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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Advancements in frequency-domain methods for rotorcraft system identification

A new method for frequency-domain identification of rotorcraft dynamics is presented. Nonparametric frequency-response identification and parametric tranfer-function modeling methods are extended to allow the extraction of state-space (stability and control derivative) representations. An interactive computer program DERIVID is described for the iterative solution of the multi-input/multi-output frequency-response matching approach used in the identification. Theoretical accuracy methods are used to determine the appropriate model structure and degree-of-confidence in the identified parameters. The method is applied to XV-15 tilt-rotor aircraft data in hover. Bare-airframe stability and control derivatives for the lateral/directional dynamics are shown to compare favorably with models previously obtained using time-domain identification methods and the XV-15 simulation program.

Tischler, Mark B.↗
NASA NTRS1989

Advancements in frequency-domain methods for rotorcraft system identification

A new method for frequency-domain identification of rotorcraft dynamics is presented. Nonparametric frequency-response identification and parametric transfer-function modeling methods are extended to allow the extraction of state-space (stability and control derivative) representations. An interactive computer program DERIVID is described for the iterative solution of the multi-input/multi-output frequency-response matching approach used in the identification. Theoretical accuracy methods are used to determine the appropriate model structure and degree-of-confidence in the identified parameters. The method is applied to XV-15 tilt-rotor aircraft data in hover. Bare-airframe stability and control derivatives for the lateral/directional dynamics are shown to compare favorably with models previously obtained using time-domain identification methods and the XV-15 simulation program.

Tischler, Mark B.↗
NASA NTRS1976

Investigation of a Real-time Processing System for the NASA Multifrequency Microwave Radiometer

A study was conducted to investigate the data reduction and processing requirements for the multifrequency microwave radiometer system (MFMR). The objectives were to develop and evaluate algorithms and processing techniques which might provide for dedicated real time or near real time data processing and to develop a configuration design and processor recommendation to accomplish the data reduction. An analysis of the required data reduction and calibration equations was included along with the identification of sources of error which may be present in the (MFMR) data. The definition and evaluation of the significance of effects introduced by aircraft perturbation was given.

Source record↗
NASA NTRS1990

The California corridor transportation system: A design summary

A design group was assembled to find and research criteria relevent to the design of a California Corridor Transportation System. The efforts of this group included defining the problem, conducting a market analysis, formulation of a demand model, identification and evaluation of design drivers, and the systematic development of a solution. The problems of the current system were analyzed and used to determine design drivers, which were divided into the broad categories of cost, convenience, feasibility, environment, safety, and social impact. The relative importance of individual problems was addressed, resulting in a hierarchy of design drivers. Where possible, methods of evaluating the relative merit of proposed systems with respect to each driver were developed. Short takeoff vertical landing aircraft concepts are also discussed for supersonic fighters.

Source record↗
NASA NTRS1977

Remote sensing-aided systems for snow qualification, evapotranspiration estimation, and their application in hydrologic models

The design of general remote sensing-aided methodologies was studied to provide the estimates of several important inputs to water yield forecast models. These input parameters are snow area extent, snow water content, and evapotranspiration. The study area is Feather River Watershed (780,000 hectares), Northern California. The general approach involved a stepwise sequence of identification of the required information, sample design, measurement/estimation, and evaluation of results. All the relevent and available information types needed in the estimation process are being defined. These include Landsat, meteorological satellite, and aircraft imagery, topographic and geologic data, ground truth data, and climatic data from ground stations. A cost-effective multistage sampling approach was employed in quantification of all the required parameters. The physical and statistical models for both snow quantification and evapotranspiration estimation was developed. These models use the information obtained by aerial and ground data through appropriate statistical sampling design.

Korram, S.↗
NASA NTRS1986

Imaging spectrometry: Aircraft and space program

Imaging spectrometry for the remote sensing of the Earth is introduced. Reflected solar energy from the surface is dispersed in a spectrometer and used to form up to 200 registered spectral images. Each pixel has associated with it sufficient information for the reconstruction of a complete reflectance spectrum. The technique allows the diagnostic narrow band spectral features that are characteristic of many surface materials to be used to identify those materials. These spectral features are typically 20 to 40 nm wide; spectral imaging systems which acquire data in contiguous 10 nm bands therefore have sufficient resolution for direct identification of those materials with diagnostic spectral bands.

