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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Automated Purgatoid Identification: Final Report

Driving on Mars is hazardous: technical problems and unforeseen natural hazards can end a mission quickly at the worst, or result in long delays at best. This project is focused on helping to mitigate hazards posed to rovers by purgatoids: small (less than 1 m high, less than 10 m wide), ripple-like eolian bedforms commonly found scattered across the Meridiani Planum region of Mars. Due to the poorly consolidated nature of purgatoids and multiple past episodes of rovers getting stuck in them, identification and avoidance of these eolian bedforms is an important feature of rover path planning (NASA, 2011).

MatLab↗
NASA NTRS2020

Aircraft System Identification from Multisine Inputs and Frequency Responses

The identification of aircraft flight dynamics is often performed using frequency responses, which are nonparametric models that quantify the steady-state magnitude and phase of a dynamic system response to sinusoidal inputs, as a function of frequency. Frequency responses are computed from measured input and output data, and then model parameters, such as stability and control derivatives, are estimated to best fit a parametric model to the empirical frequency response data. The utility of this approach is due to the familiarity of engineers with frequency responses, a number of theoretical and practical advantages under specific conditions, the availability of software packages, and many other reasons.

System identification↗
NASA NTRSDate not supplied

Wind Tunnel-Based Aerodynamic Model Identification for a Tilt-Wing, Distributed Electric Propulsion Aircraft

This paper describes the methodology used to develop a high-fidelity aerodynamic model for the Langley Aerodrome No. 8 (LA-8) tandem tilt-wing, distributed electric propulsion, vertical takeoff and landing aircraft. Electric vertical takeoff and landing (eVTOL) vehicle configurations exhibit aerodynamic characteristics of both fixed-wing and rotary-wing aircraft as well as complex vehicle-specific phenomena, such as propeller-wing interactions and high incidence angle propeller aerodynamics. Consequently, conventional aircraft aerodynamic modeling strategies require modification when applied to eVTOL aircraft. Two novel system identification-based approaches are used to develop an aerodynamic model for the LA-8 aircraft configuration using wind tunnel data collected with design of experiments techniques. The modeling strategies are compared by assessing their predictive performance for validation data acquired separately from the data used to identify the model and are shown to have sufficient predictive capability. Research findings are presented with a discussion of unique eVTOL aerodynamic modeling characteristics and practical strategies to inform future aerodynamic modeling efforts for eVTOL aircraft.

Urban Air Mobility↗
NASA NTRSDate not supplied

System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncoming airflow. In oblique flow, propellers exhibit deviations in thrust and torque oriented along the propeller axis of rotation, as well as significant off-axis forces and moments. Although important for modeling eVTOL aircraft aerodynamics, sparse experimental data or mathematical models exist for propellers at incidence. This paper describes a propulsion system modeling methodology for the Langley Aerodrome No. 8 (LA-8) tandem tilt-wing, eVTOL aircraft. System identification methods are applied to isolated propeller wind tunnel data gathered across the vehicle's flight envelope to develop a mathematical model of the propulsion system, including a static motor model, dynamic motor model, and propeller aerodynamic model. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. The results are followed by a discussion of model implementation strategies into high-fidelity eVTOL aircraft simulations.

system identification↗
NASA NTRS2021

Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.

machine learning↗
NASA NTRSDate not supplied

Interval Predictor Models for Robust System Identification

This paper proposes a framework for the identification and uncertainty quantification of plant models according to multivariable data. The only restriction imposed upon such models is for their outputs to depend continuously on their parameters. An Interval Predictor Model (IPM) prescribes the parameters of a computational model as a path-connected set thereby making each predicted output an interval-valued function of its inputs. The formulation proposed seeks the parameter set for which the predicted outputs tightly enclose the data. This set, which is modeled as a semi-algebraic set of low-degree polynomials, enables the characterization of possibly strong parameter dependencies commonly found in practice. This uncertainty characterization makes the resulting plant model amenable to robust control approaches using polynomial optimization. Furthermore, we use non-convex scenario theory to assess the reliability of the resulting IPM. This assessment yields a distribution-free upper bound on the probability that future data will fall outside the predicted intervals.

