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TECHNICAL PAPERS: Manifold Gas Dynamics and Turbocharging

The Use of Neural Nets for Matching Fixed or Variable Geometry Compressors With Diesel Engines

[+] Author and Article Information
S. A. Nelson, Z. S. Filipi, D. N. Assanis

Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109-2133

J. Eng. Gas Turbines Power 125(2), 572-579 (Apr 29, 2003) (8 pages) doi:10.1115/1.1563239 History: Received July 01, 2000; Revised November 01, 2002; Online April 29, 2003
Copyright © 2003 by ASME
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References

Figures

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Neural nets: (a) diagram of a single neuron with three inputs; and (b) a two input neural network with a single hidden layer of five neurons, each one having the inner structure resembling diagram (a)
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Compressor pressure ratio and efficiency lines as a function of mass flow and rotational speed calculated using the neural net model
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Predicted steady-state operating points at full load and part load, calculated using the linear interpolation technique (○) and the neural net model (x)
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Predicted boost pressure ratio, equivalence ratio, mass of air induced per cycle and brake specified fuel consumption (BSFC) versus engine speed
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Five contrived maps for compressors differing only by a fictional design parameter α
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Predicted steady-state operating points for different speeds and full load of the 14 L diesel engine superimposed on six maps, each one corresponding to a different value of the design parameter α
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Predicted steady-state operating parameters of an intercooled and nonintercooled 14 L diesel engine operating at 1900 rpm and full load as a function of the fictional design parameter α
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Predicted steady-state brake specific fuel consumption of an 14 L diesel engine at full load and several operating speeds as a function of the fictional design parameter α

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