Osman Akın Kutlar

More stringent emissions regulations are forcing the automotive industry to focus on new technologies ensuring lower emissions. The declaration of the European Union Commission in May 2018 targets 30% lower CO ₂ emissions compared with 2019 average fleet values in the heavy-duty vehicle market. To fulfill this requirement, conventional diesel engines must operate with maximum energy efficiency. Lowering the engine heat rejection to air and coolants is an obligation to increase energy efficiency and utilize the wasted energy. However, accurately predicting heat rejection during the early design phase remains a challenge due to the trade-off between the computational cost of 3D CFD simulations and the limited spatial resolution of 1D models. In this article, a robust and computationally efficient heat rejection prediction methodology is developed by integrating a predictive combustion model (DI-Pulse) with a conjugate heat transfer approach. First, a one-dimensional thermodynamical model is created, and thermal correlation is performed using 3D CFD outputs, including critical metal temperatures and rejected heat. Secondly, a comprehensive thermal survey is conducted in a dynamometer environment to validate the model under real-world conditions. Finally, the methodology is verified by comparing the 1D model with test outputs, achieving a prediction accuracy within approximately ±10 kW for total radiator heat rejection and ±5 kW for cylinder heat rejection. Unlike standard approaches, this methodology enables the optimization of cooling elements and combustion parameters simultaneously, significantly reducing the reliance on costly physical testing and accelerating the development of high-efficiency heavy-duty engines.

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

This study aims to increase the flame speed in the Wankel engine by using turbulent jet ignition. The Wankel engine suffers from poor exhaust emissions and high fuel consumption due to insufficient combustion speed and the inability to spread the flame throughout the shallow structured combustion chamber. With the hot turbulent flame jets obtained through the orifices, the turbulence level of the combustion chamber and the flame speed can be increased, as a result, the fuel consumption can be reduced. Various pre-chamber geometries were simulated with the Converge Studio program to determine the geometry features that can give better results. Taguchi method and ANOVA (Analysis of variance) tests were conducted to optimize the parameters and levels. Afterward, passive pre-chamber tests were conducted on a single-rotor Wankel test engine in the Istanbul Technical University Automotive Laboratory. Several load and speed operating conditions were tested, and results were compared with the base engine that has the classical spark ignition system. During the experiments, the parameters were kept constant to see the direct effect of the pre-chamber on the combustion rate. The preliminary results show that classical spark ignition engine has better specific fuel consumption and HC emissions in general. However, the turbulent jet ignition system with a passive pre-chamber has the potential to improve the Wankel engine performance at certain operating conditions.

The burning process is one of the most important periods, which affects thermal efficiency and exhaust gas emissions, in internal combustion engines. The combustion process in internal combustion engines is modeled with one-dimensional or multi-dimensional software because it is cheaper, faster, and more practical than experiment. One of these methods, which is used to model the combustion period, is the Wiebe function. The Wiebe equation is an approach used in calculating the mass fraction burned and the heat release rate. The selection of Wiebe parameters is one of the most important factors affecting the accuracy of the mass fraction burned. In this study, the measured cylinder pressure of a spark ignition engine was directly used to calculate the heat released rate. The experiments were conducted at different brake mean effective pressures, engine speeds and relative air/fuel ratios, which were called independent variables. The shape factor (m) was determined by fitting the Wiebe equation to the heat release rate curves, which were extracted from the experimental results. The relationship between determined shape factor and independent variables was analyzed with a statistical approach. Eventually, a linear regression model, which explains 80% of the change in the shape factor, was created.

Combustion is the main parameter that affects efficiency and exhaust gas emissions. Recently, different studies have been carried out to increase the combustion rates due to the increasing use of the alternative fuels and lean mixtures in spark ignition engines. In general, in the absence of systems such as an optical access engine or ionization probes, combustion process evaluation is done based on cylinder pressure. In this study, the effect of different combustion chamber geometries on the turbulent burning speeds was investigated experimentally and theoretically. A three-zone, quasi-dimensional thermodynamic model for a spark ignition engine was constructed. Measured cylinder pressure data were used to establish the transition between the zones of the model and determine turbulent burning speeds. Two different turbulent speeds were calculated in the model, these are flame propagation and consumption speeds. It was seen that combustion chamber geometry significantly affected turbulent burning speeds. In MAN-Ricardo shapes and cylindrical shapes, the turbulent burning speeds decreased after it reached a maximum value in the combustion period. In flat geometry, without any bowl, speed continuously decreased different from other two designs. By means of a quasi-dimensional thermodynamic model, mean values of the turbulent burning and the flame propagation speeds can be calculated without having any optical observation.

