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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Watson, N., and Janota, M. S., 1982, Turbocharging the Internal Combustion Engine, John Wiley and Sons, London.
Assanis, D. N., and Heywood, J. B., 1986, “Development and Use of a Computer Simulation of the Turbocompounded Diesel System for Engine Performance and Component Heat Transfer Studies,” SAE Paper No. 860329.
Harp, J., and Oatway, T. P., 1979, “Centrifugal Compressor Development for Variable Area Turbocharger,” SAE Paper No. 790066, special publication Turbochargers and Turbocharged Engines SP-442, Warrendale, PA.
Franklin, P. C., 1989, “Performance Development of the Holset Variable Geometry Turbocharger,” SAE Paper No. 890646, SAE special publication Power Boost—Light, Medium and Heavy Duty Engines SP-780, Warrendale, PA.
Hush,  D. R., and Horn,  B. G., 1993, “Progress in Supervised Neural Networks,” IEEE Signal Process. Mag., 10(1), pp. 8–39.
Smith, M., 1993, Neural Nets for Statistical Modeling, Van Nostrand Reinhold, New York.
Scaife, M. W., Charlton, S. J., and Mobley, C., 1993, “A Neural Network for Fault Recognition,” SAE Paper No. 930861.
Brace, C. J., Deacon, M., Vaughan, N. D., Charlton, S. J., and Burrows, C. R., 1994, “Prediction of Emissions From a Turbocharged Passenger Car Diesel Engine Using a Neural Network,” Proceedings of the Institute of Mechanical Engineers: Turbochargers and Turbocharging, Mechanical Engineering Publications Limited, Suffolk UK.
Shayler, P. J., Goodman, M. S., and Ma, T., 1996, “Transient Air/Fuel Ratio Control of an SI Engine Using Neural Networks,” SAE Paper No. 960326.
Asik, J. R., Peters, J. M., Meyer, G. M., and Tank, D. X., 1997, “Transient A/F Estimation and Control Using Neural Network,” SAE Paper No. 970619.
Lombardo, G., 1996, “Adaptive Control of a Gas Turbine Engine for Axial Compressor Faults,” ASME Paper No. 96-GT-445.
Roemer, M. J., and Pomfret, C., 1996, “Engine Health Monitoring (EHM) System for Advanced Diagnostics Monitoring of Gas Turbine Engines,” SAE Paper No. 961305.
DePold, H. R., and Gass, F. D., 1998, “Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics,” ASME Paper No. 98-GT-101.
Ludwig, C., and Ayobi, M., 1995, “Fault Detection Schemes for a Diesel Engine Turbocharger,” Proceedings of the 1995 American Control Conference, Part 3, Seattle, WA, pp. 2118–2122.
Nelson, S., Filipi, Z. S., and Assanis, D. N., 1996, “A Neural Network for Matching the Turbocharger to IC Engine,” Proceedings of the 1996 Spring Technical Conference of the ASME Internal Combustion Engine Division, Vol. 26-3, ASME, New York, pp. 35–42.
Kessel, J.-A., Schaffnit, J., and Schmidt, M., 1998, “Modelling and Real Time Simulations of a Turbocharger With Variable Turbine Geometry (VTG),” SAE Paper No. 980770.
Lippmann,  R. P., 1987, “An Introduction to Computing With Neural Nets,” IEEE ASSP Mag.4(2), pp. 4–22.
Rumelhart,  D. E., Hinton,  G. E., and Willians,  R. J., 1986, “Learning Representations by Back Propagating Errors,” Nature (London), 323, pp. 533–536.
Rumelhart, D. E., Hinton, G. E., and Willians, R. J., 1986, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, Volume 1: Foundations, M.I.T. Press, Cambridge, MA.
Jacobs,  R. A., 1988, “Increased Rates of Convergence Through Learning Rate Adaptation,” Neural Networks, 1(4), pp. 295–308.
Watson, N., Pilley, A. D., and Marzouk, M., 1980, “A Combustion Correlation for Diesel Engine Simulation,” SAE Paper No. 800029.
Assanis, D. N., 1985, “A Computer Simulation of the Turbocharged Turbocompounded Diesel Engine System for Studies of Low Heat Rejection Engine Performance,” Ph.D. thesis, M.I.T., Cambridge, MA.
Beale, M., and Demuth, H., 1994, The Neural Net Toolbox User’s Guide, The Math Works Inc., Natick, MA.


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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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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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