to develop a method for detecting and locating leaks to preserve vehicle health. The Robotic External Leak Locator (RELL) was developed and deployed to the ISS to provide this capability. An on-orbit validation and demonstration was successfully completed in December 2016 and leak locating operations occurred in February 2017. This paper discusses the results of these exercises including measurements of the environment around ISS, detection of a small ammonia leak and implementation of leak locating methodologies. RELL is a collaboration between NASA's Goddard Space Flight Center (GSFC) and Johnson Space Center (JSC) and was launched to the ISS as a Technology Demonstration Payload in December 2015 on Orbital-ATK Commercial Resupply Flight 4.

Naids, Adam J.↗
NASA NTRS2018

Autonomous Nested Search for Hydrothermal Venting

Ocean Worlds in the outer solar system represent one of the best chances for the discovery of extra-terrestrial life. Bodies, such as Europa and Enceladus, are thought to harbor liquid oceans, often encased in a thick icy shell. In order to further investigate these oceans, a new mission concept needs to be developed, a submersible craft. This vehicle would be required to traverse the icy shell and travel hundreds or even thousands of kilometers to survey the ocean below. In doing this, the vehicle might be out of contact for weeks or months at a time. The vehicle must be able to autonomously detect,locate, and study features of interest. One potential target is hydrothermal venting, due to their unique ecosystems on Earth. We have developed an autonomous, nested search strategy to locate sources of hydrothermal venting based on currently used methods. To test this search technique a simulation environment was developed using a hydrothermal plume dispersion simulation and a vehicle model. We show the effectiveness of the search method in this environment.

Seewald, Jeffrey S.↗