Browse Publications Technical Papers 2008-01-3006

A Technique for Processing Cylinder Pressure and Test Bed Data Sets for Engine Speed-Sweep Tests to Allow Reduced Testing Time with Enhanced Interpretation of Results 2008-01-3006

The use of cylinder pressure data for high performance and racing engine development is widespread. However there are still opportunities to increase the efficacy of this measurement technology to accelerate engine power development and calibration.
This paper covers techniques to efficiently derive more meaningful information from combustion test data using techniques learned from AVL Racing's engine development experience. The characteristics of good cylinder pressure data quality and system set-up (judicious filtering, proper pegging, encoder phasing, etc) will be reviewed, and the development of a novel technique for the efficient processing of the large amounts of test data that are generated will be presented.
For sweep testing the integration of cylinder pressure and test bed data is hampered by the fact that test bed data is acquired at constant sampling rate, while cylinder pressure data is sampled at progressively higher frequency as engine speed increases. This makes the calculation of important engine performance metrics such as firing engine friction and net specific fuel consumption problematic for sweep tests.
The analysis techniques presented in this paper allow the direct mathematical computation of cylinder pressure data with test bed data for engine speed sweep tests, resulting in the benefits of sweep testing (fast, minimum wear on engine, many different tests in a short time span) with the rigor of steady state step testing. The technique processes cylinder pressure and test bed separately at first, sorting all data into user defined speed bins and performing statistical evaluations on each bin. The analysis techniques also effectively smooth signals that are inherently noisy, such as CA50, IMEP, individual cylinder torque, and power. Variation in these metrics is captured with statistical metrics.
Particular emphasis will be placed on the application of these data processing tools for accelerated engine hardware development using the unique insight afforded by these techniques.


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