Gas turbine performence based creep life estimation using soft computing technique

Accurate and simple prediction system has become an urgent need in most disciplines. Having the accurate prediction system for gas turbine components will allow the user to produce reliable creep life prediction. Focusing on the turbine blades and its life, the current method to calculate its...

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Main Author:	Mohamed Zarti, Almehdi
Format:	Thesis
Language:	English English English
Published:	2012
Subjects:	TJ751-805 Miscellaneous motors and engines Including gas gasoline diesel engines
Online Access:	http://eprints.uthm.edu.my/1888/1/24p%20ALMEHDI%20MOHAMED%20ZARTI.pdf http://eprints.uthm.edu.my/1888/2/ALMEHDI%20MOHAMED%20ZARTI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1888/3/ALMEHDI%20MOHAMED%20ZARTI%20WATERMARK.pdf
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id	my-uthm-ep.1888
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spelling	my-uthm-ep.18882021-10-12T04:27:42Z Gas turbine performence based creep life estimation using soft computing technique 2012-12 Mohamed Zarti, Almehdi TJ751-805 Miscellaneous motors and engines Including gas, gasoline, diesel engines Accurate and simple prediction system has become an urgent need in most disciplines. Having the accurate prediction system for gas turbine components will allow the user to produce reliable creep life prediction. Focusing on the turbine blades and its life, the current method to calculate its creep life is complex and consumes a lot of time. For this reason, the aim of this research is to use an alternative performance–based creep life estimation that is able to provide a quick solution and obtain accurate creep life prediction. By the use of an artificial neural network to predict creep life, a neural network architecture called Sensor Life Based (SLB) architecture that produces a direct mapping from gas path sensor to predict the blade creep life was created by using the gas turbine simulation performance software. The performance of gas turbine and the effects of multiple operations on the blade are studied. The result of the study is used to establish the input and output to train the Sensor Life Based network. The result shows that the Sensor Life-Based architecture is able to produce accurate creep life predictions yet performing rapid calculations. The result also shows that the accuracy of prediction depends on the way, how the gas path sensor is grouped together. 2012-12 Thesis http://eprints.uthm.edu.my/1888/ http://eprints.uthm.edu.my/1888/1/24p%20ALMEHDI%20MOHAMED%20ZARTI.pdf text en public http://eprints.uthm.edu.my/1888/2/ALMEHDI%20MOHAMED%20ZARTI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1888/3/ALMEHDI%20MOHAMED%20ZARTI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TJ751-805 Miscellaneous motors and engines Including gas gasoline diesel engines
spellingShingle	TJ751-805 Miscellaneous motors and engines Including gas gasoline diesel engines Mohamed Zarti, Almehdi Gas turbine performence based creep life estimation using soft computing technique
description	Accurate and simple prediction system has become an urgent need in most disciplines. Having the accurate prediction system for gas turbine components will allow the user to produce reliable creep life prediction. Focusing on the turbine blades and its life, the current method to calculate its creep life is complex and consumes a lot of time. For this reason, the aim of this research is to use an alternative performance–based creep life estimation that is able to provide a quick solution and obtain accurate creep life prediction. By the use of an artificial neural network to predict creep life, a neural network architecture called Sensor Life Based (SLB) architecture that produces a direct mapping from gas path sensor to predict the blade creep life was created by using the gas turbine simulation performance software. The performance of gas turbine and the effects of multiple operations on the blade are studied. The result of the study is used to establish the input and output to train the Sensor Life Based network. The result shows that the Sensor Life-Based architecture is able to produce accurate creep life predictions yet performing rapid calculations. The result also shows that the accuracy of prediction depends on the way, how the gas path sensor is grouped together.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohamed Zarti, Almehdi
author_facet	Mohamed Zarti, Almehdi
author_sort	Mohamed Zarti, Almehdi
title	Gas turbine performence based creep life estimation using soft computing technique
title_short	Gas turbine performence based creep life estimation using soft computing technique
title_full	Gas turbine performence based creep life estimation using soft computing technique
title_fullStr	Gas turbine performence based creep life estimation using soft computing technique
title_full_unstemmed	Gas turbine performence based creep life estimation using soft computing technique
title_sort	gas turbine performence based creep life estimation using soft computing technique
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2012
url	http://eprints.uthm.edu.my/1888/1/24p%20ALMEHDI%20MOHAMED%20ZARTI.pdf http://eprints.uthm.edu.my/1888/2/ALMEHDI%20MOHAMED%20ZARTI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1888/3/ALMEHDI%20MOHAMED%20ZARTI%20WATERMARK.pdf
_version_	1747830874811924480

Gas turbine performence based creep life estimation using soft computing technique

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