a. h. kakaee

In this paper we aim to develop a predictive combustion model for a turbocharged engine in GT-Power software to better simulate engine characteristics and study its behavior under variety of conditions. Experimental data from combustion was initially being used for modelling combustion in software and these data were used for model calibration and result validation. EF7-TC engine was chosen for this research which is the first turbocharged engine designed and developed by IKCO and IPCO in Iran. After analyzing necessary theories for predictive combustion model and required steps for calibration of CombSITurb model in software, one final set of multipliers were calculated based on different sets derived for each engine speed and engine operation was simulated with this combustion model. In addition to improved predictability of engine model, comparing results of predictive model with non-predictive model shows better accuracy especially at lower engine speeds and less tolerance of results for each engine speed.

Nowadays, due to increasing the complexity of IC engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. Accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. In this paper four network based modeling methods are used and compared to model the behavior of an IC engine: neural networks model (NN), group method of data handling model (GMDH), a hybrid NN and GMDH model (NN-GMDH), and a GMDH model whose structure is determined by genetic algorithm (Genetic-GMDH). The inputs are engine speed, throttle angle, and intake valve opening and closing timing, and the output is the engine brake torque. Results show that NN has the best prediction capability and Genetic-GMDH model has the most flexible and simplest structure and relatively good prediction ability.

In this paper, a numerical study is performed to provide the combustion and emission characteristics resulting from fuel-reactivity controlled compression ignition (RCCI) combustion mode in a heavy-duty, single-cylinder diesel engine with gasoline and diesel fuels. In RCCI strategy in-cylinder fuel blending is used to develop fuel reactivity gradients in the combustion chamber that result in a broad combustion event and reduced pressure rise rates (PRR). RCCI has been demonstrated to yield low NOx and soot with high thermal efficiency in light and heavy-duty engines. KIVA-CHEMKIN code with a reduced primary reference fuel (PRF) mechanism are implemented to study injection timings of high reactivity fuel (i.e., diesel) and low reactivity fuel percentages (i.e., gasoline) at a constant engine speed of 1300 rpm and medium load of 9 bar indicated mean effective pressure (IMEP). Significant reduction in nitrogen oxide (NOx), while 49% gross indicated efficiency (GIE) were achieved successfully through the RCCI combustion mode. The parametric study of the RCCI combustion mode revealed that the peak cylinder pressure rise rate (PPRR) of the RCCI combustion mode could be controlled by several physical parameters – PRF number, and start of injection (SOI) timing of directly injected fuel.

In this article determination of appropriate valve timing using sensitivity analysis problem is investigated for a gasoline four stroke engine. In the first part of this study a 4-storke Spark Ignition engine (XU7JP4/L3) including its different systems such as inlet and exhaust manifold, exhaust pipe and engine geometry are modeled using GT-Power software and the model is coupled with MATLAB/Simulink to be able to control input and output parameters. Then in order to find the best model that fits experimental data, sensitivity analysis is performed and the best unknown parameters that can best model the engine are obtained. The input parameters are considered to be the inlet port temperature and pressure, and manifold friction coefficient. The target was achieving the least square error in engine power, torque and fuel consumption. In the second part of the study the optimized model is used for the sensitivity analysis and minimizing the engine specific fuel consumption up to 10 percent reduction in specific fuel consumption as a target. Sensitivity analysis is used for finding the best valve timing in different engine speeds to achieve the target.