Development of appropriate steam turbine models in Flownex

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dc.contributor.advisorFuls, W Fen_ZA
dc.contributor.authorNeerputh, Rahendra Laljithen_ZA
dc.date.accessioned2015-06-29T07:48:36Z
dc.date.available2015-06-29T07:48:36Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThe Specialization Centre for Energy Efficiency at the University of Cape Town has a goal of building thermo-hydraulic models of an entire power plant. A one-dimensional thermo-hydraulic network solver, Flownex, is the software envisaged to accomplish this goal. The development of appropriate steam turbine models in Flownex supports fulfilment of this goal. Steam turbines of fossil and nuclear power plants make up most of the generating capacity for the majority of industrialised and industrial developing countries, except for those whose power industry depends mainly on hydroelectric power plants [1]. It is therefore a matter of great interest to be ab le to predict the steady state and transient operation of steam turbines. The aim of this dissertation was to use minimal data that was readily available to the end user to develop accurate models. Acceptance test data was used as the primary source because it is more reliable than plant data. Various pressure drop correlations and methods to predict off-design efficiency were investigated. These correlations and methods were solved analytically and implemented in Flownex. Interpretation of the error analysis for the pressure drop correlations established that the general empirical law using inlet conditions and Stodola law in the volume form were the most accurate and consistent in predicting mass flow rate and pressure. The Ray method was shown to be the most accurate to predict off-design efficiency and one of the less complicated to implement. Steady state models were built for four turbine trains using the general empirical and Stodola laws. The results produced by both correlations were similar, showing that for high vacuum conditions either correlation could be used. The general empirical law was the chosen correlation to implement for transient analysis since it was generally more accurate and easier to implement than Stodola. The power predicted by the model was within ±1 % of that of the actual power produced.en_ZA
dc.identifier.apacitationNeerputh, R. L. (2014). <i>Development of appropriate steam turbine models in Flownex</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/13158en_ZA
dc.identifier.chicagocitationNeerputh, Rahendra Laljith. <i>"Development of appropriate steam turbine models in Flownex."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 2014. http://hdl.handle.net/11427/13158en_ZA
dc.identifier.citationNeerputh, R. 2014. Development of appropriate steam turbine models in Flownex. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Neerputh, Rahendra Laljith AB - The Specialization Centre for Energy Efficiency at the University of Cape Town has a goal of building thermo-hydraulic models of an entire power plant. A one-dimensional thermo-hydraulic network solver, Flownex, is the software envisaged to accomplish this goal. The development of appropriate steam turbine models in Flownex supports fulfilment of this goal. Steam turbines of fossil and nuclear power plants make up most of the generating capacity for the majority of industrialised and industrial developing countries, except for those whose power industry depends mainly on hydroelectric power plants [1]. It is therefore a matter of great interest to be ab le to predict the steady state and transient operation of steam turbines. The aim of this dissertation was to use minimal data that was readily available to the end user to develop accurate models. Acceptance test data was used as the primary source because it is more reliable than plant data. Various pressure drop correlations and methods to predict off-design efficiency were investigated. These correlations and methods were solved analytically and implemented in Flownex. Interpretation of the error analysis for the pressure drop correlations established that the general empirical law using inlet conditions and Stodola law in the volume form were the most accurate and consistent in predicting mass flow rate and pressure. The Ray method was shown to be the most accurate to predict off-design efficiency and one of the less complicated to implement. Steady state models were built for four turbine trains using the general empirical and Stodola laws. The results produced by both correlations were similar, showing that for high vacuum conditions either correlation could be used. The general empirical law was the chosen correlation to implement for transient analysis since it was generally more accurate and easier to implement than Stodola. The power predicted by the model was within ±1 % of that of the actual power produced. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Development of appropriate steam turbine models in Flownex TI - Development of appropriate steam turbine models in Flownex UR - http://hdl.handle.net/11427/13158 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13158
dc.identifier.vancouvercitationNeerputh RL. Development of appropriate steam turbine models in Flownex. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Mechanical Engineering, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13158en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Mechanical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMechanical Engineeringen_ZA
dc.titleDevelopment of appropriate steam turbine models in Flownexen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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