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dc.contributor.advisorCampean, Felician
dc.contributor.advisorNeagu, Daniel
dc.contributor.authorPant, Gaurav
dc.date.accessioned2019-08-27T09:56:55Z
dc.date.available2019-08-27T09:56:55Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10454/17223
dc.description.abstractThe data-driven models used for the design of powertrain controllers are typically based on the data obtained from steady-state experiments. However, they are only valid under stable conditions and do not provide any information on the dynamic behaviour of the system. In order to capture this behaviour, dynamic modelling techniques are intensively studied to generate alternative solutions for engine mapping and calibration problem, aiming to address the need to increase productivity (reduce development time) and to develop better models for the actual behaviour of the engine under real-world conditions. In this thesis, a dynamic modelling approach is presented undertaken for the prediction of NOx emissions for a 2.0 litre Diesel engine, based on a coupled pre-validated virtual Diesel engine model (GT- Suite ® 1-D air path model) and in-cylinder combustion model (CMCL ® Stochastic Reactor Model Engine Suite). In the context of the considered Engine Simulation Framework, GT Suite + Stochastic Reactor Model (SRM), one fundamental problem is to establish a real time stochastic simulation capability. This problem can be addressed by replacing the slow combustion chemistry solver (SRM) with an appropriate NOx surrogate model. The approach taken in this research for the development of this surrogate model was based on a combination of design of dynamic experiments run on the virtual diesel engine model (GT- Suite), with a dynamic model fitted for the parameters required as input to the SRM, with a zonal design of experiments (DoEs), using Optimal Latin Hypercubes (OLH), run on the SRM model. A response surface model was fitted on the predicted NOx from the SRM OLH DoE data. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real-time simulations of transient drive cycles to be executed. The performance of the approach was validated on a simulated NEDC drive cycle, against experimental data collected for the engine case study. The capability of methodology to capture the transient trends of the system shows promising results and will be used for the development of global surrogate prediction models for engine-out emissions.en
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.en_US
dc.subjectEngine modellingen_US
dc.subjectSystem identificationen_US
dc.subjectInternal combustion engineen_US
dc.subjectDynamic modellingen_US
dc.subjectNeural-network modelsen_US
dc.subjectLocal model networksen_US
dc.subjectLOLIMOTen_US
dc.subjectEmissions modellingen_US
dc.subjectCallibrationen_US
dc.titleHybrid Dynamic Modelling of Engine Emissions on Multi-Physics Simulation Platform. A Framework Combining Dynamic and Statistical Modelling to Develop Surrogate Models of System of Internal Combustion Engine for Emission Modellingen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2018
refterms.dateFOA2019-08-27T09:56:55Z


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