interval↗
NASA NTRS2021

System Identification for Propellers at High Incidence Angles

Propellers used for electric vertical takeoff and landing (eVTOL) aircraft propulsion systems experience a wide range of aerodynamic conditions, including large incidence angles relative to oncoming airflow. In oblique flow, propellers exhibit deviations in thrust and torque oriented along the propeller axis of rotation, as well as significant off-axis forces and moments. Although important for modeling eVTOL aircraft aerodynamics, sparse experimental data or mathematical models exist for propellers at incidence. This paper describes a propulsion system modeling methodology for the LA-8 tandem tilt-wing, eVTOL aircraft. System identification methods are applied to isolated propeller wind tunnel data gathered across the vehicle's flight envelope to develop a mathematical model of the propulsion system, including a static motor model, dynamic motor model, and propeller aerodynamic model. Modeling results validated against data withheld from the modeling process indicate good predictive capability and agree with theoretical expectations. The results are followed by a discussion of model implementation strategies into high-fidelity eVTOL aircraft simulations.

system identification↗
NASA NTRSDate not supplied

Recent System Identification Research at NASA Langley Research Center

This talk summarizes some of the recent advances in system identification at NASA Langley Research Center. Efforts discussed were applied to aeroelastic models, aircraft with redundant inputs and feedback control active, and aircraft flying in turbulence. Topics mentioned include experiment design with orthogonal multisine inputs, frequency response estimation, maximum likelihood parameter estimation, and parameter estimation considering process noise.

NASA LaRC↗
NASA NTRS2023

Advances in Aircraft System Identification at NASA Langley Research Center

Advances in aircraft system identification at NASA Langley Research Center are discussed. The relevant time period includes the years since the last summary paper of this kind, which was published in the Journal of Aircraft in 2005. Research advances were achieved in flight test experiment design, frequency-domain modeling, real-time autonomous global modeling, rapid simulation development and updating, dynamic modeling in turbulence, flight data corrections, model uncertainty characterization, and aeroelastic modeling using distributed sensing. Possible future developments in the field are identified.

Aircraft system identification↗
NASA NTRS2024

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

system identification↗
NASA NTRSDate not supplied

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification(Presentation)

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

flight test↗
DOE OSTI2022

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification: Preprint

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs)at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases - distribution system state estimation (DSSE) and fault identification - are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system monitoring↗
DOE OSTI2023

3D Virtual Simulation for Radiation Safety and Hazards Identification Training

This presentation will be presented at CAES codebreaker session on May 4th. The slides include two projects that demonstrate how virtual training can benefit from visualization technology. One is a collaboration project between the Applied Visualization Laboratory and College of Eastern Idaho for radiation safety and survey training. The other one is Idaho National Laboratory's hazards identification online training that utilized LiDAR technology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Characterization of LIBS emission lines for the identification of chlorides, carbonates, and sulfates in salt/basalt mixtures for the application to MSL ChemCam data: LIBS OF CL, C, S IN SALT-BASALT MIXTURES

Ancient environmental conditions on Mars can be probed through the identification of minerals on its surface, including water-deposited salts and cements dispersed in the pore space of sedimentary rocks. Laser-induced breakdown spectroscopy (LIBS) analyses by the Martian rover Curiosity's ChemCam instrument can indicate salts, and ChemCam surveys aid in identifying and selecting sites for further, detailed in situ analyses. Here, we performed laboratory LIBS experiments under simulated Mars conditions with a ChemCam-like instrument on a series of mixtures containing increasing concentrations of salt in a basaltic background to investigate the potential for identifying and quantifying chloride, carbonate, and sulfate salts found only in small amounts, dispersed in bulk rock with ChemCam, rather than concentrated in veins. The data then indicate that the presence of emission lines from the basalt matrix limited the number of Cl, C, and S emission lines found to be useful for quantitative analysis; nevertheless, several lines with intensities sensitive to salt concentration were identified. Detection limits for the elements based on individual emission lines ranged from ~20 wt % carbonate (2 wt % C), ~5–30 wt % sulfate (1–8 wt % S), and ~5–10 wt % chloride (3–6 wt % Cl) depending on the basaltic matrix and/or salt cation. Absolute quantification of Cl, C, and S in the samples via univariate analysis depends on the cation-anion pairing in the salt but appears relatively independent of matrices tested, following normalization. Our results are promising for tracking relative changes in the salt content of bulk rock on the Martian surface with ChemCam.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning

Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

36 MATERIALS SCIENCE↗