scholarly journals Determination of longitudinal aerodynamic derivatives using flight data from an icing research aircraft

1989 ◽  
Author(s):  
R. RANAUDO ◽  
A. REEHORST ◽  
T. BOND ◽  
J. BATTERSON ◽  
T. O'MARA
Keyword(s):  
Flight Data ◽  
Aerodynamic Derivatives ◽  
Research Aircraft

scholarly journals Alignment determination of the Hayabusa2 laser altimeter (LIDAR)

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Hirotomo Noda ◽  
Hiroki Senshu ◽  
Koji Matsumoto ◽  
Noriyuki Namiki ◽  
Takahide Mizuno ◽  
...  
Keyword(s):  
Altimeter Data ◽  
Gravity Assist ◽  
Laser Altimeter ◽  
Trajectory Error ◽  
Flight Data ◽  
Camera Image ◽  
Operation Period ◽  
The Cross ◽  
Along Track

AbstractIn this study, we determined the alignment of the laser altimeter aboard Hayabusa2 with respect to the spacecraft using in-flight data. Since the laser altimeter data were used to estimate the trajectory of the Hayabusa2 spacecraft, the pointing direction of the altimeter needed to be accurately determined. The boresight direction of the receiving telescope was estimated by comparing elevations of the laser altimeter data and camera images, and was confirmed by identifying prominent terrains of other datasets. The estimated boresight direction obtained by the laser link experiment in the winter of 2015, during the Earth’s gravity assist operation period, differed from the direction estimated in this study, which fell on another part of the candidate direction; this was not selected in a previous study. Assuming that the uncertainty of alignment determination of the laser altimeter boresight was 4.6 pixels in the camera image, the trajectory error of the spacecraft in the cross- and/or along-track directions was determined to be 0.4, 2.1, or 8.6 m for altitudes of 1, 5, or 20 km, respectively.

GPR-based novel approach for non-linear aerodynamic modelling from flight data

2018 ◽  
Vol 123 (1259) ◽  
pp. 79-92
Author(s):  
A. Kumar ◽  
A. K. Ghosh
Keyword(s):  
Aerodynamic Force ◽  
Equations Of Motion ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Flight Data ◽  
Novel Approach ◽  
Non Linear ◽  
Research Aircraft ◽  
Novel Method ◽  
Aerodynamic Modelling

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

scholarly journals Design and characterization of specMACS, a multipurpose hyperspectral cloud and sky imager

2016 ◽  
Vol 9 (5) ◽  
pp. 2015-2042 ◽  
Author(s):  
Florian Ewald ◽  
Tobias Kölling ◽  
Andreas Baumgartner ◽  
Tobias Zinner ◽  
Bernhard Mayer
Keyword(s):  
Software Design ◽  
Infrared Region ◽  
Central Wavelength ◽  
Laboratory Characterization ◽  
Research Aircraft ◽  
Sky Imager ◽  
Remote Sensing Techniques ◽  
Shortwave Infrared

Abstract. The new spectrometer of the Munich Aerosol Cloud Scanner (specMACS) is a multipurpose hyperspectral cloud and sky imager designated, but is not limited to investigations of cloud–aerosol interactions in Earth's atmosphere. With its high spectral and spatial resolution, the instrument is designed to measure solar radiation in the visible and shortwave infrared region that is reflected from, or transmitted through clouds and aerosol layers. It is based on two hyperspectral cameras that measure in the solar spectral range between 400 and 2500 nm with a spectral bandwidth between 2.5 and 12.0 nm. The instrument was operated in ground-based campaigns as well as aboard the German High Altitude LOng Range (HALO) research aircraft, e.g., during the ACRIDICON-CHUVA campaign in Brazil during summer 2014. This paper describes the specMACS instrument hardware and software design and characterizes the instrument performance. During the laboratory characterization of the instrument, the radiometric response as well as the spatial and spectral resolution was assessed. Since the instrument is primarily intended for retrievals of atmospheric quantities by inversion of radiative models using measured radiances, a focus is placed on the determination of its radiometric response. Radiometric characterization was possible for both spectrometers, with an absolute accuracy of 3 % at their respective central wavelength regions. First measurements are presented which demonstrate the wide applicability of the instrument. They show that key demands are met regarding the radiometric and spectral accuracy which is required for the intended remote sensing techniques.

scholarly journals Calibration of the passive cavity aerosol spectrometer probe for airborne determination of the size distribution

2013 ◽  
Vol 6 (3) ◽  
pp. 4123-4152 ◽  
Author(s):  
Y. Cai ◽  
J. R. Snider ◽  
P. Wechsler
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Scattered Light ◽  
Particle Diameter ◽  
Polystyrene Latex ◽  
Calibration Methods ◽  
Research Aircraft ◽  
Amplifier Gain ◽  
Gain Stage

Abstract. This work describes calibration methods for the particle sizing and particle concentration systems of the passive cavity aerosol spectrometer probe (PCASP). Laboratory calibrations conducted over six years, in support of the deployment of a PCASP on a cloud physics research aircraft, are analyzed. Instead of using the many calibration sizes recommended by the PCASP manufacturer, a relationship between particle diameter and scattered light intensity is established using three sizes of mobility-selected polystyrene latex particles, one for each amplifier gain stage. In addition, studies of two factors influencing the PCASP's determination of the particle size distribution – amplifier baseline and particle shape – are conducted. It is shown that the PCASP-derived size distribution is sensitive to adjustments of the sizing system's baseline voltage, and that for aggregate spheres, a PCASP-derived particle size and a sphere-equivalent particle size agree within uncertainty dictated by the PCASP's sizing resolution. Robust determination of aerosol concentration, and size distribution, also require calibration of the PCASP's aerosol flowrate sensor. Sensor calibrations, calibration drift, and the sensor's non-linear response are documented.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhigang Wang ◽  
Aijun Li ◽  
Lihao Wang ◽  
Xiangchen Zhou ◽  
Boning Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Aerodynamic Parameter ◽  
Content Type ◽  
Flight Data ◽  
Output Error ◽  
Aerodynamic Derivatives ◽  
Levenberg Marquardt ◽  
Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

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