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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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NASA NTRSDate not supplied

Aero-Propulsive Damping Characterization for eVTOL Aircraft Using Free Motion Wind-Tunnel Testing

This paper describes an electric vertical takeoff and landing (eVTOL) aircraft system identification method applied using three degree-of-freedom (3DOF) free motion wind-tunnel testing. The approach, similar to flight-test system identification, allows for efficient mathematical model development of the aero-propulsive moments applied on an eVTOL vehicle, including aerodynamic damping effects. The approach is demonstrated using a subscale tiltrotor eVTOL aircraft mounted on a new 3DOF wind-tunnel apparatus. To execute the test, a model-based 3DOF control system is designed to track attitude commands and transition the aircraft based on the freestream dynamic pressure. While the flight controller is active, orthogonal phase-optimized multisine inputs are injected into the attitude command and control effector command signals to enable collection of informative data for model identification. Aero-propulsive models are then identified at several reference conditions in the transition flight envelope using the equation-error method in the frequency domain. The identified models are shown to have a good fit to the modeling data and good prediction capability of data not used for model identification. The method yields aerodynamic damping estimates using less wind-tunnel test time compared to traditional forced oscillation experiments and supplements static wind-tunnel testing to produce a comprehensive transition aero-propulsive model suitable for use in flight dynamics simulations.

RAVEN↗

NASA NTRS1990

Development of an adaptive failure detection and identification system for detecting aircraft control element failures

A methodology for designing a failure detection and identification (FDI) system to detect and isolate control element failures in aircraft control systems is reviewed. An FDI system design for a modified B-737 aircraft resulting from this methodology is also reviewed, and the results of evaluating this system via simulation are presented. The FDI system performed well in a no-turbulence environment, but it experienced an unacceptable number of false alarms in atmospheric turbulence. An adaptive FDI system, which adjusts thresholds and other system parameters based on the estimated turbulence level, was developed and evaluated. The adaptive system performed well over all turbulence levels simulated, reliably detecting all but the smallest magnitude partially-missing-surface failures.

Bundick, W. Thomas↗

NASA NTRS1994

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

Kong, Jeffrey↗

NASA NTRSDate not supplied

System Identification for eVTOL Aircraft Using Simulated Flight Data

Explore the source record for details and available documents.

system identification↗

NASA NTRS1991

Joint University Program for Air Transportation Research, 1990-1991

The goals of this program are consistent with the interests of both NASA and the FAA in furthering the safety and efficiency of the National Airspace System. Research carried out at the Massachusetts Institute of Technology (MIT), Ohio University, and Princeton University are covered. Topics studied include passive infrared ice detection for helicopters, the cockpit display of hazardous windshear information, fault detection and isolation for multisensor navigation systems, neural networks for aircraft system identification, and intelligent failure tolerant control.

Morrell, Frederick R.↗

NASA NTRSDate not supplied

Preliminary Steps in Developing Rapid Aero Modeling Technology

The Rapid Aero Modeling (RAM) approach is a method to efficiently and automatically obtain aerodynamic models during testing, significantly saving time and resources. Motivation for this technology results from demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities. These issues are typical in the responses presented by a class of vehicles categorized as Urban Air Mobility aircraft where many features from both airplane and rotorcraft are present. For UAM configurations, with typically many more factors than conventional aircraft, traditional test methods can lead to increased costs and missed interactions. RAM guides the test to obtain high-fidelity, statistically rigorous aircraft models, and the approach is applicable to computational, ground, or flight-test experiments. It combines concepts from Design of Experiment theory and Aircraft System Identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity typically, in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. An initial wind tunnel test to support development of RAM was conducted to assess potential metrics, algorithms, and procedures. This paper presents results from initial tests for the development of RAM technology and highlights some of the unique features of RAM applied toa conventional configuration during a ground-based, static, wind-tunnel test.

