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Development of a Piston Deposit Prediction Model for the Evaluation of Diesel Engine Lubricants, Based on Multiple Laboratory Bench Tests
Published May 13, 1997 by Coordinating European Council in Belgium
Lubricants for modern European heavy duty diesel engines have to meet the requirements listed in the ACEA Heavy Duty Diesel Engine Oil specifications. These specifications are, in part, based on engine tests which are expensive, and time consuming. For this reason it is desirable to have a bench test as a quick and inexpensive screening tool for additive packages. Numerous laboratory screening tools already exist in the industry, each of them evaluating a very limited part of the lubricant's performance. Predicting the performance in an engine using a regression model which is based on multiple bench tests, may expand the applicability of a prediction model.
The work presented describes the development of a prediction model for piston cleanliness in the CEC L-42-A-92 (Mercedes-Benz OM 364A) test. Three essential performance parameters of the additive package; 1) dispersancy, 2) detergency, and 3) oxidation inhibition were selected. The effects of these parameters on the piston cleanliness and on a set of six bench tests, were evaluated using a factorial matrix design. Several statistical techniques were used for the final selection of the laboratory bench tests to be used for the development of a multiple bench test prediction model.
The validity of the model has been tested using the OM 364A reference oils. Their predicted performance was in agreement with the industry average of the piston cleanliness performance. In addition, a demonstration is given, showing that the model, used a screening tool, gave the same ranking of a number of additive approaches as the OM 364A engine did