Identification of ground vehicle's aerodynamic derivatives using neural network

Stability of a ground vehicle is dependent on its aerodynamic characteristics when encountered by sudden crosswind disturbances. Aerodynamic side force and yaw moment have been identified as the main influence on the sensitivity of a vehicle towards crosswind, which is largely shape related. A relia...

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Main Author:	Ramli, Nabilah
Format:	Thesis
Language:	English
Published:	2008
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/6942/1/NabilahRamliMFKM2008.pdf
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id	my-utm-ep.6942
record_format	uketd_dc
spelling	my-utm-ep.69422018-10-14T07:19:49Z Identification of ground vehicle's aerodynamic derivatives using neural network 2008-03 Ramli, Nabilah TJ Mechanical engineering and machinery Stability of a ground vehicle is dependent on its aerodynamic characteristics when encountered by sudden crosswind disturbances. Aerodynamic side force and yaw moment have been identified as the main influence on the sensitivity of a vehicle towards crosswind, which is largely shape related. A reliable identification technique is a prerequisite to estimate the aerodynamic side force and the yaw moment. One of the recent techniques in wind-tunnel testing is the use of a pure yawing motion test rig to simulate the transient behavior of a simple vehicle model in crosswind condition. Adapting the stiffness and damping approach, the lateral aerodynamic derivatives are evaluated from the identified systematics frequency and damping of a pure yawing motion. This research explores the alternative identification technique apart from the conventional method of using a spectral density plot to identify the systematics frequency and the logarithmic decrement of peak amplitude for estimating the systemâ€™s damping from a recorded impulse response data. The present study aims to design a multilayer feedforward neural network to carry out the estimation of natural frequency and damping ratio trained with the Bayesian Regularization training algorithm. The network properties studied are necessary to give insight on the optimum network architecture, the suitable input representation and the effect of noise. The possibility of using principal component analysis technique for reducing the network input dimension has also been explored. The results show that the neural network is able to approximate the natural frequency and the damping ratio of an impulse response data and also the ability of the network to handle noisy input data. The application of principal component analysis technique has been shown to reduce the network input dimension while maintaining good estimation results and shortening the network training period. This study demonstrates that the identification of the frequency and the damping of the system can be done using neural network and can be applied to any other similar systems. 2008-03 Thesis http://eprints.utm.my/id/eprint/6942/ http://eprints.utm.my/id/eprint/6942/1/NabilahRamliMFKM2008.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:1258 masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Ramli, Nabilah Identification of ground vehicle's aerodynamic derivatives using neural network
description	Stability of a ground vehicle is dependent on its aerodynamic characteristics when encountered by sudden crosswind disturbances. Aerodynamic side force and yaw moment have been identified as the main influence on the sensitivity of a vehicle towards crosswind, which is largely shape related. A reliable identification technique is a prerequisite to estimate the aerodynamic side force and the yaw moment. One of the recent techniques in wind-tunnel testing is the use of a pure yawing motion test rig to simulate the transient behavior of a simple vehicle model in crosswind condition. Adapting the stiffness and damping approach, the lateral aerodynamic derivatives are evaluated from the identified systematics frequency and damping of a pure yawing motion. This research explores the alternative identification technique apart from the conventional method of using a spectral density plot to identify the systematics frequency and the logarithmic decrement of peak amplitude for estimating the systemâ€™s damping from a recorded impulse response data. The present study aims to design a multilayer feedforward neural network to carry out the estimation of natural frequency and damping ratio trained with the Bayesian Regularization training algorithm. The network properties studied are necessary to give insight on the optimum network architecture, the suitable input representation and the effect of noise. The possibility of using principal component analysis technique for reducing the network input dimension has also been explored. The results show that the neural network is able to approximate the natural frequency and the damping ratio of an impulse response data and also the ability of the network to handle noisy input data. The application of principal component analysis technique has been shown to reduce the network input dimension while maintaining good estimation results and shortening the network training period. This study demonstrates that the identification of the frequency and the damping of the system can be done using neural network and can be applied to any other similar systems.
format	Thesis
qualification_level	Master's degree
author	Ramli, Nabilah
author_facet	Ramli, Nabilah
author_sort	Ramli, Nabilah
title	Identification of ground vehicle's aerodynamic derivatives using neural network
title_short	Identification of ground vehicle's aerodynamic derivatives using neural network
title_full	Identification of ground vehicle's aerodynamic derivatives using neural network
title_fullStr	Identification of ground vehicle's aerodynamic derivatives using neural network
title_full_unstemmed	Identification of ground vehicle's aerodynamic derivatives using neural network
title_sort	identification of ground vehicle's aerodynamic derivatives using neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department	Faculty of Mechanical Engineering
publishDate	2008
url	http://eprints.utm.my/id/eprint/6942/1/NabilahRamliMFKM2008.pdf
_version_	1747814704523247616

Identification of ground vehicle's aerodynamic derivatives using neural network

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