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An experimental investigation was performed on an engine dynamometer to study in cylinder pressure curve and combustion parameters variability with ethanol addition. It was used a Flex-Fuel engine, 1.4 L, 4 cylinders, with a programmable engine control unit to optimize the calibration for different blends of Brazilian gasoline and hydrous ethanol. Engine was calibrated for maximum break torque limited by knocking. In-cylinder pressure was measured by using a pressure sensor installed on the spark plug and analyzed by a combustion data system. Combustion duration, mass fraction burned, indicated mean effective pressure (IMEP) and others were calculated based on in-cylinder pressure curve data. The combustion variability was analyzed from 300 recorded engine cycle for each operating condition. Results for some operating conditions indicated that ethanol addition can reduce combustion variability on a non linear pattern.

Nowadays computer simulation is an important tool to support new internal combustion engine projects, but still further studies are necessary for its use in fuel development. In order to study the influence of fuel properties on engine combustion and emission performance, a computer model was designed based on a Flex-Fuel engine geometric data. Model was validated with experimental tests done on an engine dynamometer. A simulation software was used to simulate the experimental conditions, by using Wiebe two zone combustion and Woschni heat transfer models. In-cylinder maximum pressure, IMEP and emission data were calculated for different gasoline-hydrous ethanol blends at 3875 rpm, 60 Nm and 105 Nm. Total hydrocarbons concentration was simulated comparing the experimental data of hydrocarbons added with unburned ethanol emission measured with a FTIR analyzer.

Experiments in engine test cells are under the influence of several parameters and types of equipment, which may impact the test results. Many variables of interest are derived from the combination of more than one quantity, increasing the results uncertainty of the final reported value. This paper describes a detailed procedure to calculate uncertainty of measurement of emission tests using a FTIR (Fourrier Transformed Infrared) emission analyzer. A Flex-Fuel engine using gasoline and ethanol was tested under different operating conditions on an engine dynamometer equipped with automation system. For each operating condition at least four different measurements were taken. The expanded uncertainty was calculated by the combination of Type A (due to repeatability) and Type B (due to calibration, sensor resolution and others).

The worldwide concerns and some countries stricter legislations regarding the CO2 emission of light duty vehicles are motivating new technologies adoption, such as hybrids and electric battery vehicles, and discussions about what fuel economy data comparison between different countries. International discussions were done about the need to reevaluate the existing standardized driving cycles due to large emission and fuel economy differences when compared to the real road values, leading to the creation of a new cycle called WLTC (Worldwide Harmonized Light Duty Vehicle Test Cycle). Light duty vehicle fuel economy tests are usually performed on a chassis dynamometer using standard driving cycles under controlled laboratory conditions. Each country regulation defines the standard cycles used for the fuel economy tests.

Combustion Modeling of Internal combustion engines is still a complex matter, requiring further developments to better simulate the performance and emissions of different fuels. In order to study the influence of gasoline-ethanol blends on a Flex-Fuel engine, a computer model was designed to simulate the experimental conditions using Fractal combustion and Woschni based heat transfer models. The simulations were validated with engine dynamometer experimental tests. In-cylinder maximum pressure, IMEP and emissions data were calculated for different gasoline-hydrous ethanol blends at different engine conditions. The computer model presented a predictive behavior and a good agreement with experimental data for in-cylinder maximum pressure and IMEP. Regarding emissions, the simulations of some pollutants could not match precisely the experimental data, showing the need for additional combustion modeling improvements.