- -

Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine

Mostrar el registro completo del ítem

Torregrosa, AJ.; Broatch, A.; Olmeda, P.; Aceros, S. (2021). Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine. SAE International. 1-10. https://doi.org/10.4271/2021-01-0379

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/190749

Ficheros en el ítem

Thumbnail
Nombre: TorregrosaBroatch ...
Tamaño: 747.2Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: PC-2021-04.pdf
Tamaño: 1.377Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine
Autor: Torregrosa, A. J. Broatch, A. Olmeda, P. Aceros, Sebastian
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny
Fecha difusión:
Resumen:
[EN] In modeling an Internal Combustion Engine, the combustion sub-model plays a critical role in the overall simulation of the engine as it provides the Mass Fraction Burned (MFB). Analytically, the Heat Release Rate (HRR) ...[+]
Derechos de uso: Reserva de todos los derechos
Fuente:
SAE Technical Papers. (issn: 0148-7191 )
DOI: 10.4271/2021-01-0379
Editorial:
SAE International
Versión del editor: https://doi.org/10.4271/2021-01-0379
Título del congreso: SAE World Congress Experience (WCX 2021)
Lugar del congreso: Online
Fecha congreso: Abril 13-15,2021
Tipo: Comunicación en congreso Artículo

References

Krzyzanowski, M., Kuna-Dibbert, B., and Schneider, J. , Health Effects of Transport-related Air Pollution (2005), ISBN:9289013737.

Heck, R.M., and Farrauto, R.J. , “Automobile Exhaust Catalysts,” Appl. Catal. A Gen. 221(1-2):443-457, 2001, doi:10.1016/S0926-860X(01)00818-3.

Attard, W.P., Konidaris, S., Hamori, F., Toulson, E., and Watson, H.C. , “Compression Ratio Effects on Performance, Efficiency, Emissions and Combustion in a Carbureted and PFI Small Engine,” SAE Technical Paper 2007-01-3623, 2007, https://doi.org/10.4271/2007-01-3623. [+]
Krzyzanowski, M., Kuna-Dibbert, B., and Schneider, J. , Health Effects of Transport-related Air Pollution (2005), ISBN:9289013737.

Heck, R.M., and Farrauto, R.J. , “Automobile Exhaust Catalysts,” Appl. Catal. A Gen. 221(1-2):443-457, 2001, doi:10.1016/S0926-860X(01)00818-3.

Attard, W.P., Konidaris, S., Hamori, F., Toulson, E., and Watson, H.C. , “Compression Ratio Effects on Performance, Efficiency, Emissions and Combustion in a Carbureted and PFI Small Engine,” SAE Technical Paper 2007-01-3623, 2007, https://doi.org/10.4271/2007-01-3623.

Payri González, F. and Desantes Fernández, J.M. , “Motores de Combustión Interna Alternativos,” Editorial Universitat Politècnica de València, ISBN 9788483637050, 2011.

De Bellis, V., Bozza, F., and Tufano, D. , “A Comparison between Two Phenomenological Combustion Models Applied to Different SI Engines,” SAE Technical Paper 2017-01-2184, 2017, https://doi.org/10.4271/2017-01-2184.

Giglio, V., and Di Gaeta, A. , “Novel Regression Models for Wiebe Parameters Aimed at 0D Combustion Simulation in Spark Ignition Engines,” Energy 210:118442, 2020, doi:10.1016/j.energy.2020.118442.

Payri, F., Molina, S., Martín, J., and Armas, O. , “Influence of Measurement Errors and Estimated Parameters on Combustion Diagnosis,” Appl. Therm. Eng. 26(2-3):226-236, 2006, doi:10.1016/j.applthermaleng.2005.05.006.

Heywood, J.B., Higgins, J.M., Watts, P.A., and Tabaczynski, R.J. , Development and Use of a Cycle Simulation to Predict SI Engine Efficiency and NOx Emissions, 1979, https://doi.org/10.4271/790291.

Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A.E., and Arshad, H. , “State-of-the-Art in Artificial Neural Network Applications: A Survey,” Heliyon 4(11):e00938, 2018, doi:10.1016/j.heliyon.2018.e00938.

Eyercioglu, O., Kanca, E., Pala, M., and Ozbay, E. , “Prediction of Martensite and Austenite Start Temperatures of the Fe-Based Shape Memory Alloys by Artificial Neural Networks,” J. Mater. Process. Technol. 200(1-3):146-152, 2008, doi:10.1016/j.jmatprotec.2007.09.085.

Yang, I.H., Yeo, M.S., and Kim, K.W. , “Application of Artificial Neural Network to Predict the Optimal Start Time for Heating System in Building,” Energy Convers. Manag. 44(17):2791-2809, 2003, doi:10.1016/S0196-8904(03)00044-X.

Kalogirou, S.A. , “Artificial Neural Networks in Renewable Energy Systems Applications: A Review,” Renew. Sustain. Energy Rev. 5(4):373-401, 2000, doi:10.1016/S1364-0321(01)00006-5.

