Dossier: New Trends on Engine Control, Simulation and Modelling
Open Access
Issue
Oil & Gas Science and Technology - Rev. IFP
Volume 62, Number 4, July-August 2007
Dossier: New Trends on Engine Control, Simulation and Modelling
Page(s) 539 - 553
DOI https://doi.org/10.2516/ogst:2007047
Published online 06 December 2007
  • Ahmad, T. and Theobald, M.A. (1989) A survey of variable-valve-actuation technology. SAE Technical Paper No. 891674. [Google Scholar]
  • Hannibal, W., Flierl, R., Stiegler, L. and Meyer, R. (2004) Overview of Current Continuously Variable Valve Lift Systems for Four-Stoke Spark-Ignition Engines and the Criteria for Their Design Ratings. SAE Technical Paper No. 2004-01-1263. [Google Scholar]
  • Flierl, R. and Fluting, M. (2000) The Third Generation of Valvetrains – New Fully Variable Valvetrains for Throttle-FreeLoad Control. SAE Technical Paper No. 2000-01-1227. [Google Scholar]
  • Nakamura, M., Hara, S., Yamada, Y., Takeda, K., Okamoto, N. and Hibi, T. (2001) A Continuous Variable Valve Event and Lift Control Device (VEL) for Automotive Engines. SAE Technical Paper No. 2001-01-0244. [Google Scholar]
  • Nishizawa, K., Mitsuishi, S., Mori, K. and Yamamoto, S. (2001) Development of Second Generation of Gasline P-ZEV Technology. SAE Technical Paper No. 2001-01-1310. [Google Scholar]
  • Hong, H.,Parvate-Patil, G.B. and Gordon, B. (2004) Review and Analysis of Variable Valve Timing Strategies-Eight Ways to Approach. Proceedings of the Institution of Mechanical Engineers, Part D. J. Automobile Eng., 218, 1179-1200. [CrossRef] [Google Scholar]
  • Gray, C. (1988) A review of variable engine valve timing. SAE Technical Paper No. 880386. [Google Scholar]
  • Dresner, T. and Barkan, P. (1989) A review of variable valvetiming benefits and modes of operation. SAE Technical Paper No. 891676. [Google Scholar]
  • Asmus, T.W. (1991) Perspectives on applications of variable valve timing. SAE Technical Paper No. 910445. [Google Scholar]
  • Ma, T.H. (1988) Effect of variable engine valve timing on fuel economy. SAE Technical Paper No. 880390. [Google Scholar]
  • Bohac, S. and Assanis, D. (2004) Effects of Exhaust Valve Timing on Gasoline Engine Performance and Hydrocarbon Emissions. SAE Technical Paper No. 2004-01-3058. [Google Scholar]
  • Roepke, K. and Fischer, M. (2001) Efficient Layout and Calibration of Variable Valve Trains. SAE Technical Paper 2001-01-0668. [Google Scholar]
  • Flint, S. and Causey, P. (2003) Use of Experimental Design and Two Stage Modeling in Calibration Generation for Variable Camshaft Timing Engines, Design of Experiments (DOE), in der Motorenentwicklung, Expert Verlag, ISBN 3-8169-2271-6, pp. 57-77. [Google Scholar]
  • Morton, T., Connors, R., Maloney, P. and Sampson, D. (2003) Model-Based Optimal Calibration of a Dual Independent Variable Valve-Timing Engine, Design of Experiments (DOE), in der Motorenentwicklung, Expert Verlag, ISBN 3-8169-2271-6, pp. 77-85. [Google Scholar]
  • Rask, E. and Sellnau, M. (2004) Simulation-Based Engine Calibration: Tools, Techniques, and Applications. SAE Technical Paper No. 2004-01-1264. [Google Scholar]
  • Fu, H., Chen, X., Mustafa, E., Trigui, N., Richardson, S. and Shilling, I. (2004) Analytical Investigation of Cam Strategies for SI Engine Part-Load Operation. SAE Technical Paper No. 200401-0997. [Google Scholar]
  • Kramer, U. and Philips, P. (2002) Phasing Strategy for an Engine with Twin Variable Cam Timing. SAE Technical Paper No. 2002-01-1101. [Google Scholar]
  • Wu, B., Prucka, R.G., Filipi, Z.S., Kramer, D.M. and Ohl, G.L. (2005) Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models – Maximizing Torque Output. SAE Technical Paper No. 2005-01-3757. SAE Trans. – J. Engines. [Google Scholar]
  • Mehrotra, K., Mohan, C.K., Ranka, S. (1997) Elements of Artificial Neural Networks, the MIT Press, Cambridge ,Massachusetts, ISBN 0-262-13328-8. [Google Scholar]
  • He, Y. and Rutland, C.J. (2002) Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks. SAE Technical Paper No. 2002-01-2772. [Google Scholar]
  • He, Y. and Rutland, C.J. (2004) Application of Artificial Neural Networks in Engine Modeling. Int. J. Engine Res., 5, 4, 281-296. [CrossRef] [Google Scholar]
  • Brahma, I. and Rutland, C.J. (2003) Optimization of Diesel Engine Operating Parameters Using Neural Networks. SAE Technical Paper No. 2003-01-3228. [Google Scholar]
  • Grimaldi, C.N. and Mariani, F. (1997) On-board Diagnosis of Internal Combustion Engines: a New Model Definition and Experimental Validation. SAE Technical Paper No. 970211. [Google Scholar]
