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Predictive Semi-Empirical NOx Model for Diesel Engine

Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

Abstract:

Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O2 concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO2/NOx ratio etc.

Keywords: Diesel engine, machine learning, NOx emission, semi-empirical.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298854

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References:


[1] Finesso, R., Marello, O., Misul, D., Spessa, E. et al., "Development and Assessment of Pressure-Based and Model-Based Techniques for the MFB50 Control of a Euro VI 3.0L Diesel Engine," SAE Int. J. Engines 10(4):2017, doi:10.4271/2017-01-0794.
[2] Pariotis, E. and Hountalas, D., "A New Quasi-Three Dimensional Combustion Model for Prediction of DI Diesel Engines' Performance and Pollutant Emissions," SAE Technical Paper 2003-01-1060, 2003, https://doi-org.proxy.lib.umich.edu/10.4271/2003-01-1060.
[3] Mobasheri, R., Peng, Z., Mirsalim, S.M., “Analysis the effect of advanced injection strategies on engine performance and pollutant emissions in a heavy duty DI-diesel engine by CFD modeling”, International Journal of Heat and Fluid Flow 33:59-69, 2012, doi: 10.1016/j.ijheatfluidflow.2011.10.004.
[4] O'Connor, J., White, C., and Charnley, M., "Optimising CFD Predictions of Diesel Engine Combustion and Emissions Using Design of Experiments: Comparison with Engine Measurements," SAE Technical Paper 982458, 1998.
[5] Verma, I., Meeks, E., Bish, E., Kuntz, M. et al., "CFD Modelling of the Effects of Exhaust Gas Recirculation (EGR) and Injection Timing on Diesel Combustion and Emissions," SAE Technical Paper 2017-01-0574, 2017, https://doi-org.proxy.lib.umich.edu/10.4271/2017-01-0574.
[6] Ericson, C., Westerberg, B., and Egnell, R., “Transient Emission Predictions With Quasi Stationary Models,” SAE Technical Paper 2005-01-3852, 2005, doi:10.4271/2005-01-3852.
[7] Subramaniam, M., Tomazic, D., Tatur, M., and Laermann, M., “An Artificial Neural Network-based Approach for Virtual NOx Sensing,” SAE Technical Paper 2008-01-0753, 2008, doi:10.4271/2008-01-0753.
[8] Sequenz, H., Isermann, R., “Emission Model Structures for an Implementation on Engine Control Units”, In Proc. Of the 18th IFAC World Congress, Milano, Italy, Aug. 28 - Sept. 2, 2011, doi:10.3182/20110828-6-IT-1002.03131.
[9] Hashemi, N., Clark, N.N., “Artificial neural network as a predictive tool for emissions from heavy-duty Diesel vehicles in Southern California”, International Journal of Engine. Research. 8(4):321-336, 2007, doi: 10.1243/14680874JER00807.
[10] Heywood, J. B., “Internal Combustion Engine Fundamentals,” McGraw- Hill Book Company, 1988.
[11] Lavoie, G. A., Heywood, J. B., and Keck, J. C., “Experimental and Theoretical Study of Nitric Oxide Formation in Internal Combustion Engines,” Combustion Science and Technology, vol. 1, pp. 313-326, 1970.
[12] Lee, J., Lee, S., Park, W., Min, K. et al., “The Development of Real-time NOx Estimation Model and its Application,” SAE Technical Paper 2013- 01-0243, 2013, doi:10.4271/2013-01-0243.
[13] Lebas, R., Fremovici, M., Font, G., and Le Berr, F., “A Phenomenological Combustion Model Including In-Cylinder Pollutants To Support Engine Control Optimisation Under Transient Conditions,” SAE Technical Paper 2011-01-1837, 2011, doi:10.4271/2011-01-1837.
[14] Walke, N., Marathe, N., and Nandgaonkar, M., “Simplified Combustion Pressure and NOx Prediction Model for DI Diesel Engine,” SAE Technical Paper 2013-26-0131, 2013, doi:10.4271/2013-26-0131.
[15] Hegarty, K., Favrot, R., Rollett, D., and Rindone, G., “Semi-Empiric Model Based Approach for Dynamic Prediction of NOx Engine Out Emissions on Diesel Engines,” SAE Technical Paper 2010-01-0155, 2010, doi:10.4271/2010-01-0155.
[16] Querel, C., “Modélisation des émissions de NOx pour le contrôle des moteurs diesel,” Rouen: Université de Rouen, 2013.
[17] Quérel, C., Grondin, O., and Letellier, C., “A Semi-Physical NOx Model for Diesel Engine Control,” SAE Technical Paper 2013-01-0356, 2013, doi:10.4271/2013-01-0356.
[18] Savva, N. and Hountalas, D., “Detailed Evaluation of a New Semi- Empirical Multi-Zone NOx Model by Application on Various Diesel Engine Configurations,” SAE Technical Paper 2012-01-1156, 2012, doi:10.4271/2012-01-1156.
[19] Wang, Y., He, Y., and Rajagopalan, S., “Design of Engine- Out Virtual NOx Sensor Using Neural Networks and Dynamic System Identification,” SAE Int. J. Engines 4(1):837-849, 2011, doi:10.4271/2011-01-0694.
[20] Saravanan, S., Nagarajan, G., Anand, S., and Sampath, S., “Correlation for thermal NOx formation in compression ignition (CI) engine fuelled with diesel and biodiesel,” Energy, vol. 42, pp. 401-410, 2012.
[21] Singh, N., Nagabushan-Venkatesh, P., Nigro, E., and Lack, A., “Development of the NOx Emission Model for the Heavy Duty Diesel Engine Application Using Combustion Characteristic Parameters,” SAE Technical Paper 2013-01-0532, 2013, doi:10.4271/2013-01-0532.
[22] Faghani, E., Andric, J., and Sjoblom, J., “Toward an Effective Virtual Powertrain Calibration System,” SAE Technical Paper 2018-01-0007, 2018, doi:10.4271/2018-01-0007.
[23] Cosadia, I., Silvestri, J., Papadimitriou, I., Maroteaux, D. et al., "Traversing the V-Cycle with a Single Simulation - Application to the Renault 1.5 dCi Passenger Car Diesel Engine," SAE Technical Paper 2013-01-1120, 2013.
[24] Engine performance application manual, GT SUITE, Gamma Technologies.
[25] Piano, A., Millo, F., Boccardo, G., Rafigh, M. et al., "Assessment of the Predictive Capabilities of a Combustion Model for a Modern Common Rail Automotive Diesel Engine," SAE Technical Paper 2016-01-0547, 2016.
[26] Lakshmidhar, U., “Multi-Cylinder TPA and DI-Pulse Model development using GT-SUITE” GT Conference NA 2017.
[27] Dec, J., "A Conceptual Model of DI Diesel Combustion Based on Laser-Sheet Imaging*," SAE Technical Paper 970873, 1997, https://doi org.proxy.lib.umich.edu/10.4271/970873