Yadav, A.K., and Chandel, S.S. , “Solar Radiation Prediction Using Artificial Neural Network Techniques: A Review,” Renew. Sustain. Energy Rev. 33:772-781, 2014, doi:10.1016/j.rser.2013.08.055.

Abhishek, K., Kumar, A., Ranjan, R., and Kumar, S. , “A Rainfall Prediction Model Using Artificial Neural Network,” in Proc. - 2012 IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2012 (Icsgrc):82-87, 2012, doi:10.1109/ICSGRC.2012.6287140.

Kara Togun, N., and Baysec, S. , “Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks,” Appl. Energy 87(1):349-355, 2010, doi:10.1016/j.apenergy.2009.08.016.

Deh Kiani, M.K., Ghobadian, B., Tavakoli, T., Nikbakht, A.M., and Najafi, G. , “Application of Artificial Neural Networks for the Prediction of Performance and Exhaust Emissions in SI Engine Using Ethanol-gasoline Blends,” Energy 35(1):65-69, 2010, doi:10.1016/j.energy.2009.08.034.

Roy, S., Banerjee, R., and Bose, P.K. , “Performance and Exhaust Emissions Prediction of a CRDI Assisted Single Cylinder Diesel Engine Coupled with EGR Using Artificial Neural Network,” Appl. Energy 119:330-340, 2014, doi:10.1016/j.apenergy.2014.01.044.

Çay, Y., Korkmaz, I., Çiçek, A., and Kara, F. , “Prediction of Engine Performance and Exhaust Emissions for Gasoline and Methanol Using Artificial Neural Network,” Energy 50(1):177-186, 2013, doi:10.1016/j.energy.2012.10.052.

Oǧuz, H., Saritas, I., and Baydan, H.E. , “Prediction of Diesel Engine Performance using Biofuels with Artificial Neural Network,” Expert Syst. Appl. 37(9):6579-6586, 2010, doi:10.1016/j.eswa.2010.02.128.

Tarabet, L., Lounici, M.S., Loubar, K., and Tazerout, M. , “Dual Wiebe Function Prediction of Eucalyptus Biodiesel/Diesel Fuel Blends Combustion in Diesel Engine Applying Artificial Neural Network,” SAE Technical Paper 2014-01-2555, 2014, https://doi.org/10.4271/2014-01-2555.

Payri, F., Olmeda, P., Martín, J., and García, A. , “A Complete 0D Thermodynamic Predictive Model for Direct Injection Diesel Engines,” Appl. Energy 88(12):4632-4641, 2011, doi:10.1016/j.apenergy.2011.06.005.

Lapuerta, M., Armas, O., and Hernández, J.J. , “Diagnosis of DI Diesel Combustion from In-Cylinder Pressure Signal by Estimation of Mean Thermodynamic Properties of the Gas,” Appl. Therm. Eng. 19(5):513-529, 1999, doi:10.1016/S1359-4311(98)00075-1.

Olmeda, P., Martín, J., Novella, R., and Carreño, R. , “An Adapted Heat Transfer Model for Engines with Tumble Motion,” Appl. Energy 158:190-202, 2015, doi:10.1016/j.apenergy.2015.08.051.

Broatch, A., Olmeda, P., García, A., Salvador-Iborra, J., and Warey, A. , “Impact of Swirl on In-Cylinder Heat Transfer in a Light-Duty Diesel Engine,” Energy 119:1010-1023, 2017, doi:10.1016/j.energy.2016.11.040.

Torregrosa, A., Olmeda, P., Degraeuwe, B., and Reyes, M. , “A Concise Wall Temperature Model for di Diesel Engines,” Appl. Therm. Eng. 26(11-12):1320-1327, 2006, doi:10.1016/j.applthermaleng.2005.10.021.

Cooney, C., Worm, J., Michalek, D., and Naber, J. , “Wiebe Function Parameter Determination for Mass Fraction Burn Calculation in an Ethanol-Gasoline Fuelled SI Engine,” J. KONES 15(3):567-574, 2008.

Serrano, J.R., Climent, H., Guardiola, C., and Piqueras, P. , “Methodology for Characterisation and Simulation of Turbocharged Diesel Engines Combustion During Transient Operation. Part 2: Phenomenological Combustion Simulation,” Appl. Therm. Eng. 29(1):150-158, 2009, doi:10.1016/j.applthermaleng.2008.02.010.

Yeliana, Y., Cooney, C., Worm, J., Michalek, D.J., and Naber, J.D. , “Estimation of Double-Wiebe Function Parameters Using Least Square Method for Burn Durations of Ethanol-Gasoline Blends in Spark Ignition Engine over Variable Compression Ratios and EGR Levels,” Appl. Therm. Eng. 31(14-15):2213-2220, 2011, doi:10.1016/j.applthermaleng.2011.01.040.

Yu, H. and Wilamowski, B.M. , “Levenberg-Marquardt Training,” Industrial Electronics Handbook, ISBN 10.1201/b10604: 12.1-12.16, 2011.

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem