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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 451 records · Page 25

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NASA NTRS2011

Mars Exploration Rover Heatshield Observation Campaign

For the first time ever, engineers were able to observe a heatshield on the surface of another planet after a successful entry through the atmosphere. A three-week heatshield observation campaign was conducted in December 2004 after the Mars Exploration Rover Opportunity rover exited "Endurance Crater." By utilizing the rover's scientific instruments, data was collected to make a qualitative assessment of the performance of the heatshield. This data was gathered to gain a better understanding of how the heatshield performed during entry through the Martian atmosphere. In addition, this unprecedented look at the heatshield offered engineers the opportunity to assess if any unexpected anomalies occurred. Once a survey of the heatshield debris was completed, multiple targets of interest were chosen for the collection of imaging data. This data was then used to assess the char depth of the thermal protection material, which compared well with computational predictions. Extensive imaging data was collected and showed the main seal in pristine conditions, and no observable indications of structure overheating. Additionally, unexpected vehicle dynamics during the atmospheric entry were explained by the observation of thermal blanket remnants attached to the heatshield.

MER↗

NASA NTRS2017

'Fly Like This': Natural Language Interface for UAV Mission Planning

With the increasing presence of unmanned aerial vehicles (UAVs) in everyday environments, the user base of these powerful and potentially intelligent machines is expanding beyond exclusively highly trained vehicle operators to include non-expert system users. Scientists seeking to augment costly and often inflexible methods of data collection historically used are turning towards lower cost and reconfigurable UAVs. These new users require more intuitive and natural methods for UAV mission planning. This paper explores two natural language interfaces - gesture and speech - for UAV flight path generation through individual user studies. Subjects who participated in the user studies also used a mouse-based interface for a baseline comparison. Each interface allowed the user to build flight paths from a library of twelve individual trajectory segments. Individual user studies evaluated performance, efficacy, and ease-of-use of each interface using background surveys, subjective questionnaires, and observations on time and correctness. Analysis indicates that natural language interfaces are promising alternatives to traditional interfaces. The user study data collected on the efficacy and potential of each interface will be used to inform future intuitive UAV interface design for non-expert users.

Chandarana, Meghan↗

NASA NTRSDate not supplied

Medium-Sized Helicopter Noise Abatement Flight Test

An extensive flight test campaign was recently completed which aims to reduce the noise generated by helicopters in an effort to improve community acceptance. Using a ground-based microphone array, acoustic measurements were acquired on helicopters in the medium-size vehicle weight class over a number of flight conditions including steady level flight, steady descents, and approaches. While data were collected across four helicopters, the Leonardo AW139 and Sikorsky S-76D will be the focus of this paper. The extent of measured ground noise con- tours was limited due to the site constraints. Thus, the Noise Informed Community Environment Operations Planning System (NICEOPS) in conjunction with the flight test data was used to estimate how changes in the operating procedures impact noise exposure over a wider area during approaches. It was found that even small longitudinal accelerations can have a substantial influence on the noise generation processes during approach and must be modeled appropriately to develop new flight procedures which minimize the acoustic impact on the ground.

Flight Testing↗

NASA NTRS1999

Effects of Command and Control Vehicle (C2V) Operational Environment on Soldier Health and Performance

The purpose of this project was to use NASA technology to assist the US Army in the assessment of motion sickness and performance of soldiers in the Command and Control Vehicle (C2V). Three different vehicle configurations were tested: oblique, (3 seats at a 20-degree angle from the direction of travel); perpendicular, (3 seats at a 90 degree angle); and 4-forward, (all seats faced forward). In all vehicles, the front seat faced forward. Sixteen men and eight women participated for 15 days: 2 days of classroom instruction; 12 days of field tests in the C2V, and 15 minutes of post-field test performance measures. Conditions for field tests were: an initial Park; four Moves (i.e., travel over a mixed terrain); and four Short-halts following movement. NASA task batteries, mood and symptom scales, and physiological data were collected during field tests. Motion sickness symptoms ranging from slight to severe were reported for all subjects. Conclusions were: (1) there was no difference between vehicle configurations; (2) there was a negative impact on crew performance and health when subjects attended to visual screens during vehicle movement; and (3) symptoms and performance degradation were not mitigated by intermittent short-halts.

Cowings, Patricia S.↗

DOE OSTI2026

Irradiation of UC1+x kernels using the MiniFuel vehicle: Microstructure, phase analysis, and initial post-irradiation examination

Uranium carbide is a candidate fuel form for a wide range of advanced reactors, including larger Generation IV reactors as well as small modular reactors and microreactors. However, its commercial deployment timeline faces challenges via traditional qualification approaches. To address this issue, an accelerated fission rate irradiation test was performed to collect basic fuel performance data to inform fuel performance models and potential future integral tests. Hyperstoichiometric UC (UC1+x) kernels were irradiated in the High Flux Isotope Reactor using the MiniFuel irradiation vehicle. The test matrix spanned two temperature regimes (700 °C and 800 °C) and burnup levels (1.8 % FIMA and 2.8 % FIMA). Between 21 and 63 kernels were tested at each unique temperature and burnup condition. As-fabricated microstructural analysis revealed a multiphase composition with UC, UC2, UC2−y, and U-C-O bearing phases for the irradiated kernels. Following irradiation, fission gas release, swelling, and microstructures were analyzed. Measured fission gas release was below 5 % for all irradiation conditions, reaching a maximum at the highest temperature and burnup condition. A binary swelling response was observed; the lower burnup and temperature conditions resulted in negligible swelling, but the higher burnup and temperature conditions produced significant anisotropic swelling and densification in a subset of kernels. The basic microstructural exams of kernels following irradiation were not capable of showing a correlation between kernels that exhibited excessive swelling and those that did not. Characterization of a subset of samples using the Advanced Photon Source and more detailed microstructural examination of unirradiated kernels revealed that a subset of kernels contained very high UC2 phase fractions. The anomalous swelling response is hypothesized to have been driven by this chemical variation. The results of this irradiation highlight the potential of accelerated fission rate irradiation testing to explore such behaviors and inform the development of fuel specifications.

Adorno Lopes, Denise [ORNL] (ORCID:000900023705987↗

DOE OSTI2022

Bounds on Optimal Revisit Times in Persistent Monitoring Missions With a Distinct and Remote Service Station</strong

We report persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. Unmannned aerial vehicles (UAVs) can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this article, we consider the case in which the service station is not colocated with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.

42 ENGINEERING↗

DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗

NASA NTRS1989

B-737 flight test of curved-path and steep-angle approaches using MLS guidance

A series of flight tests were conducted to collect data for jet transport aircraft flying curved-path and steep-angle approaches using Microwave Landing System (MLS) guidance. During the test, 432 approaches comprising seven different curved-paths and four glidepath angles varying from 3 to 4 degrees were flown in NASA Langley's Boeing 737 aircraft (Transport Systems Research Vehicle) using an MLS ground station at the NASA Wallops Flight Facility. Subject pilots from Piedmont Airlines flew the approaches using conventional cockpit instrumentation (flight director and Horizontal Situation Indicator (HSI). The data collected will be used by FAA procedures specialists to develop standards and criteria for designing MLS terminal approach procedures (TERPS). The use of flight simulation techniques greatly aided the preliminary stages of approach development work and saved a significant amount of costly flight time. This report is intended to complement a data report to be issued by the FAA Office of Aviation Standards which will contain all detailed data analysis and statistics.

Branstetter, J. R.↗

NASA NTRS2004

Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation

This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.

Miller, David W.↗

NASA NTRS2016

A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition

The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like Earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like be able to deploy an available fleet of UAVs to fly a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown.

Chandarana, Meghan↗

NASA NTRS2017

ISS Habitability Data Collection and Preliminary Findings

Habitability is the relationship between an individual and their surroundings (i.e. the interplay of the person, machines, environment, and mission). The purpose of this study is to assess habitability and human factors on the ISS to better prepare for future long-duration space flights. Scheduled data collection sessions primarily require the use of iSHORT (iPad app) to capture near real-time habitability feedback and analyze vehicle layout and space utilization.

