Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks
A Master of Science Thesis in Mechanical Engineering submitted by Amin Moh'd AlSharif entitled, "Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks," submitted in May 2011. Available are both soft and hard copies of the thesis.
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| Format: | doctoralThesis |
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2011
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| Online Access: | http://hdl.handle.net/11073/2735 |
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| _version_ | 1864513442851848192 |
|---|---|
| author | AlSharif, Amin Moh'd |
| author_facet | AlSharif, Amin Moh'd |
| author_role | author |
| dc.contributor.none.fl_str_mv | Ahmed, Saad El Kadi, Hany |
| dc.creator.none.fl_str_mv | AlSharif, Amin Moh'd |
| dc.date.none.fl_str_mv | 2011-09-18T11:28:42Z 2011-09-18T11:28:42Z 2011-05 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2011.14 http://hdl.handle.net/11073/2735 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | neural networks Combustion engineering Jets Fluid dynamics Turbulence Mathematical models Combustion Research |
| dc.title.none.fl_str_mv | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science Thesis in Mechanical Engineering submitted by Amin Moh'd AlSharif entitled, "Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks," submitted in May 2011. Available are both soft and hard copies of the thesis. |
| format | doctoralThesis |
| id | aus_f7b2ff451cb7b24e6f049d4a5f56bf9d |
| identifier_str_mv | 35.232-2011.14 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/2735 |
| publishDate | 2011 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural NetworksAlSharif, Amin Moh'dneural networksCombustion engineeringJetsFluid dynamicsTurbulenceMathematical modelsCombustionResearchA Master of Science Thesis in Mechanical Engineering submitted by Amin Moh'd AlSharif entitled, "Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks," submitted in May 2011. Available are both soft and hard copies of the thesis.The flowfield characteristics downstream of an axisymmetric suddenexpansion dump combustor model are important to designers of gas turbines and liquid-fuel ramjets ducted rockets. Many experimental techniques such as Laser Doppler Velocimetry (LDV) measurements provide only limited discrete information at given points; especially, for the cases of complex flows such as swirling flows of a dump combustor. For these types of flows, usual numerical interpolating schemes appear to be unsuitable. Artificial Neural Networks (ANN) methods are thus proposed as an alternative and the flow predictions obtained are tested and presented in this thesis. To predict the velocity components and the turbulence statistics obtained experimentally under a variety of swirl numbers, the use of a variety of ANN architectures is investigated. In each case, the predictions obtained are compared with published experimental data to determine the ANN structure that predicts the flow parameters most accurately. Moreover, the generated data is used to provide contour and surface plots to show the detailed flow characteristics throughout the model. The examined turbulence statistics are fluid flow velocity components in axial, radial, and tangential directions, in addition to Reynolds shear and normal stresses. Also triple velocity correlations are scrutinized. The investigation of ANN architecture variation shows that generalized feedforward network (GFF) with one hidden layer is the most efficient network, in which most turbulence statistics are predicted accurately. Moreover, the study shows that GFF network performs better when its built architecture uses Levenberg-Marquardt learning rule and Tanhaxon transfer function for weights update. The obtained results look promising, thus, ANN is utilized to enhance the understanding of the behavior of swirling, recalculating, axisymmetric, and turbulent flow inside dump combustors. ANN is employed to compute kinetic energy terms (production, diffusion, convection, and viscous dissipation as well as estimating stream function to recognize the recirculation regions at combustor's corners and centerline.College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME)Ahmed, SaadEl Kadi, Hany2011-09-18T11:28:42Z2011-09-18T11:28:42Z2011-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2011.14http://hdl.handle.net/11073/2735en_USoai:repository.aus.edu:11073/27352025-06-26T12:29:20Z |
| spellingShingle | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks AlSharif, Amin Moh'd neural networks Combustion engineering Jets Fluid dynamics Turbulence Mathematical models Combustion Research |
| status_str | publishedVersion |
| title | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| title_full | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| title_fullStr | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| title_full_unstemmed | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| title_short | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| title_sort | Prediction of Turbulence Statistics in a Model Dump Combustor Using Artificial Neural Networks |
| topic | neural networks Combustion engineering Jets Fluid dynamics Turbulence Mathematical models Combustion Research |
| url | http://hdl.handle.net/11073/2735 |