  • Grimaldi, C.N. and Mariani, F. (2001) OBD Engine Fault Detection Using a Neural Approach. SAE Technical Paper No. 2001-01-0559. [Google Scholar]
  • Krug, C., Liebl, J., Munk, F., Kammer, A. and Reuss, H.-C. (2004) Physical Modelling and Use of Modern System Identification for Real-Time Simulation of Spark Ignition Engines in All Phases of Engine Development. SAE Technical Paper No. 2004-01-0421. [Google Scholar]
  • Winsel, T., Ayeb, M., Theuerkauf, H.J., Pischinger, S., Schernus, C. and Lutkemeyer, G. (2004) HiL-Calibration of SI Engine Cold Start and Warm-up Using Neural Real-Time Model. SAE Technical Paper No. 2004-01-1362. [Google Scholar]
  • Ayeb, M., Lichtenthäler, D., Winsel, T. and Theuerkauf, H.J. (1998) SI engine modeling using neural networks. SAE Technical Paper No. 980790. [Google Scholar]
  • Wu, B., Filipi, Z.S., Assanis, D.A., Kramer, D.M., Ohl, G.L., Prucka, M.J. and DiValentin, E. (2004) Using artificial neural networks for representing the air flow rate through a 2.4 liter VVT engine. SAE Technical Paper No. 2004-01-3054. SAE Trans. – J. Engines. [Google Scholar]
  • Hornik, K.,Stinchcombe, M., and White, H. (1989) Multilayer feedforward networks are universal approximators. Neural Networks, 2, 5, 359-366. [CrossRef] [Google Scholar]
  • Hornik, K. (1991) Approximation capabilities of multilayer feedforward networks. Neural Networks, 4, 2, 251-257. [CrossRef] [MathSciNet] [Google Scholar]
  • Wu, B., Filipi, Z.S., Prucka, R.G., Kramer, D.M. and Ohl, G.L. (2006) Cam-phasing Optimization Using Artificial Neural Networks as Surrogate Model – Fuel Consumption and NOx Emissions, SAE paper 2006-01-1512. SAE Trans., J. Engines, presented at the 2006 SAE World Congress in Detroit. [Google Scholar]
  • WAVE V5 Engine Reference Manual (2002) Ricardo Software, Ricardo, Inc., November. [Google Scholar]
  • Morel, T., Flemming, M. and LaPointe, L.A. (1990) Characterization of Manifold Dynamics in the Chrysler 2.2l S.I. Engine by Measurements and Simulation. SAE Technical Paper No. 900679. [Google Scholar]
  • Wren, C.S. and Johnson, O. (1995) Gas Dynamics Simulationfor the Design of Intake and Exhaust Systems – Latest Techniques. SAE Technical Paper No. 951367. [Google Scholar]
  • Millo, F., Ferraro, C.V. and Pilo, L. (2000) A Contribution to Engine and Vehicle Performance Prediction. SAE Technical Paper No. 2000-01-1266. [Google Scholar]
  • Filipi, Z., and Assanis, D.N. (1991) Quasi-dimensional computer simulation of the turbocharged spark-ignition engine and its usefor 2- and 4-valve engine matching studies. SAE Technical Paper No. 910075. SAE Trans., 100, Sect. 3. [Google Scholar]
  • Filipi, Z. (1994) Investigation of Variable Valve Area Strategies for a Turbocharged SI-Engine. Proceedings of the IMechE 5th International Conference on Turbocharging and Turbochargers, London, pp. 93-102. [Google Scholar]
  • Filipi, Z.S. and Assanis, D.N. (2000) The Effect of Stroke-to-Bore Ratio on Combustion, Heat Transfer and Performance of a Homogeneous-Charge Spark-Ignited Engine of Given Displacement. Int. J. Engine Res., 1, 2, JER0500, London, 191-208. [CrossRef] [Google Scholar]
  • Tabaczynski, R.J.,Trinker, F.H. and Shannon, B.A. (1980) Further refinement and validation of a turbulent flame propagation model for spark-ignition engines. Combust. Flame, 39, 2,111-121. [Google Scholar]
  • Poulos, S.G. and Heywood, J.B. (1983) The effect of chamber geometry on spark-ignition engine combustion. SAE Technical Paper No. 830334. [Google Scholar]
  • Ho, S.Y. and Kuo, T. (1997) A Hydrocarbon Autoignition Model for Knocking Combustion in SI Engines. SAE Technical Paper No. 971672. [Google Scholar]
  • Wu, B., Filipi, Z., Kramer, D.M., Ohl, G.L., Prucka, M.J. and DiValentin, E. (2005) Using Neural Networks to Compensate Altitude Effects on the Air Flow Rate in Variable Valve Timing Engines. SAE Technical Paper No. 2005-01-0066. SAE Trans. – J. Engines. [Google Scholar]
  • McKay, M.D.,Beckman, R.J. and Conover, W.J. (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 2, 239-245. [Google Scholar]
  • Lunani, M., Sudjianto, A. and Johnson, P.L. (1995) Generating efficient training samples for neural networks using Latin Hypercube sampling. Proceedings of the 1995 Artificial Neural Networks in Engineering (ANNIE'95), St. Louis, MO, USA, Nov. 12-15, pp. 209-214. [Google Scholar]
  • Demuth, H. and Beale, M. (2002) Neural Network Toolbox User's Guide (Version 4), Mathworks Inc. [Google Scholar]
  • Rublewski, M.J. and Heywood, J.B. (2001) Modeling No Formation in Spark Ignition Engines With a Layered Adiabatic Core and Combustion Inefficiency Routine. SAE Technical paper No. 2001-01-1011. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.