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Integration of Diesel Engine, Exhaust System, Engine Emissions and Aftertreatment Device Models
Technical Paper
2005-01-0947
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually.
Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude. A system of controllers were implemented which limited the triggering of sub-models with very small time-steps so that simulation speed was maintained while minimizing the adverse effects on calculation accuracy.
Integration of the models allowed for the visualization of dynamic interactions between sub-models that were not seen when simulating individual components. An example of which was an interesting filter pressure drop overshoot during a speed-step transient simulation.
In both steady state and transient simulations, overall model results fit expectations. Three steady-state cases (a baseline, an increased fueling, and an increased engine speed) and two transient cases (baseline to increased fueling and baseline to increased speed) were analyzed. While numerous results were studied, pressure drop across the filter was emphasized.
Reasonable trends were observed. The system developed in this study will assist in the design and optimization of diesel automotive systems for reduction of tailpipe emissions.
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Authors
- David J. Kapparos - Engine Research Center, University of Wisconsin-Madison
- Indranil Brahma - Engine Research Center, University of Wisconsin-Madison
- Andrea Strzelec - Engine Research Center, University of Wisconsin-Madison
- Christopher J. Rutland - Engine Research Center, University of Wisconsin-Madison
- David E. Foster - Engine Research Center, University of Wisconsin-Madison
- Yongsheng He - General Motors Research & Development
Topic
Citation
Kapparos, D., Brahma, I., Strzelec, A., Rutland, C. et al., "Integration of Diesel Engine, Exhaust System, Engine Emissions and Aftertreatment Device Models," SAE Technical Paper 2005-01-0947, 2005, https://doi.org/10.4271/2005-01-0947.Also In
Diesel Exhaust Emission Control and Modeling on CD-ROM from the SAE 2005 World Congress
Number: SP-1981CD; Published: 2005-04-11
Number: SP-1981CD; Published: 2005-04-11
References
- Kapparos, D.J. ‘Thermal Studies of Diesel Engine Exhaust Systems’ Department of Mechanical Engineering, University of Wisconsin-Madison Madison, WI 2004
- Kapparos, D.J Foster, D.E. Rutland, C.J. “Sensitivity Analysis of a Diesel-Exhaust System Thermal Model” SAE Paper No 2004-01-1131 2004
- He, Y. Rutland, C.J. “Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks” SAE Paper No 2002-01-2772 2002
- He, Y. Rutland, C.J. “Application of Artificial Neural Networks in Engine Modeling,” International Journal of Engine Research 5 4 281 296 2004
- He, Y. “Development of a Diesel Engine Simulation Tool using Artificial Neural Networks” Ph.D thesis Dept. of Mechanical Engineering, University of Wisconsin-Madison 2002
- Traver, M.L. Atkinson, R.J. Atkinson, C.M. “Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure” SAE Paper No 1999-01-1532 1999
- Desantes, J.M Lopez J.J. Garcia, J.M. Hernandez, L. “Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H.H. Diesel Engine” SAE Paper No 2002-01-1144 2002
- Lichtenthaler, D. Ayeb, M. Theuerkauf, H.J. Winsel, T. “Improving Real-Time SI Engine Models by Integration of Neural Approximators” SAE Paper No 1999-01-1164 1999
- Brahma, I. He, Y. Rutland, C.J. “Improvement of Neural Network Accuracy for Engine Simulations” SAE Paper No 2003-01-3227 2003
- Bayer, J. Foster, D.E. “Zero-Dimensional Soot Modeling” SAE Paper No 2003-01-1070 2003
- Reitz, R.D. Bracco, F.B. “On the Dependence of Spray Angle and Other Spray Parameters on Nozzle Design and Operating Conditions” SAE Paper No 790494 1979
- Siebers, Dennis “Scaling Liquid-Phase Fuel Penetration in Diesel Sprays Based on Mixing-Limited Vaporization” SAE Paper No 1999-01-0528 1999
- Siebers Dennis Dr. “Unpublished Notes” Sandia National Laboratory 2002
- Chomiak, Jerzy Karlsson, Anders “Flame Liftoff in Diesel Sprays” 26 th International Symposium on Combustion 1996 2557 2564
- Hiroyasu, Kadota “Models for Combustion and Formation of Nitric Oxide and Soot in Direct Injection Diesel Engines” SAE Paper No 760129 1976
- Bayer, J. “Zero-Dimensional Direct Injection Spray and Particulate Emission Modeling” MS thesis Dept. of Mechanical Engineering, University of Wisconsin-Madison 2003
- Huynh, C.T. ‘A Study of the Filtration and Regeneration Characteristics of a Catalyzed Wall-Flow Diesel Particulate Filter: One-Dimensional Model Calibrated and Validated with Experimental Data,’ Department of Mechanical Engineering, Michigan Technological University Houghton, MI i 2002
- Huynh, C.T. Johnson, J.H. Yang, S.L. Bagley, S.T. Warner, J.R. “A One-Dimensional Computational Model for Studying the Filtration and Regeneration Characteristics of a Catalyzed Wall-Flow Diesel Particulate Filter.” SAE Paper No 2003-01-0841 2003
- WAVE v5 Co-Simulation Reference Manual 2002