Thaxton, Sherry↗

NASA NTRS2022

Characterization of Infrared Optical Motion Tracking System in NASA's Simulated Lunar Operations (SLOPE) Laboratory

This work characterizes the accuracy of a 16 camera OptiTrack motion tracking system installed in NASA Glenn Research Center's Simulated Lunar Operations (SLOPE) laboratory. The position of a rigid body mounted on a motorized linear stage is compared to its position reported by the motion tracking system as it travels through the facility's 777m$^3$ capture volume of interest. Experiments show that the mean error reported by the motion tracking system for the aggregate capture volume is in-line with independent measurements collected using the motion stage. Error within regions of the capture volume exceed the mean error reported by the motion tracking system, likely due to occlusion, and suggests that additional cameras should be used to increase measurement accuracy in these regions. Overall, results show that error values reported by the motion tracking system are representative of the measurement error in a collected data set and validates the system's use for characterizing the mobility and tractive performance of robots, rovers, and other vehicles for planetary exploration.

Motion capture↗

DOE OSTI2024

FedFleet 2024: Fleet Data Quality - Understanding and Improving Your Agency's FAST Fleet Data Quality Metric? [Slides]

This presentation presents an overview of how the team supporting the Federal Automotive Statistical Tool (FAST) project for DOE and GSA view and assess the quality of fleet data submissions from federal agencies. It also discusses ways in which agencies can improve the quality of their fleet data and of their fleet data submissions. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗

NASA NTRSDate not supplied

Operational Modal Analysis of the Artemis I Dynamic Rollout Test and Wet Dress Rehearsal

NASA has developed an expendable heavy lift launch vehicle capability, the Space Launch System (SLS), to support lunar and deep space exploration. The uncrewed Artemis I was the first flight of this new launch vehicle and tested critical systems for the upcoming crewed Artemis II flight to the moon. Accelerations were recorded at a multitude of locations on Artemis, the Mobile Launcher (ML), and the Crawler Transporter (CT)during the rollout of Artemis I from the Vehicle Assembly Building (VAB) to Launch Pad 39B March 2022 and is referred to as the Artemis I Dynamic Rollout Test (DRT). While Artemis I was at Launch Pad 39B, the Wet Dress Rehearsal (WDR) was performed to demonstrate launch readiness and acceleration measurements were also recorded. Finally, Artemis I rolled back from Launch Pad 39B to the VAB in April 2022, where acceleration measurements were also recorded and is referred to as the rollback portion of DRT. Because the forces during rollout and at the launch pad acting on Artemis I, the ML, and the CT are not directly measurable, Operational Modal Analysis (OMA) techniques, instead of traditional Experimental Modal Analysis (EMA) techniques, were used to identify modal characteristics. The OMA analysis of DRT and WDR directly builds upon the lessons learned from the OMA analysis of an earlier rollout of the ML from the VAB. DRT and WDR dynamic characteristics will be used to support SLS Integrated Modal Test finite element model correlation efforts and Exploration Ground System ML and CT finite element model verification and validation, which are part of the Building Block approach the Space Launch System program has implemented. The dynamic characteristics extracted from DRT as well as the rollout acceleration time histories themselves will be used in the development of generic rollout forcing functions that will provide refined estimates of the Artemis IV rollout forces, which will have the heavier and larger SLS Block 1B launch vehicle and Mobile Launcher 2 (ML-2). This paper briefly describes Artemis I, the ML, and the CT physical characteristics, DRT rollout/rollback and WDR data collection, the challenges in implementing OMA techniques due in part to the CT harmonics, and how these challenges were overcome to obtain the Artemis I DRT configuration and WDR configuration modal characteristics.

