Experimental Data Processing Techniques to Map the Performance of a Spark Ignition Engine

Abstract

Mapping the performance of an internal combustion engine over a wide range of operating conditions is a common procedure during development. The generation and post-processing of the data are high-cost activities. Two approaches which offer advantages over parametric test plans have been investigated. A statistically designed matrix of tests has been employed to map engine stability and combustion performance parameters. This approach minimizes the number of tests required and post-processing techniques provide valuable insight to relationships which exist between variables. This is particularly useful and efficient when qualitative trends are of prime interest. When large data sets are necessarily acquired and quantitative relationships between variables are of particular concern, then data processing using neural networks is shown to be an effective approach. The use of this technique is illustrated by application to evaluate relationships between engine-out emissions and engine state variables.