In this study, canola oil has been selected for the test on a diesel engine and its suitability as an alternative fuel has been examined. To decrease the high viscosity of canola oil, the effect of temperature on viscosity has been researched. Then the fuel delivery system has been modified to heat canola oil before injecting the oil into the combustion chamber. Also, ethanol has been tested as an additive by blending with canola oil. An experimental setup has been installed according to standards to carry out tests. The set up has been controlled with a computer to take measurements more precisely and to perform experiment automatically. Experimental investigations have been conducted on a four cylinder, direct injection diesel engine. Full load-variable speed tests have been conducted to evaluate engine performance parameters. In addition 13 mode ESC test cycle has been performed to determine the exhaust emissions. Engine performance and emissions characteristics of canola oil and canola-ethanol blend containing 30% ethanol have been compared with those of baseline diesel fuel. Experimental results show that engine performance decreased for canola oil. Addition of ethanol into canola oil has been noticed to improve performance a little with respect to pure canola oil. Although, maximum performance has been obtained with diesel fuel, minimum specifics energy cost is obtained with canola oil. It has been observed that hydrocarbon (HC) emissions decrease with canola oil, blending ethanol with canola oil increase HC emissions and maximum values are read for diesel fuel. Carbon monoxide (CO) emissions have been observed to be the highest for canola oil but blending ethanol has a decreasing effect on CO emissions. As for particulate matter (PM), use of canola oil has been seen to be more pollutant than diesel but adding ethanol in canola reduces PM emissions significantly.

Growing concerns about interruption to oil supply and oil shortages have led to escalating global oil prices. In addition, increased public acceptance of the global warming problem has prompted car manufacturers to agree to carbon emission targets in many regions including most recently, the Californian standards. Other legislating bodies are sure to follow this lead with increasingly stringent targets. As a result of these issues, spark ignition engines in their current form will need significant improvements to meet future requirements. One technically feasible option is smaller capacity downsized engines with enhanced power that could be used in the near term to reduce both carbon emissions and fuel consumption in passenger vehicles.This research focuses on exploring the performance limits of a 0.43 liter spark ignited engine and defining its operating boundaries. Limiting factors such as combustion, gas exchange and component design are investigated to determine if they restrict small engine performance. The research gives direction to the development of smaller gasoline engines and establishes the extent to which they can contribute to future powertrain fuel consumption reduction whilst maintaining engine power at European intermediate class requirements.

É apresentada uma metodologia para descrever o mapa de desempenho (ou mapa de consumo de combustível) de um motor de combustão interna como função de suas condições de operação (rotação e torque) baseados em medições embarcadas. É utilizada para este levantamento a combinação de medições via GPS (para a velocidade longitudinal e inclinação de pista) e OBD-II para aquisição de sinais da rede CAN, como rotação do motor e consumo de combustível. É desenvolvida uma metodologia para o cálculo do torque líquido do motor baseado na medição de velocidade e aceleração longitudinal do veículo com uma margem de incerteza de 2% a 5% no cálculo do torque em condições normais de operações. É realizado um detalhamento da origem das incertezas para avaliar a contribuição individual de cada parâmetro. Um modelo de regressão polinomial é utilizado para descrever o mapa de consume de combustível do motor cujos coeficientes característicos são determinados experimentalmente através da metodologia proposta para cinco veículos diferentes a fim de comprovar a eficácia da metodologia. Os coeficientes de correlação variam de 0.797 a 0.997, sendo que em três de cinco veículos o coeficiente de correlação é maior que 0.910, comprovando a robustez da metodologia. / It is presented a methodology to describe the engine performance map (or the engine fuel map) for an internal combustion engine as a function of its operating conditions (engine speed and torque) based on on-board measurements. It is used a combination of GPS measurements for vehicle speed and road grade together with a OBD-II acquisition system in order to acquire information provided by CAN network, such as engine speed and fuel consumption. A methodology to calculate the engine torque based on speed and acceleration measurements is shown with an average uncertainty in the range of 2% to 5% for torque calculation in normal operating conditions. It is presented a detailed breakdown of the contribution of individual parameters in torque calculation uncertainty. A polynomial regression model to describe the engine fuel map is presented and the coefficients for this model is calculated based on on-road measurements for 5 different vehicles to prove the accuracy of the proposed methodology. The correlation coefficients obtained for these measurements are within the range of 0.797 to 0.997 and three out of five vehicles with correlation coefficient higher than 0.910, proving the methodology robust.

The demand for turbofan engine performance development is high in the military industry. However, to develop the engine, it is necessary to predict its performance, and engine testing is both time-consuming and costly. Therefore, simulation is an effective approach to predicting the engine’s performance. During this thesis, a low bypass ratio turbofan engine is created in the simulation tool Simulink to investigate the engine performance throughout different flight conditions and maneuvers. The engine model is constructed for the future fighter aircraft at SAAB Aeronautics. The development of a design point has received particular attention throughout the work. After that, the development of proven methods for estimating engine performance of other parts of the flight envelope, resulting in increased model fidelity and enabling simulations of the same engine type but under different conditions and flight cases. To summarize, the tests of the engine model are successful under various design characteristics, conditions, and flight cases. In addition, simulations of the performance evaluation of fighter aircraft engines have been accomplished.

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.

This work will be focused on issue of a jet engine. The thesis will be divided into search retrieval part and computational part. In the search retrieval part it will focus on different configurations of jet engines as well as areas of their use. The main part of the thesis will however focus on a calculations where a turbine, compressor and an exhaust nozzle will be designed in order to give a thrust of approximately 1kN. Next step will be determination of an engine charcteristic that will give us a preview on how the engine performance will look like in off-design modes.

<p>One of the major challenges in the field of internal combustion engines is keeping up with the advancements in electrification and hybridization. Automakers are striving to design environment – friendly and highly efficient engines to meet stringent emission standards worldwide. Improving engine efficiency and reducing heat losses are critical aspects of this development. Therefore, accurate heat transfer prediction capabilities play a vital role in engine design process. Current methods rely on computationally intensive 3D numerical analyses, there is a growing interest in reliable simplified models. </p> <p>In this study, a 1D diesel engine model featuring predictive combustion was integrated with a detailed finite element thermal primitive based on the 3D meshing feature available in GT Suite. Coolant and oil hydraulic circuits were incorporated in the model. The model proves to be an effective means to assess the impact on heat rejection and engine heat distribution given by an engine calibration and operating conditions. </p> <p>This work also contributes to the advancement of virtual IC engine development methods by focusing on the design and tuning of complex engine system models using GT Power for accurate prediction of engine performance. The current processes in engine simulations are assessed to identify sources of errors and opportunities for improvements. The methods discussed in this work include isolated sub system level calibration and model evolution specifically address the issue of identifying noise factors and issues in smaller parts. Additionally, the study aims on improving the model’s trustworthiness by computing 1st law sanity checks, replicating real-life compressor map calculations and refining GT’s existing global convergence criteria. </p>