Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Sartor, Pia N; Becker, William E.; Worden, Keith; Schmidt, R. Kyle; Bond, David

doi:10.2514/1.c032757

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…However, confronted with the staggering computational cost of a single evaluation, an efficient methodology is sought to explore the design space based on a number of samples. An established methodology to answer the problem at hand is found in the field of surrogate modeling, which is actively used for aerospace applications: calibration of turbulence models [21], modeling of flight data [22,23], uncertainty quantification [24][25][26] and robust [27,28], multi-objective [5], shape [29] and multi-disciplinary [30] optimization. This implies that, after defining the objective function and the design space, a design of experiments (DoE) is set up to select samples in the design space, for which the objective function is subsequently calculated and of which a surrogate is defined.…”

Section: Surrogate Modelingmentioning

confidence: 99%

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

Wauters

Degroote

2021

Journal of Aircraft

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In this paper the application of a wing fence on an unmanned aerial vehicle (UAV) is examined. The UAV under consideration is characterized by flow separation initiating at the tip, leading to a loss of lift and controllability and the appearance of a nose-up pitching moment. A possible solution to this problem is the use of wing fences: plates placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. Firstly, existing theories in regard to the working of wing fences are brought together. Secondly, the sensitivity of stall speed and controllability to the design variables of the wing fence are laid bare. Finally, the aerodynamic and stability characteristics of the UAV as a function of the design variables are assessed. To accomplish the aforementioned three objectives in both an affordable and accurate manner, computational fluid dynamics (CFD) simulations using the − model to correctly model the low Reynolds effects that characterize the flow over a UAV and surrogate modeling in the form of regressive universal co-Kriging are brought together.

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Section: Surrogate Modelingmentioning

confidence: 99%

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

Wauters

Degroote

2021

Journal of Aircraft

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“…A hard landing refers to the phenomenon that the aircraft has an excessive impact on the ground at the moment of landing. The Airbus maintenance manual stipulates that at the moment of aircraft landing, when the normal acceleration or vertical speed exceeds a certain threshold, a hard landing occurs [1]. Hard landing, as an unsafe event in the landing phase of the aircraft, will accelerate the fatigue damage of the landing gear, even leading to the phenomenon of jump landing, causing damages ranging from reduction of fatigue life of landing gear to plane crash.…”

Section: Introductionmentioning

confidence: 99%

An advanced warning model of airplane hard landing based on Adaboost

Zhang

Liu

Huang

et al. 2023

J. Phys.: Conf. Ser.

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This paper breaks through the limitation of establishing early warning logic in the time dimension of the previous hard landing early warning model. In this research, flight altitude has been set as the scale of the corresponding early warning logic. According to the specific characteristics of the original flight parameter data set, the data preprocessing methods have been studied. The application of oversampling and frequency compression methods effectively solved the problems of different sample size and multi-frequency flight parameters in the original data set. On this basis, the method of ensemble learning is used to establish the early warning model of aircraft hard landing based on Adaboost. Further, the model is optimized to ensure the accuracy and generalization ability of the early warning model. Verified by the case study of the actual flight parameter data of Airbus A320 aircraft, the early warning model can realize high-precision prediction of hard landing events, which is of great significance for the improvement of the landing safety level of the aircraft in constructing both.

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“…Sartor [17] used the Bayesian network for flight landing data analysis. Simon [18] presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream.…”

Section: Introductionmentioning

confidence: 99%

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

et al. 2022

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There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.

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Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Cited by 10 publications

References 22 publications

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

Surrogate-Assisted Parametric Study of a Wing Fences for Unmanned Aerial Vehicles

An advanced warning model of airplane hard landing based on Adaboost

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

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