BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • University of Bradford eTheses
    • Theses
    • View Item
    •   Bradford Scholars
    • University of Bradford eTheses
    • Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    View/Open
    Abstract-List of Chapters-Figures-Tabels and Abbrevations- .pdf (319.2Kb)
    Download
    Exergy Based SI Engine Model Optimisation- Final version-31.pdf (2.610Mb)
    Download
    Exergy Based SI Engine Model Optimisation-PhD Thesis Covers.pdf (169.6Kb)
    Download
    Publication date
    2012-02-13
    Author
    Rezapour, Kambiz
    Supervisor
    Ebrahimi, Kambiz M.
    Wood, Alastair S.
    Keyword
    Engine modelling
    Exergy
    Engine performance optimisation
    Four-stroke bi-fuel spark ignition (SI) engine
    Constrained particle swarm optimisation (CPSO)
    Artificial neural network (ANN)
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    School of Engineering, Design and Technology
    Awarded
    2011
    
    Metadata
    Show full item record
    Abstract
    In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.
    URI
    http://hdl.handle.net/10454/5386
    Type
    Thesis
    Qualification name
    PhD
    Collections
    Theses

    entitlement

     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      Application of Multidisciplinary Design Optimisation to Engine Calibration Optimisation.

      Campean, I. Felician; Wood, Alastair S.; Yin, Xuefei (University of BradfordSchool of Engineering, Design and Technology, 2012)
      Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively.
    • Thumbnail

      Evaluation of camshaft control strategies for a GCI engine using a multidisciplinary optimisation framework

      Kianifar, Mohammed R.; Campean, I. Felician; Richardson, D. (2014)
      This paper presents a calibration optimization study for a Gasoline Direct Injection engine based on a multidisciplinary design optimization (MDO) framework. The paper presents the experimental framework used for the GDI engine mapping, followed by an analysis of the calibration optimization problem. The merits of the MDO approach to calibration optimization are discussed in comparison with a conventional two-stage approach based on local trade-off optimization analysis, focused on a representative emissions drive cycle (NEDC) and limited part load engine operation. The benefits from using the MDO optimisation framework are further illustrated with a study of relative effectiveness of different camshaft timing control strategies (twin independent Versus fixed timing, exhaust only, inlet only and fixed overlap / dual equal) for the reference GDI engine based on the part load test data. The main conclusion is that the MDO structure offers an effective framework for the GDI steady state calibration optimization analysis.
    • Thumbnail

      Analytical target cascading framework for engine calibration optimisation

      Kianifar, Mohammed R.; Campean, I. Felician (2014)
      This paper presents the development and implementation of an Analytical Target Cascading (ATC) Multi-disciplinary Design Optimisation (MDO) framework for the steady state engine calibration optimisation problem. The case is made that the MDO / ATC offers a convenient framework for the engine calibration optimisation problem based on steady state engine test data collected at specified engine speed / load points, which is naturally structured on 2 hierarchical levels: the “Global” level, associated with performance over a drive cycle, and “Local” level, relating to engine operation at each speed / load point. The case study of a gasoline engine equipped with variable camshaft timing (VCT) was considered to study the application of the ATC framework to a calibration optimisation problem. The paper describes the analysis and mathematical formulation of the VCT calibration optimisation as an ATC framework, and its Matlab implementation with gradient based and evolutionary optimisation algorithms. The results and performance of the ATC are discussed comparatively with the conventional two-stage approach to steady state calibration optimisation. The main conclusion from this research is that ATC offers a powerful and efficient approach for engine calibration optimisation, delivering better solutions at both “Global” and “Local” levels. Further advantages of the ATC framework is that it is flexible and scalable to the complexity of the calibration problem, and enables calibrator preference to be incorporated a priori in the optimisation problem formulation, delivering important time saving for the overall calibration development process.
    DSpace software (copyright © 2002 - 2022)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.