PM-emissions during a load transient have been measured regarding particle mass, exhaust transparency and particle number concentrations in different size ranges. The load transient was from low to medium load at constant speed and was performed with a single cylinder research engine. Mass measurements were conducted with a Tapered Element Oscillating Microbalance . Exhaust transparency was measured with an Opacimeter. Particle Number Concentrations were measured with two different Condensation Particle Counters , CPCs, where one of them was equipped with a Particle Size Selector , PSS, in order to distinguish accumulation mode particles from nucleation mode. An Engine Exhaust Particle Sizer , EEPS, was also used in parallel with the CPCs and provided a full size distribution. For dilution, a rotating disc diluter and a two stage ejector diluter was used. In total two stages of hot dilution and one unheated. It was found that all instruments, except the TEOM, had acceptable time resolution for dynamic measurements with the dilution and acquisition setup used in this experiment. In most aspects, the measurements from the different instruments were consistent and the discrepancies could be explained by their measuring principles. In some cases, simultaneous use of different instruments could provide a more detailed description of the emitted PM. It was also concluded that the rotating disc diluter, with some reservations, could be used for transient measurements.

A clear trend in engine development is that the engines are becoming more and more complex both regarding components and component-systems as well as controlling them. These complex engines have great potential to minimize emissions but they also have a great number of combinations of setting. Systematic testing to find these optimum settings is getting more and more challenging. A possible remedy is to roughly optimize these settings offline with predictive models and then only perform the fine tuning in the engine test bed. To be able to do so, two things are needed; firstly a engine model that will predict how the different setting affect engine performance and secondly how the engine performance affects the emissions.

A clear trend in engine development is that the engines are becoming more and more complex both regarding components and component-systems as well as controlling them. These complex engines have great potential to minimize emissions but they also have a great number of combinations of setting. Systematic testing to find these optimum settings is getting more and more challenging. A possible remedy is to roughly optimize these settings offline with predictive models and then only perform the fine tuning in the engine test bed. To be able to do so, two things are needed; firstly a engine model that will predict how the different setting affect engine performance and secondly how the engine performance affects the emissions.