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With the current trend towards engine downsizing, the use of turbochargers to obtain extra engine power has become common. A great difficulty in the use of turbochargers is in the modelling of the compressor map. In general this is done by inserting the compressor map directly into the engine ECU (Engine Control Unit) as a table. This method uses a great deal of memory space and often requires on-line interpolation and thus a large amount of CPU time. In this paper a more compact, accurate and rapid method of dealing with the compressor modelling problem is presented. This method is physically based and is applicable to all turbochargers with radial compressors for either Spark Ignition (SI) or diesel engines.

The thermal efficiency of a large ship diesel engine is determined mainly by the design of the engine/propellor combination but small efficiency increments can be obtained through the careful design of automatic controllers for the system. A fuel saving regulator requires an accurate model for the internal states of the engine in order that its thermal efficiency can be maximized. Such a model has been recently obtained by one of the authors (E. Hendricks). This model has been shown to give accurate estimates of the thermal efficiency which can be expected under normal sea conditions. Using the model as a basis an adaptive energy minimizing controller has been designed and tested. Depending upon sea conditions, simulations suggest that fuel savings on the order of 0.5% can be expected. Though small percent-wise, savings on this order could more than pay for the installation costs of the regulator during the first year of use. The project is carried out in cooperation with M.A.N.

The model described here is a dynamic mean value model which is small enough to be realized on a microcomputer. Nevertheless it contains significant and very accurate information on the gross internal variables of an engine (the indicated efficiency, scavenge ratio, scavenge efficiency, etc.). This makes the model useful for control and expert system application on line and/or in parallel with an operating diesel engine. Moreover, because of its simplicity, it gives an overall picture of engine operation which is not possible with more complex single engine cycle models. Comparisons with time dependent experimental data show that the model is very accurate both for static and dynamic predictions of engine performance over a large operating range.