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This paper describes a research activity, carried out at the University of Perugia, focused on the modelling of an automatic reed valve in a coupled fluid-structure approach. The application here concerned is a reed device used to control a Secondary Air Injection (SAI) system which allows ambient air to enter the exhaust pipe upstream of the catalyst (useful for the reduction of emissions in rich mixture engine operating conditions). Since currently no commercial codes are still available for simulating in a comprehensive way the non-linear dynamics of a reed valve device with position constraints, the main objective of the work is the calculation of the air mass flow rate admitted to the exhaust system through the reed, by means of a slim and easy software tool. The task is accomplished by integrating two different codes, developed by the authors.

Modeling and studies on reed valve devices are topics often dealt with when designing internal combustion engine intake and exhaust systems. This paper describes an activity about the modeling and the optimization potentiality of an engine equipped with a secondary air injection system by means of a reed valve device. The first step of the work dealt with the development and tuning of a non-linear Finite Element model of reed valve and with the integration of this model into a one-dimensional fluid-dynamics simulation code. In particular during this phase the potentialities of the method were tested by implementing the FE model both in a 1D University code and in a 1D commercial code (by means of a provided interface for User Defined Elements). In the second step of the work the simulation results were analyzed for different engine operating points.

In recent years there has been an increasing worldwide effort to limit polluting emissions from road vehicles. The On Board II Diagnostic (OBD II) regulations adopted by California Air Resources Board (CARB) are among the most restrictive rules. They require on-board devices which monitor emission control systems in order to identify deterioration or malfunction of components. For automotive purpose, the high cost of achieving hardware redundancy can be reduced by substituting software redundancy. This approach requires an engine model definition. In this work the application of the Artificial Neural Networks (ANNs) technology, is analyzed and validated by experiments. First model has been tested under varying load conditions with very encouraging results.

The recent introduction of electronic controlled, high pressure injection systems has deeply changed the scenario for light duty, automotive diesel engines. This change is mainly due to the enhanced flexibility in obtaining the desired injection law (time history and injected fuel quantity), while high injection pressures also favour a suitable mixture formation. This results in higher engine performance (efficiency and power) and in better pollutant emissions control. At the same time, in order to reduce the greenhouse gases net production, research is analyzing alternative resources, such as bio-derived fuels. In particular, methyl esters derived by different vegetable oils are characterized by high cetane numbers and very small sulfur content. The present work reports the results of a comparative analysis performed on a modern DI, common-rail, turbocharged engine by using three different bio-derived fuels (rape seed, soybean, waste cooked oil) and conventional fossil diesel fuel.

In this paper, a further step along a research line concerning the set up of a Fault Diagnosis system for OBD-II purpose is presented. The suitability of Artificial Neural Networks for the use as engine simulation modules in the framework of a software redundancy approach has been analyzed. Experimental tests were performed, by acquiring four main engine operational parameters. Using this knowledge base, the performance of a wide variety of different Net Types was analyzed and discussed. Peculiar aspects of the possible industrial applications of this methodology are also deeply examined.