| description abstract | The main goal of current engine development is to increase power density and efficiency and to minimize engine emissions. The idea is to obtain the desired power output with a highly charged combustion engine in combination with exhaust gas turbocharging and a very small engine displacement, which is known as downsizing. The selection of a turbocharger is based on the maps of the turbine and compressor, which are usually measured on a test bench. They also provide important boundary conditions on the engine process simulation of a supercharged engine with this turbocharger. In general, a very accurate measurement of the characteristic maps is desired to ensure the best possible matching. However, random and systematic errors have an impact on the measurement results. In order to assess the quality of the measured and calculated values, it is necessary to determine the uncertainties of the measurement variables as accurately as possible; particularly, the error propagation in calculating the efficiencies. The uncertainties are based on a systematic uncertainty component of the sensor and the confidence interval. In this way, the measurement uncertainty is estimated by linear and geometric combination of the calculated random and systematic uncertainties. After that, the respective uncertainty contributions and the identification of the relevant parameters that influence the resulting measurement uncertainty are evaluated. Knowing the measurement uncertainties of the characteristic maps of a turbocharger, the influence on engine operation will be determined with a onedimensional engine process simulation model. Consequently, the determined measurement uncertainty will be applied as a deviation on the efficiencies and will be investigated in a GT POWER simulation. The impact of the measurement uncertainty on the engine performance is shown on the basis of load steps. | |
