Geometry-Based Compressor Data-Maps Prediction

Paper #:
  • 2013-01-0933

Published:
  • 2013-04-08
DOI:
  • 10.4271/2013-01-0933
Citation:
El Hadef, J., Janas, P., Colin, G., Talon, V. et al., "Geometry-Based Compressor Data-Maps Prediction," SAE Technical Paper 2013-01-0933, 2013, doi:10.4271/2013-01-0933.
Pages:
13
Abstract:
In the past few years, the increasing market penetration of downsized engines has reduced the pollutant emissions of internal combustion engines. The addition of a turbocharger to the air path has usually enabled the dynamic performances of the vehicles to be maintained. However, in the development process, deciding on the appropriate set of components is not straightforward and a lengthy fitting process is usually required to find the right turbocharger. Car manufacturers usually have access to a limited library of compressors and turbines which have actually been built and for which measurement campaigns have been carried out. This study is motivated by the need to extend the libraries available for simulation in order to provide a substantial increase in freedom in the matching process.In this paper, we present three different methods to predict the two compressor data-maps that are usually needed in simulations: mass flow rate and isentropic efficiency versus pressure ratio and rotational speed. With the first two methods, extended data-maps are obtained using only the inducer and exducer diameters, the Trim and the A/R ratio of the compressor, i.e. only geometrical characteristics. However, their accuracy remains limited to the database that is used to train the model. The third method consists in predicting the influence of a geometry change on the compressor data-maps. It is considerably more accurate than the first two methods. The paper will present results obtained for more than thirty compressors which have been used to calibrate and validate the tools.
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