Direct injection on the Wankel engines has been practiced since the 1970’s. By applying direct injection in the Wankel engine, the specific fuel consumption and HC emissions, which are seen as disadvantages for this engine, might be reduced and the mixture formation is improved. In order to obtain a better mixture formation, the fuel injector must be located in a proper place and a correct direction. In addition, the most suitable injector for the engine structure should be selected. In this study, direct injection was applied for the RENESIS 13B Wankel engine and injector selection for the engine and the location of the injector on the housing were examined. In addition, the diameter of orifice and flow rate characteristics of the injector were investigated. According to the results, two types of injectors were selected. These injectors were called as low speed and high speed. An injector with a narrow nozzle angle (<30°) was used to deliver the fuel to the leading of the chamber, and the fuel injected after the intake ports closed. The orifice diameters of low-speed and high- speed injectors were measured 0.33 and 0.45 mm, respectively. In addition, both injectors have low speed rates when injection duration was below 2 ms. This flow rate was increased by providing high voltage.

The tightening of the exhaust emission limits and the desire to reduce fuel consumption determine the direction of research on internal combustion engines. As it is known, the leaning of the mixture in spark ignition engines generally decreases fuel consumption and NOx emission together. Due to this fact, many researchers are focusing on lean combustion. The main purpose of this study is to compare the effect of different chamber geometries on combustion parameters of a spark ignition engine operated at lean and stoichiometric mixtures. One of the most important parameters of the combustion process is cyclic variations. It defines the stability of a spark ignition engine in operating conditions. Cyclic variations are increased with the leaning of the mixture in spark ignition engines. For lean mixture, a new combustion chamber was designed and manufactured for accelerating flow motions through its sickle and bowl shape. This chamber called MAN-Ricardo (MR) type decreased burn durations in the combustion process. While the start of the ignition for MR was later, the burn duration is shorter than that of the other two geometries. Therefore, MR type reduced the cyclic variations compared to other geometries in homogenous and lean mixtures. As a result, the combustion chamber must be designed to increase the turbulence intensity for a spark ignition engine to operate stably in lean mixtures, unlike the stoichiometric mixture.

The scope of this work was to establish a one-zone thermodynamic calculation model to analyze 13B multi-side port rotary engine on the base of Mazda RX-8. Volume variation was calculated according to the basic geometrical data of the engine and heat transfer was calculated using Wilmer’s heat transfer coefficient. Combustion process was defined as heat addition according to Vibe function and simultaneously converting fresh charge to combustion products. To account related losses, discharge drag, leakage, crevice volumes, and mean friction pressure were taken into account. The port fuel injection (homogeneous mixture) was used in the model assumptions. Model calculations were validated with experimental data. Many parameters were investigated in the model such as combustion chamber pressure, mass and engine torque at different speeds, and full load conditions. Moreover, the effect of the Vibe parameter, combustion duration, and intake port variations on the torque was investigated. The highest torque value was observed in the model with only primary intake port at low rpm (1000–3750 rpm) and primary and secondary port at mid rpm (3750–6250 rpm). Higher torque values were obtained from two secondary and two auxiliaries than primary, secondary, and auxiliary in the literature at high rpm (6250–8500 rpm). Combustion duration, Vibe parameter, and variations of different intake ports have a significant effect on the engine characteristics. The thermodynamic model developed in this study may be used as an effective tool to examine the performance parameters of the Wankel engine.

Mixture formation in port fuel spark ignition engines is one of the most im-portant parameters, which affect combustion and emissions. In this study, the ef-fects of different injection start timings and some other port fuel injection (PFI) parameters on the performance and emissions of a water cooled and single cylin-der spark ignition engine were examined experimentally. The experiments were performed at different engine speeds (1200 and 1500 rpm for effect of injection pressure and 1500 rpm for changing injection timings) and different engine loads (3 bar and 5 bar for injection pressure and 1 bar and 5 bar for start of injection). The start of injection was chosen according to intake pressure measurements. The experiment results showed that brake specific fuel consumption (BSFC) and total hydrocarbon (THC) emissions are increased by the increase of the injection pressure. Because with the increase of injection pressure, the fuel can reach the intake manifold wall or intake valve. Therefore, the fuel enters the cylinder as a droplet. Different start of injection experiments showed that injection times have an effect on emissions and performance. The minimum brake specific fuel con-sumption and THC values were obtained at -243 °CA value of injection start. The twice injection in one cycle increased brake specific fuel consumption and THC emissions.

The researches on Wankel engines are very rare and considered new in modelling and prediction. Therefore this study deals with the artificial neural network (ANN) modelling of a Wankel engine to predict the power, volumetric efficiency and emissions, including nitrogen oxide, carbon dioxide, carbon monoxide and oxygen by using the change of mean effective pressure, intake manifold pressure, start of ignition angle and injection duration as inputs. The experiment results were taken from a research which is performed on a single-rotor, four stroke and port fuel injection 13B Wankel engine. The number of datas which are taken from experimental results were scarce and varied in six different data set (for example; mean effective pressure, from 1 to 6 bar) at 3000 rpm engine speed. The standard back-propagation (BPNN) Levenberg-Marquardt neural network algorithm is applied to evaluate the performance of middle speed range Wankel engine. The model performance was validated by comparing the prediction data sets with the measured experimental data. Results approved that the artificial neural network (ANN) model provided good agreement with the experimental data with good accuracy while the correlation coefficient R varies between 0.79 and 0.97.