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Definition of a Methodology Promoting the Use of 1D Thermo-Fluid Dynamic Analysis for the Reduction of the Experimental Effort in Engine Base Calibration
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 9, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Over the last decades, internal combustion engines have undergone a continuous evolution to achieve better performance, lower pollutant emissions and reduced fuel consumption. The pursuit of these often-conflicting goals involved changes in engine architecture in order to carry out advanced management strategies. Therefore, Variable Valve Actuation, Exhaust Gas Recirculation, Gasoline Direct Injection, turbocharging and powertrain hybridization have found wide application in the automotive field. However, the effective management of a such complex system is due to the contemporaneous development of the on-board Engine electronic Control Unit. In fact, the additional degrees of freedom available for the engine regulation highly increased the complexity of engine control and management, resulting in a very expensive and long calibration process. To overcome these drawbacks, an effective methodology based on the adoption of 1D thermo-fluid dynamic analysis is proposed in this study. In particular, starting from a complete experimental set of data actually used for the base calibration of a reference spark ignition engine, a novel procedure based on vector optimization approach is used to reliably calibrate a 1D engine model starting from a reduced experimental dataset. Once validated, the engine model is then used as a virtual test bench to reproduce the experimental campaign numerically, thus obtaining a detailed and complete dataset exploitable for calibration purposes, here called numerical or virtual dataset. To verify the potential of the proposed methodology, experimental and virtual dataset have been finally compared. The research clearly demonstrates the effectiveness of the proposed approach since the average errors are comparable with the measurement errors. Therefore, the methodology shows promising results concerning the use of numerical dataset obtained from reliable 1D CFD engine models as input to computer aided calibration software. This way, a significant cut to the experimental campaign required for calibration purposes is achieved, with their related times and costs.
Citationde Nola, F., Giardiello, G., Gimelli, A., Molteni, A. et al., "Definition of a Methodology Promoting the Use of 1D Thermo-Fluid Dynamic Analysis for the Reduction of the Experimental Effort in Engine Base Calibration," SAE Technical Paper 2019-24-0013, 2019, https://doi.org/10.4271/2019-24-0013.
Data Sets - Support Documents
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- Napolitano, P. et al. , “Study of the Effect of the Engine Parameters Calibration to Optimize the Use of Bio-Ethanol/RME/Diesel Blend in a Euro 5 Light Duty Diesel Engine,” SAE International Journal of Fuels and Lubricants 6(1):263-275, April 2013.
- Beatrice, C. et al. , “Emission Reduction Technologies for the Future Low Emission Rail Diesel Engines: EGR Vs SCR,” SAE Technical Paper 2013-24-0087 6, 2013, doi:10.4271/2013-24-0087.
- Bozza, F. et al. , “Strategies for Improving Fuel Consumption at Part-Load in a Downsized Turbocharged SI Engine: A Comparative Study,” SAE International Journal of Engines 7(1), 2014, doi:10.4271/2014-01-1064.
- De Simio, L. et al. , “Experimental Analysis of a Natural Gas Fueled Engine and 1-D Simulation of VVT and VVA Strategies,” SAE Technical Paper 2013-24-0111, 2013, doi:10.4271/2013-24-0111.
- Bozza, F. et al. , “Pre-Lift Valve Actuation Strategy for the Performance Improvement of a DISI VVA Turbocharged Engine,” Energy Procedia, 45, 819-828, 2014, ISSN: 18766102. DOI:10.1016/j.egypro.2014.01.087.
- De Bellis, V. et al. , “Effects of Pre-Lift Intake Valve Strategies on the Performance of a DISI VVA Turbocharged Engine at Part and Full Load Operation,” Energy Procedia, 81, December 2015, 874-882, ISSN: 1876-6102, doi:10.1016/j.egypro.2015.12.141.
- Gimelli, A. et al. , “Study of a New Mechanical VVA System. Part I: Valve Train Design and Friction Modeling,” International Journal of Research Engines 16(6):750-761, SEP 2015, doi:10.1177/1468087414548773.
