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

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

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dc.contributor.author Torregrosa, A. J. es_ES
dc.contributor.author Broatch, A. es_ES
dc.contributor.author Olmeda, P. es_ES
dc.contributor.author Aceros, Sebastian es_ES
dc.date.accessioned 2022-12-16T08:09:04Z
dc.date.available 2022-12-16T08:09:04Z
dc.date.issued 2021-04-15 es_ES
dc.identifier.issn 0148-7191 es_ES
dc.identifier.uri http://hdl.handle.net/10251/190749
dc.description.abstract [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) can be obtained using the Wiebe function, which is nothing more than a mathematical formulation of the MFB. The mentioned function depends on the following four parameters: efficiency parameter, shape factor, crankshaft angle, and duration of the combustion. In this way, the Wiebe function can be adjusted to experimentally measured values of the mass fraction burned at various operating points using a least-squares regression, and thus obtaining specific values for the unknown parameters. Nevertheless, the main drawback of this approach is the requirement of testing the engine at a given engine load/speed condition. Furthermore, the main objective of this study is to propose a predictive model of the Wiebe parameters for any operating point of the tested SI engine. For this purpose, an Artificial Neural Network (ANN) is developed from the experimental data. A criterion was defined to choose the best-trained network. Finally, the Wiebe parameters are estimated with the neural networks for different operating conditions. Moreover, the mass fractions burned generated from the Wiebe functions are compared with the respective experimental values from several operating points measured in the engine test bench. Small differences were found between the estimated and experimental mass fractions burned. Therefore, the effectiveness of the developed ANN model as a prediction tool for the engine MFB is verified. es_ES
dc.language Inglés es_ES
dc.publisher SAE International es_ES
dc.relation.ispartof SAE Technical Papers es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Numerical Estimation of Wiebe Function Parameters Using Artificial Neural Networks in SI Engine es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4271/2021-01-0379 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename SAE World Congress Experience (WCX 2021) es_ES
dc.relation.conferencedate Abril 13-15,2021 es_ES
dc.relation.conferenceplace Online es_ES
dc.relation.publisherversion https://doi.org/10.4271/2021-01-0379 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\435301 es_ES
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