Abstract
The present paper proposes an optimized algorithm for minimizing the amount of data processed in order to maintain all critical information from the in-cylinder pressure sensor but with minimum computational cost. The algorithm uses singular value decomposition (SVD) for reducing the number of samples and pivoting QR decomposition to identify the optimal sampling locations. Experimental data from four engines with different combustion modes, namely Spark ignition (SI), Compression ignition (CI), turbulent jet ignition (TJI), and dual fuel ignition (DFCI), was used to validate the algorithm. The impact of the different sampling methodologies on different metrics for engine performance has been addressed and studied showing negligible information loss when reducing to 25 samples per cycle the acquisition buffer size.
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