Abrams, M. J.↗
NASA NTRS1998

A Methodology for Flight-Time Identification of Helicopter-Slung Load Frequency Response Characteristics Using CIFER

Helicopter slung load operations are common in both military and civil contexts. The slung load adds load rigid body modes, sling stretching, and load aerodynamics to the system dynamics, which can degrade system stability and handling qualities, and reduce the operating envelope of the combined system below that of the helicopter alone. Further, the effects of the load on system dynamics vary significantly among the large range of loads, slings, and flight conditions that a utility helicopter will encounter in its operating life. In this context, military helicopters and loads are often qualified for slung load operations via flight tests which can be time consuming and expensive. One way to reduce the cost and time required to carry out these tests and generate quantitative data more readily is to provide an efficient method for analysis during the flight, so that numerous test points can be evaluated in a single flight test, with evaluations performed in near real time following each test point and prior to clearing the aircraft to the next point. Methodology for this was implemented at Ames and demonstrated in slung load flight tests in 1997 and was improved for additional flight tests in 1999. The parameters of interest for the slung load tests are aircraft handling qualities parameters (bandwidth and phase delay), stability margins (gain and phase margin), and load pendulum roots (damping and natural frequency). A procedure for the identification of these parameters from frequency sweep data was defined using the CIFER software package. CIFER is a comprehensive interactive package of utilities for frequency domain analysis previously developed at Ames for aeronautical flight test applications. It has been widely used in the US on a variety of aircraft, including some primitive flight time analysis applications.

Sahai, Ranjana↗
NASA NTRS1983

Filtering flight data prior to aerodynamic system identification

An algorithm for processing flight-test data to provide state estimates and instrument calibrations for aerodynamic or hydrodynamic system identification by the equation error estimation method is developed and demonstrated on synthesized data. The extended-Kalman-filter algorithm employs a locally level, north-pointing frame of reference, accounts for rotating ellipsoidal earth effects, and estimates sensor bias, scale factors, wind components, and process noise levels by maximum-likelihood parameters. The method is found to be most effective with navigation quality inertial input data. The algorithm is applied to data from a six-degree-of-freedom F-4 aircraft simulation and shown to produce state estimates in good agreement with the simulation values.

Trankle, T. L.↗
NASA NTRS1996

X-29A Lateral-Directional Stability and Control Derivatives Extracted From High-Angle-of-Attack Flight Data

The lateral-directional stability and control derivatives of the X-29A number 2 are extracted from flight data over an angle-of-attack range of 4 degrees to 53 degrees using a parameter identification algorithm. The algorithm uses the linearized aircraft equations of motion and a maximum likelihood estimator in the presence of state and measurement noise. State noise is used to model the uncommanded forcing function caused by unsteady aerodynamics over the aircraft at angles of attack above 15 degrees. The results supported the flight-envelope-expansion phase of the X-29A number 2 by helping to update the aerodynamic mathematical model, to improve the real-time simulator, and to revise flight control system laws. Effects of the aircraft high gain flight control system on maneuver quality and the estimated derivatives are also discussed. The derivatives are plotted as functions of angle of attack and compared with the predicted aerodynamic database. Agreement between predicted and flight values is quite good for some derivatives such as the lateral force due to sideslip, the lateral force due to rudder deflection, and the rolling moment due to roll rate. The results also show significant differences in several important derivatives such as the rolling moment due to sideslip, the yawing moment due to sideslip, the yawing moment due to aileron deflection, and the yawing moment due to rudder deflection.

Iliff, Kenneth W.↗
NASA NTRS2004

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

Williams, Peggy S.↗
NASA NTRS2009

The Use of Meteorlogical Data to Improve Contrail Detection in Thermal Imagery over Ireland.

Aircraft induced contrails have been found to have a net warming influence on the climate system, with strong regional dependence. Persistent linear contrails are detectable in 1 Km thermal imagery and, using an automated Contrail Detection Algorithm (CDA), can be identified on the basis of their different properties at the 11 and 12 m w av.el enTgthshe algorithm s ability to distinguish contrails from other linear features depends on the sensitivity of its tuning parameters. In order to keep the number of false identifications low, the algorithm imposes strict limits on contrail size, linearity and intensity. This paper investigates whether including additional information (i.e. meteorological data) within the CDA may allow for these criteria to be less rigorous, thus increasing the contrail-detection rate, without increasing the false alarm rate.