- Gimelli, A. et al. , “Study of a New Mechanical VVA System. Part II: Estimation of the Actual Fuel Consumption Improvement through 1D Fluid Dynamic Analysis and Valve Train Friction Estimation,” International Journal of Engine Research 16(6):762-772, SEP 2015, doi:10.1177/1468087414548773.
- Rask, E. and Sellnau, M. , “Simulation-Based Engine Calibration: Tools, Techniques, and Applications,” SAE Technical Paper 2004-01-1264, 2004, doi:10.4271/2004-01-1264.
- Gerhardt, J. et al. , “A New Approach to Functional and Software Structure for Engine Management Systems - BOSCH ME7,” SAE Technical Paper 980801, 1998, doi:10.4271/980801.
- de Nola, F. et al. , “A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings through Regression Techniques and Neural Networks,” SAE Technical Paper 2017-24-0054, 2017, doi:10.4271/2017-24-0054.
- de Nola, F. et al. , “Enhancing the Accuracy of Engine Calibration through a Computer Aided Calibration Algorithm,” Energy Procedia 148:916-923, 2018, doi:10.1016/j.egypro.2018.08.094.
- de Nola, F. et al. , “Reduction of the Experimental Effort in Engine Calibration by Using Neural Networks and 1D Engine Simulation,” Energy Procedia 148:344-351, August 2018, doi:10.1016/j.egypro.2018.08.087.
- Raynaud, Y. et al. , “Application of Adaptive Online DOE Techniques for Engine ECU Calibration,” in 2002 IMECHE Conference on Statistics and Analytical Methods in Automotive Engineering.
- Stuhler, H. et al. , “Automated Model-Based GDI Engine Calibration Adaptive Online DOE Approach,” SAE Technical Paper 2002-01-0708, 2002, doi:10.4271/2002-01-0708.
- Riegel, A., Montalto, I., and De Cristofaro, F. , 2007, “Interactive Optimization Methodology for Robust Base Engine Calibration,” European Automotive Congress, Budapest.
- de Nola, F., Giardiello, G., Gimelli, A., Molteni, A. et al. , “Volumetric Efficiency Estimation Based on Neural Networks to Reduce the Experimental Effort in Engine Base Calibration,” Fuel 244(15):31-39, May 2019, doi:10.1016/j.fuel.2019.01.182.
- Stinstra, E., and et al. , “Design Optimization: Some Pitfalls and Their Remedies,” Eindhoven, 2001, Netherlands, Centre for Quantitative Methods.
- Chen, S. K. and Flynn, P. F. , “Development of a Single Cylinder Compression Ignition Research Engine,” SAE Technical Paper 650733, 1965, doi:10.4271/650733.
- Hires, S. D., Tabaczynski, R. J., and Novak, J. M. , “The Prediction of Ignition Delay and Combustion Intervals for a Homogeneous Charge, Spark Ignition Engine,” SAE Technical Paper 780232, 1978, doi:10.4271/780232.
- Blizard, N. C. and Keck, J. C. , “Experimental and Theoretical Investigation of Turbulent Burning Model for Internal Combustion Engine,” SAE Technical Paper 740191, 1974, doi:10.4271/740191.
- Heywood, J. B. , Internal Combustion Engine Fundamentals (McGraw-Hill, 1988). ISBN:0-07-1004999-8.
- Shafie, N. A. M. et al. , “Discharge and Flow Coefficient Analysis in Internal Combustion Engine Using Computational Fluid Dynamics Simulation”, ARPN Journal of Engineering and Applied Sciences, ISSN: 1819-6608, 12, 8, april 2017.
- Bordjane, M., Chalet, D. , “Numerical Investigation of Throttle Valve Flow Characteristics for Internal Combustion Engines,” Journal of Multidisciplinary Engineering Science and Thecnology, ISSN: 3159-0040, 2, 12, December 2015.
- Morel, T., Rackmil, C. I., Keribar, R., and Jennings, M. J. , “Model for Heat Transfer and Combustion in Spark-Ignited Engine and its Comparison with Experiments,” SAE Technical Paper 880198, 1988, doi:10.4271/880198.