A technique which uses trained neural nets to model the compressor in the context of a turbocharged diesel engine simulation is introduced. This technique replaces the usual interpolation of compressor maps with the evaluation of a smooth mathematical function. Following presentation of the methodology, the proposed neural net technique is validated against data from a truck type, 6-cylinder 14-liter diesel engine. Furthermore, with the introduction of an additional parameter, the proposed neural net can be trained to simulate an entire family of compressors. As a demonstration, a family of compressors of different sizes is represented with a single neural net model which is subsequently used for matching calculations with intercooled and nonintercooled engine configurations at different speeds. This novel approach readily allows for evaluation of various options within a wide range of possible compressor configurations prior to prototype production. It can also be used to represent the variable geometry machine regardless of the method used to vary compressor characteristics. Hence, it is a powerful design tool for selection of the best compressor for a given diesel engine system and for broader system optimization studies.
The Use of Neural Nets for Matching Fixed or Variable Geometry Compressors With Diesel Engines
Contributed by the Internal Combustion Engine Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received by the ICE Division July 2000; final revision received by the ASME Headquarters Nov. 2002. Associate Editor: H. Nelson.
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Nelson, S. A., II , Filipi , Z. S., and Assanis, D. N. (April 29, 2003). "The Use of Neural Nets for Matching Fixed or Variable Geometry Compressors With Diesel Engines ." ASME. J. Eng. Gas Turbines Power. April 2003; 125(2): 572–579. https://doi.org/10.1115/1.1563239
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