Patrick C Murphy↗

NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Computational Experiments

Rapid Aero Modeling (RAM) applied to computational testing, RAM-C, is an approach to efficiently and automatically obtain aerodynamic models during computational investigations. RAM-C is designed to estimate models appropriate for flight dynamics studies and simulations. The approach responds to a demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities associated with hybrid and electric vertical takeoff and landing (eVTOL) aircraft. In an Urban Air Mobility (UAM) transportation system, it is expected that aircraft will embrace many features from both airplanes and rotorcraft. These vehicles present many more factors than conventional aircraft which can lead to increased computational costs and missed key factor interactions when applying traditional testing and modeling methods. RAM-C provides feedback loops around computational codes to rapidly guide testing toward aerodynamic models meeting user-defined fidelity goals. It combines and extends concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM-C only collects enough data required to meet the user-specified prediction error requirements thus saving computational time and resources. The overall achievable fidelity of the final model also depends on the accuracy of the test facility, or in this case, the computational modeling approach. Previous studies to support development of the RAM-T process were conducted in wind tunnel tests to assess potential metrics, algorithms, and procedures. This paper presents results from the next steps taken and tests conducted for the development of RAM-C technology and highlights some of the unique features of RAM applied eVTOL configurations in a computational study.

Aerodynamics↗

NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Wind-Tunnel Tests

Rapid Aero Modeling (RAM) applied to wind tunnel testing, RAM-T, is an approach to efficiently and automatically obtain aerodynamic models during testing. The approach saves time and resources by responding to the demand for experimental efficiency and model fidelity. Motivation for this demand is more acute when investigating a class of vehicles categorized as Urban Air Mobility (UAM) aircraft where many features from both aircraft and rotorcraft are present. RAM-T provides a feedback loop around the test facility to guide the test toward high-fidelity, statistically rigorous aircraft models. The general RAM approach is applicable to computational or physical experiments. It combines concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. This paper presents results from tests conducted for development of an automated RAM-T technology. The results highlight some of the unique features of RAM applied to eVTOL configurations.

Aerodynamics↗

NASA NTRS2022

Rapid Flight Control Law Deployment and Testing Framework for Subscale VTOL Aircraft

A set of procedures was developed to enable rapid flight control law deployment and testing on subscale vertical takeoff and landing (VTOL) aircraft. Low-cost, subscale flight vehicles have become well-suited testbeds for rapid flight dynamics and controls research progression; however, integration of custom flight control laws onto flight hardware has historically been an arduous task. The toolchain described in this report leverages Simulink with the UAV Toolbox, a Pixhawk flight computer running PX4 firmware, and QGroundControl to efficiently design and flight test custom control algorithms. A subscale CL-84 VTOL aircraft was used as a testbed in this investigation to exercise the hardware integration process on a physical model. Implementation of custom attitude stabilization control laws and programmed test input excitations for aircraft system identification were demonstrated using the expeditious hardware integration process. The detailed procedures given in this report are expected to be used in future flight test efforts.

flight testing↗

NASA NTRSDate not supplied

System Identification of Flexible Aircraft: Lessons Learned from the X-56A Phase 1 Flight Tests

The X-56A Multi-Utility Technology Testbed (MUTT) is a subscale airplane that was de-signed as an experimental flight research platform for improving aeroelastic modeling and control technologies. The Phase 1 flight tests, conducted from 2017 to 2019 at the NASA Arm-strong Flight Research Center (AFRC), included 39 flights and approximately 1000 research maneuvers, some of which demonstrated stable closed-loop flight beyond the open-loop flutter speed. This paper summarizes the system identification effort to extract nondimensional stability and control derivatives from the flight test data for constructing aeroelastic models of the flight dynamics. Topics discussed include instrumentation, experiment design, model postulation and reduction, parameter estimation, and others. Throughout the paper, unique challenges for the identification of flexible aircraft, practical aspects of the analysis, and lessons learned are presented.

