Engine modelling for virtual mapping. Development of a physics based cycle-by-cycle virtual engine that can be used for cyclic engine mapping applications, engine flow modelling, ECU calibration, real-time engine control or vehicle simulation studies.

Pezouvanis, Antonios

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Publication date

2010-08-27T15:53:10Z

Author

Pezouvanis, Antonios

Supervisor

Ebrahimi, Kambiz M.
Olley, Peter

Keyword

IC Engine
Engine control
Calibration
Mathematical modelling
Cyclic engine mapping
Engine flow modelling

Rights

The University of Bradford theses are licenced under a Creative Commons Licence.

Institution

University of Bradford

Department

School of Engineering, Design and Technology

Awarded

2009

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Abstract

After undergoing a study about current engine modelling and mapping approaches as well as the engine modelling requirements for different applications, a major problem found to be present is the extensive and time consuming mapping procedure that every engine has to go through so that all control parameters can be derived from experimental data. To improve this, a cycle-by-cycle modelling approach has been chosen to mathematically represent reciprocating engines starting by a complete dynamics crankshaft mechanism model which forms the base of the complete engine model. This system is modelled taking into account the possibility of a piston pin offset on the mechanism. The derived Valvetrain model is capable of representing a variable valve lift and phasing Valvetrain which can be used while modelling most modern engines. A butterfly type throttle area model is derived as well as its rate of change which is believed to be a key variable for transient engine control. In addition, an approximation throttle model is formulated aiming at real-time applications. Furthermore, the engine inertia is presented as a mathematical model able to be used for any engine. A spark ignition engine simulation (SIES) framework was developed in MATLAB SIMULINK to form the base of a complete high fidelity cycle-by-cycle simulation model with its major target to provide an environment for virtual engine mapping procedures. Some experimental measurements from an actual engine are still required to parameterise the model, which is the reason an engine mapping (EngMap) framework has been developed in LabVIEW, It is shown that all the moving engine components can be represented by a single cyclic variable which can be used for flow model development.

URI

http://hdl.handle.net/10454/4419

Type

Thesis

Qualification name

PhD

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Theses

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

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).

Ebrahimi, Kambiz M.; Wood, Alastair S.; Rezapour, Kambiz (University of BradfordSchool of Engineering, Design and Technology, 2012-02-13)

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.
Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

Campean, I. Felician; Neagu, Daniel; Doikin, Aleksandr; Soleimani, Morteza; Byrne, Thomas J.; Sherratt, A. (2019)

Underpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.
Directing the Assembly of Multicomponent Organic Crystals. Synthesis, characterisation and structural analysis of multicomponent organic systems formed from dynamic processes.

Scowen, Ian J.; Munshi, Tasnim; Alomar, Taghrid S. (University of BradfordSchool of Life Sciences, 2015-04-22)

Directed assembly of molecular solids continues to attract widespread interest with its fundamental application in a wide range of commercial settings where control of the crystalline state of materials corresponds with product performance. These arenas include pharmaceuticals, personal care formulations, foods, paints and pigments and explosives. In recent times, the assembly of multicomponent organic systems has achieved considerable impetus with the widespread interest in co-crystal systems. However, cogent assembly (or engineering) of multicomponent materials is still in its infancy. Considerable advances in crystal design have been made through consideration of intermolecular ‘synthons’ – identifiable motifs utilising hydrogen bonds – but the translation of other molecular information (conformation, chirality, etc.) into solid state properties (e.g. long-range (translational) symmetry, crystal chirality) remains poorly understood. In this study, we have attempted to evaluate the influence of a chiral centre adjacent to molecular synthons to identify potential translation of information into the solid form. We have compared the co-crystallisation of nicotinamide with both the racemic mixture of malic acid against that with an enantiomerically pure form of the acid (L-malic acid). As well as DL-phenyllactic acid and L-phenyllactic acid. iii It is apparent that recognition between enantiomeric molecular forms play a significant role in the assembly of these systems. This mechanism can be considered independently from the H-bonding networks supporting the hetero-molecular interactions (e.g. acid-amide recognition). Discrimination and control of such interactions may play a role in transmitting chiral molecular information into solid state multi-component assemblies. In order to develop an understanding of co-crystal formation in chiral and achiral forms with intermolecular interactions, the CSD and crystal structures were obtained to do the analysis of how co-crystals pack. This study has also investigated the use of boronic acids. The aim of this study was to investigate the modification of the hydrogen bonding environment within the hydrogen bonded multi-component systems of boroxines. The study also attempted to determine how the starting materials drive the systems between the boronic acid co-crystal and the boroxine adduct.