One of the major goals for manufacturers of turbocharged spark ignited (SI) engines is to increase the knock resistance at high loads. In order to move out of knocking COMBUSTION, timing of the spark ignition is retarded, thus limiting the maximum pressure and temperature in the COMBUSTION chamber. This is an effective method to eliminate the knock. The negative effect of retarding spark timing is increasing the exhaust gas temperature. In order to reduce the exhaust gas temperature, rich mixtures are normally used. As a result, the fuel consumption increases. The cooled EGR can be used to solve this problem. Using the cooled EGR decreases the pressure and the temperature in the unburned zone. There are a number of different possible architectures, each with its specific characteristics. Main objectives of this study were to quantify the increase the knock resistance and to decrease the enrichment in order to the target stoichiometric operation over the full operating range. All the simulations were carried out in the GT-POWER Software on the EF7-TC engine.

The present work includes a quasi-dimensional COMBUSTION MODEL to predict the COMBUSTION of direct injection dual fuel diesel engines by a detailed chemical kinetic MODEL for gaseous fuel COMBUSTION. Chemical kinetic MODEL consist of 325 reactions with 53 species (GRI3). Heat release rate of pilot fuel at this MODEL is considered by two Wiebe functions. Predicted values of cylinder pressure for dual fuel operation show good agreement with corresponding previous experimental data.

The homogeneous charge compression ignition (HCCI) engines with ethanol fuel as a renewable fuel is a promising solution to some of the major challenges of COMBUSTION engines. Incomplete or misfiring COMBUSTION limits HCCI operation and damages the catalyst converter and exhaust systems. The experimental data of a 0. 3-liter COMBUSTION engine was used for MODELing and detecting misfiring COMBUSTION. Incomplete and misfiring COMBUSTION in HCCI engine was studied by fuzzy-neural network. There is a significant relationship between misfiring COMBUSTION and in-cylinder pressure variations at 0, 5, 10, 15 and 20 crankshafts. These experimental findings were used to design a fuzzy-neural network for misfiring incomplete COMBUSTION in a HCCI engine. This MODEL has been tested on experimental data. The results showed that the fuzzy-neural network fault diagnostic MODEL can detect incomplete and misfiring COMBUSTION in HCCI engine with ethanol fuel. In addition, the developed MODEL was able to identify the transition success from the normal operating area and incomplete COMBUSTION.

The calibration of modern internal COMBUSTION engines is complex and requires a lot of time and cost on the test bench. A large number of calibration parameters and a tradeoff between fuel consumption and emissions make calibration a complex multi-objective optimization problem. The purpose of this article is the development of calibration and MODEL-based optimization techniques for internal COMBUSTION engines to reduce calibration time and cost and improve optimization accuracy. This paper focuses on empirical MODELing of engines and optimization concerning emissions standards and fuel consumption. To identify the COMBUSTION MODELs, a MODELing toolbox is developed with an intelligent identification method. To optimize the data collection, the design of experiment methods is reviewed and appropriate methods are selected to collect information with the minimum data from all over the design space. Finally, a study is performed on the EF7 engine in the test room of IPCO and it is shown that the number of experimental data is reduced from 5500 data to 1500 data with the help of the design of experiment by Sobol method and MODELing by a deep neural network. so, the virtual engine can replace the real engine in the calibration process.

In a vapor cloud explosion, flame propagation phenomenon and turbulence flow field occur simultaneously. At the same time, the existence of obstacles in the flame propagation direction, leads to wrinkling of the flame front. The mechanism of flame wrinkling phenomenon occurs due to the effect of the flame-vortex interactions. Large scale vortices deform the flame front and increase its surface area. Increasing flame surface area ends in an increase of burning velocity and intensification of pressure build-up. The major challenge in MODELing of large scale vapor cloud explosion is calculation of turbulent flame speed. In this paper a combined MODEL of Weller’s wrinkling factor transport equation and SCOPE3 MODEL was utilized to calculate this parameter. The SCOPE3 MODEL considers high intensity turbulence. Therefore. the combined MODEL is suitable for MODELing of the vapor cloud explosion in presence of significant obstacles. Numerical results also confirm. the ability of this MODEL to accurately predict pressure history.

Availability analysis provides useful information for optimizing the systems. In the present investigation, a MULTI-ZONE COMBUSTION MODEL is developed to simulate diesel engine cycle. Then, the governing equations of availability analysis are applied in this MODEL. The concept of chemical equilibrium based on Olikara and Borman method is used to calculate the concentration of COMBUSTION products. Chemical availability is considered as oxidation, reduction, and diffusion availabilities. The availability analysis is applied to the engine from the inlet valve closing (IVC) until exhaust valve opening (EVO).The effect of fuel injection timing is investigated by various availability terms. The results indicate that advancing time of injection increases work and heat transfer availabilities, but decreases irreversibility.

In this work, a detailed combined chemical kinetics mechanism (76 chemical species, 464 reactions) for a mixture of natural gas and n-heptane with arbitrary mass fractions of natural gas between 36 and 85 percent was developed from the combination of the detailed reaction schemes for natural gas and n-heptane fuels. Then, essential reactions were determined through performing a sensitivity analysis on the combined mechanism. In addition, genetic algorithm was applied to optimize the Arrhenius rate coefficients of the specified reactions by means of sensitivity analysis. Finally, accuracy of the presented mechanism was investigated through two different zone configurations (6 zones and 11 ones) of a MULTI-ZONE COMBUSTION MODEL as well as available laboratory results. Also, the results of the two considered zone configurations from the MULTI-ZONE COMBUSTION MODEL were compared, whereby it was found that the two zone configurations considered could properly predict COMBUSTION, performance parameters (i.e. start of COMBUSTION), burn duration, indicated mean effective pressure, indicated thermal efficiency as well as important HCCI engine emissions (i.e. unburned HC and CO emissions) in good accord with the experimental data. Also, it was discovered that the results of the 6 zone COMBUSTION MODEL were closer to the results of the 11 zone COMBUSTION MODEL. But the required computational time for the 11 zone COMBUSTION MODEL was approximately twice that of the 6 zone COMBUSTION MODEL.

One of the main challenges in HCCI engines is lack of suitable control system as exists in conventional internal COMBUSTION engines. Therefore, controlling HCCI COMBUSTION can improve the commercial application of these types of engine. In this study a control oriented MODEL (COM) that can predict SOC, BD, and CA50hasbeen developed. The validity of the MODEL has been verified by comparing the predicted pressure traces with the corresponding experimental data, resulted from analysis of in cylinder pressure data, in a wide range of operating conditions. Then, various amounts of intake temperature, intake pressure, and equivalence ratio in HCCI engine in the developed MODEL have been implemented. These data are used to optimize coefficients in Modified Knock Integral MODEL (MKIM) correlation, and other correlations for predicting COMBUSTION parameters. One of the advantages of this COM is easy access to inlet parameters.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation MODELs which can predict the timing of occurrence each development stage by integrating effective factors. The MODEL was constructed based on linear equation of plant temperature response. In order to MODEL evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed MODEL was calibrated and run for simulation. Comparison of simulated and observed data showed that the MODEL can strongly predict wheat development stages.