Whelan, Gillian M.↗
NASA NTRS2017

New Flutter Analysis Technique for Time-Domain Computational Aeroelasticity

A new time-domain approach for computing flutter speed is presented. Based on the time-history result of aeroelastic simulation, the unknown unsteady aerodynamics model is estimated using a system identification technique. The full aeroelastic model is generated via coupling the estimated unsteady aerodynamic model with the known linear structure model. The critical dynamic pressure is computed and used in the subsequent simulation until the convergence of the critical dynamic pressure is achieved. The proposed method is applied to a benchmark cantilevered rectangular wing.

HWB aircraft↗
NASA NTRS2017

UAS Well Clear Recovery Against Non-Cooperative Intruders Using Vertical Maneuvers

This paper documents a study that drove the development of a mathematical expression in the minimum operational performance standards (MOPS) of detect-and-avoid (DAA) systems for unmanned aircraft systems (UAS). This equation describes the conditions under which vertical maneuver guidance could be provided during recovery of well clear separation with a non-cooperative VFR aircraft in addition to horizontal maneuver guidance. Although suppressing vertical maneuver guidance in these situations increased the minimum horizontal separation from 500 to 800 feet, the maximum severity of loss of well clear increased in about 35 of the encounters compared to when a vertical maneuver was preferred and allowed. Additionally, analysis of individual cases led to the identification of a class of encounter where vertical rate error had a large effect on horizontal maneuvers due to the difficulty of making the correct left-right turn decision: crossing conflict with intruder changing altitude. These results supported allowing vertical maneuvers when UAS vertical performance exceeds the relative vertical position and velocity accuracy of the DAA tracker given the current velocity of the UAS and the relative vertical position and velocity estimated by the DAA tracker. Looking ahead, these results indicate a need to improve guidance algorithms by utilizing maneuver stability and near mid-air collision risk when determining maneuver guidance to regain well clear separation.

detect and avoid↗
NASA NTRSDate not supplied

Experimental Characterization of an Electric Ducted Fan for the SUSAN 25% Scale Research Vehicle

An electric ducted fan (EDF) was tested in the NASA Langley 12-Foot Low-Speed Tunnel for the SUbsonic Single Aft eNgine (SUSAN) Electrofan 25% scale research vehicle, which incorporates distributed electric fans along the wings and serves as a scaled electrified transport aircraft testbed. The commercial-off-the-shelf EDF was selected as a propulsor that can meet the 25% scale vehicle geometry constraints, along with the performance requirements throughout the flight profile. The test included a series of one-factor-at-a-time (OFAT) and design of experiments (DOE) approaches that were implemented across a range of fan speed, voltage, freestream dynamic pressure, and incidence angle settings to characterize the performance of the EDF. Several test matrix designs and modeling approaches were considered. System identification methods were used to develop a static motor model, a dynamic motor model, an EDF thrust model, and a power consumption model. The model predictive capabilities were then evaluated with validation data not used in the modeling process. The results provide a comprehensive set of models developed to characterize the fan, and offer general insight into EDF modeling approaches.

electric ducted fan↗
NASA NTRS1992

Geologic remote sensing - New technology, new information

Results of geologic studies using data collected by the NASA/JPL Thermal Infrared Imaging Spectrometer (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Airborne Synthetic Aperture Radar (AIRSAR) are discussed. These instruments represent prototypes for the Earth Observing System (EOS) satellite instruments ASTER, High Resolution Imaging Spectrometer (HIRIS), and EOS SAR. Integrated analysis of this data type is one of the keys to successful geologic research using EOS. TIMS links the physical properties of surface materials in the 8-12-*mm region to their composition. Calibrated aircraft data make direct lithological mapping possible. AVIRIS, an analog for HIRIS, provides quantitative information about the surface composition of materials based on their detailed visible and infrared spectral signatures (0.4-2.45 mm). Calibrated AVIRIS data make direct identification of minerals possible. The AIRSAR provides additional complementary information about the surface morphology of rocks and soils.

Kruse, F. A.↗
NASA NTRS2003

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

Smith, Mark S.↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