A Knowledge Based Algorithm to Streamline Estimation of Engine Performance Parameters from Combustion Pressure, Crank Signal -Time History Test Data 2015-26-0075
In the quest towards meeting stringent emission norms as well as robust performance requirements, there is an ever growing need to continually research into and develop high caliber engines. This necessitates handling huge amounts of generated test data that monitors a multitude of variables like engine speed, combustion chamber pressure, engine load and the like. Further, in order to establish the scalar engine performance parameters like efficiency, Brake Mean Effective Pressure, Indicated Mean Effective Pressure, P-V diagram, post processing is required to be done on the measured test data that involves complex calculations like numerical integration and other mathematical operations on a grand scale.
In order to meet this objective, the authors hereby showcase a knowledge based algorithm that integrates and streamlines the entire procedure from handling of the huge test data to performing all the calculations in order to arrive at the scalar engine performance parameters. In addition to being highly productive, the knowledge based algorithm also leverages customizable tools readily available right within the MS Windows platforms, and hence proves to be highly cost effective. The algorithm first processes pressure time history raw signals measured for various loads and speeds to filter them and derive pressure vs. crank angle diagrams with help of available crank signal. These are then input to numerical integration scheme along with engine geometric dimensions to establish scalar engine performance parameters. The entire algorithm is automatically performed in a streamlined manner and is phenomenally productive and cost effective.
Citation: Kaundinya, A., S Thipse, Y., Sagare, V., and Marathe, N., "A Knowledge Based Algorithm to Streamline Estimation of Engine Performance Parameters from Combustion Pressure, Crank Signal -Time History Test Data," SAE Technical Paper 2015-26-0075, 2015, https://doi.org/10.4271/2015-26-0075. Download Citation
Ashwin Subramanian Kaundinya, Yogesh S Thipse, Vinayak Shivalink Sagare, Neelkanth V Marathe
A R A I
Symposium on International Automotive Technology 2015