Apollo↗

NASA NTRS1994

Program test objectives milestone 3

The following conclusions have been developed relative to propulsion system technology adequacy for efficient development and operation of recoverable and expendable launch vehicles (RLV and ELV) and the benefits which the integrated propulsion technology demonstrator will provide for enhancing technology: (1) Technology improvements relative to propulsion system design and operation can reduce program cost. Many features or improvement needs to enhance operability, reduce cost, and improve payload are identified. (2) The Integrated Propulsion Technology Demonstrator (IPTD) Program provides a means of resolving the majority of issues associated with improvement needs. (3) The IPTD will evaluate complex integration of vehicle and facility functions in fluid management and propulsion control systems, and provides an environment for validating improved mechanical and electrical components. (4) The IPTD provides a mechanism for investigating operational issues focusing on reducing manpower and time to perform various functions at the launch site. These efforts include model development, collection of data to validate subject models, and ultimate development of complex time line models. (5) The IPTD provides an engine test bed for tri/bi-propellant engine development firings which is representative of the actual vehicle environment. (6) The IPTD provides for only a limited multiengine configuration integration environment for RLV. Multiengine efforts may be simulated for a number of subsystems and a number of subsystems are relatively independent of the multiengine influences.

Gaynor, T. L.↗

NASA NTRS2014

Imagery Integration Team

The Human Exploration Science Office (KX) provides leadership for NASA's Imagery Integration (Integration 2) Team, an affiliation of experts in the use of engineering-class imagery intended to monitor the performance of launch vehicles and crewed spacecraft in flight. Typical engineering imagery assessments include studying and characterizing the liftoff and ascent debris environments; launch vehicle and propulsion element performance; in-flight activities; and entry, landing, and recovery operations. Integration 2 support has been provided not only for U.S. Government spaceflight (e.g., Space Shuttle, Ares I-X) but also for commercial launch providers, such as Space Exploration Technologies Corporation (SpaceX) and Orbital Sciences Corporation, servicing the International Space Station. The NASA Integration 2 Team is composed of imagery integration specialists from JSC, the Marshall Space Flight Center (MSFC), and the Kennedy Space Center (KSC), who have access to a vast pool of experience and capabilities related to program integration, deployment and management of imagery assets, imagery data management, and photogrammetric analysis. The Integration 2 team is currently providing integration services to commercial demonstration flights, Exploration Flight Test-1 (EFT-1), and the Space Launch System (SLS)-based Exploration Missions (EM)-1 and EM-2. EM-2 will be the first attempt to fly a piloted mission with the Orion spacecraft. The Integration 2 Team provides the customer (both commercial and Government) with access to a wide array of imagery options - ground-based, airborne, seaborne, or vehicle-based - that are available through the Government and commercial vendors. The team guides the customer in assembling the appropriate complement of imagery acquisition assets at the customer's facilities, minimizing costs associated with market research and the risk of purchasing inadequate assets. The NASA Integration 2 capability simplifies the process of securing one-of-a-kind imagery assets and skill sets, such as ground-based fixed and tracking cameras, crew-in the-loop imaging applications, and the integration of custom or commercial-off-the-shelf sensors onboard spacecraft. For spaceflight applications, the Integration 2 Team leverages modeling, analytical, and scientific resources along with decades of experience and lessons learned to assist the customer in optimizing engineering imagery acquisition and management schemes for any phase of flight - launch, ascent, on-orbit, descent, and landing. The Integration 2 Team guides the customer in using NASA's world-class imagery analysis teams, which specialize in overcoming inherent challenges associated with spaceflight imagery sets. Precision motion tracking, two-dimensional (2D) and three-dimensional (3D) photogrammetry, image stabilization, 3D modeling of imagery data, lighting assessment, and vehicle fiducial marking assessments are available. During a mission or test, the Integration 2 Team provides oversight of imagery operations to verify fulfillment of imagery requirements. The team oversees the collection, screening, and analysis of imagery to build a set of imagery findings. It integrates and corroborates the imagery findings with other mission data sets, generating executive summaries to support time-critical mission decisions.

Calhoun, Tracy↗

NASA NTRSDate not supplied

Operational Modal Analysis of the Artemis I Dynamic Rollout Test and Wet Dress Rehearsal

Apollo↗

NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