Jared A Grauer↗

NASA NTRS1975

Rotor systems research aircraft (RSRA) requirements for, and contributions to, rotorcraft state estimation and parameter identification

Rotor System Research Aircraft (RSRA) is designed to provide the capabilities necessary for the effective and efficient in-flight test and verification of promising rotor concepts and supporting technology developments. The RSRA requirements for, and possible contributions to, rotorcraft state estimation and parameter identification technology are discussed.

Condon, G. W.↗

NASA NTRS1983

A Review of System Identification Methods Applied to Aircraft

Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.

Klein, V.↗

NASA NTRSDate not supplied

Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results

Estimating aircraft models from test data requires several simplifying assumptions that introduce biases into the parameter estimates. In this paper, these biases are defined and a method for estimating the biases is discussed. Having an estimate of the bias allows the parameters estimated from test to be integrated into a high-order model. The Integrated Adaptive Wing Technology Maturation (IAWTM) wind tunnel model is discussed and the bias is demonstrated for one of the testing configurations. The methodology was able to estimate these biases and apply corrections to high-order aeroelastic models to improve the fit to test data. The consideration of the biases allows more meaningful comparisons and avoids the erroneous differences between pretest predictions and the fitted model.

Jeffrey Ouellette↗

NASA NTRSDate not supplied

Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results

Jeffrey Ouellette↗

NASA NTRS1995

Flight demonstration of integrated airport surface automation concepts

A flight demonstration was conducted to address airport surface movement area capacity issues by providing pilots with enhanced situational awareness information. The demonstration showed an integration of several technologies to government and industry representatives. These technologies consisted of an electronic moving map display in the cockpit, a Differential Global Positioning System (DGPS) receiver, a high speed VHF data link, an ASDE-3 radar, and the Airport Movement Area Safety System (AMASS). Aircraft identification was presented to an air traffic controller on AMASS. The onboard electronic map included the display of taxi routes, hold instructions, and clearances, which were sent to the aircraft via data link by the controller. The map also displayed the positions of other traffic and warning information, which were sent to the aircraft automatically from the ASDE-3/AMASS system. This paper describes the flight demonstration in detail, along with preliminary results.

Jones, Denise R.↗

NASA NTRS1995

Flight Demonstration of Integrated Airport Surface Automation Concepts

Jones, Denise R.↗

NASA NTRS1998

Flight Demonstration of Integrated Airport Surface Technologies for Increased Capacity and Safety

A flight demonstration was conducted to address airport surface movement area capacity and safety issues by providing pilots with enhanced situational awareness information. The demonstration presented an integration of several technologies to government and industry representatives. These technologies consisted of an electronic moving map display in the cockpit, a Differential Global Positioning system (DGPS) receiver, a high speed very high frequency (VHF) data link, an Airport Surface Detection Equipment (ASDE-3) radar, and the Airport Movement Area Safety System (AMASS). Aircraft identification was presented to an air traffic controller on an AMASS display. The onboard electronic map included the display of taxi routes, hold instructions, and clearances, which were sent to the aircraft via data link by the controller. The map also displayed the positions of other traffic and warning information, which were sent to the aircraft automatically from the ASDE-3/AMASS system. This paper describes the flight demonstration in detail, along with test results.

Jones, Denise R.↗

NASA NTRS1987

Efficient computation of parameter confidence intervals

An important step in system identification of aircraft is the estimation of stability and control derivatives from flight data along with an assessment of parameter accuracy. When the maximum likelihood estimation technique is used, parameter accuracy is commonly assessed by the Cramer-Rao lower bound. It is known, however, that in some cases the lower bound can be substantially different from the parameter variance. Under these circumstances the Cramer-Rao bounds may be misleading as an accuracy measure. This paper discusses the confidence interval estimation problem based on likelihood ratios, which offers a more general estimate of the error bounds. Four approaches are considered for computing confidence intervals of maximum likelihood parameter estimates. Each approach is applied to real flight data and compared.

Murphy, Patrick C.↗