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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Shahrood University of Technology</PublisherName>
				<JournalTitle>Journal of Solid and Fluid Mechanics</JournalTitle>
				<Issn>2251-9475</Issn>
				<Volume>6</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2016</Year>
					<Month>09</Month>
					<Day>22</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Using net present value as a single objective function for thermo-economic optimization of a plate-fin recuperator in microturbine</ArticleTitle>
<VernacularTitle>Using net present value as a single objective function for thermo-economic optimization of a plate-fin recuperator in microturbine</VernacularTitle>
			<FirstPage>213</FirstPage>
			<LastPage>226</LastPage>
			<ELocationID EIdType="pii">810</ELocationID>
			
<ELocationID EIdType="doi">10.22044/jsfm.2016.810</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>P.</FirstName>
					<LastName>Maghsoudi</LastName>
<Affiliation></Affiliation>

</Author>
<Author>
					<FirstName>P.</FirstName>
					<LastName>Hanafizadeh</LastName>
<Affiliation></Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>04</Month>
					<Day>24</Day>
				</PubDate>
			</History>
		<Abstract>In this paper, genetic and particle swarm algorithms were applied to optimize a recuperator in a 200 kW microturbine, with the net present value (NPV) considered as the objective function. Examined for the optimization purposes is a plate-fin heat exchanger of offset strip fin configuration as well as counter- and cross-flow arrangements. Fin pitch, fin height, fin offset length, recuperator flow length, recuperator width, and recuperator height were considered as the six design parameters investigated. A sensitivity analysis is performed in order to investigate the effect of design parameters on the objective function. Comparing the results of genetic algorithm(GA) to those of particle swarm algorithm indicated that, the particle swarm optimization(PSO) approach had succeeded to achieve better results from computations’ volume and optimum value perspectives. As the used objective function in this research was a function of total cost and efficiency of the recuperator, the results of the present research were compared to thermo-economic optimization results. Being based on NPV and cycle efficiency criteria, the comparisons indicated superiority of the results of the present study, when viewed from technical and economic perspectives. Results show that in the present study NPV and cycle efficiency have increased up to 11.54% and 11.69%.</Abstract>
			<OtherAbstract Language="FA">In this paper, genetic and particle swarm algorithms were applied to optimize a recuperator in a 200 kW microturbine, with the net present value (NPV) considered as the objective function. Examined for the optimization purposes is a plate-fin heat exchanger of offset strip fin configuration as well as counter- and cross-flow arrangements. Fin pitch, fin height, fin offset length, recuperator flow length, recuperator width, and recuperator height were considered as the six design parameters investigated. A sensitivity analysis is performed in order to investigate the effect of design parameters on the objective function. Comparing the results of genetic algorithm(GA) to those of particle swarm algorithm indicated that, the particle swarm optimization(PSO) approach had succeeded to achieve better results from computations’ volume and optimum value perspectives. As the used objective function in this research was a function of total cost and efficiency of the recuperator, the results of the present research were compared to thermo-economic optimization results. Being based on NPV and cycle efficiency criteria, the comparisons indicated superiority of the results of the present study, when viewed from technical and economic perspectives. Results show that in the present study NPV and cycle efficiency have increased up to 11.54% and 11.69%.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Recuperator</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Net Present Value</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Genetic Algorithm</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Particle Swarm Optimization</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jsfm.shahroodut.ac.ir/article_810_779fe49ed028a4d711e415531462c78a.pdf</ArchiveCopySource>
</Article>
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