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Computational optimization of the piston bowl geometry for the different combustión regimes of the dual-mode dual-fuel (DMDF) concept through an improved genetic algorithm

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Computational optimization of the piston bowl geometry for the different combustión regimes of the dual-mode dual-fuel (DMDF) concept through an improved genetic algorithm

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dc.contributor.author Xu, Guangfu es_ES
dc.contributor.author García Martínez, Antonio es_ES
dc.contributor.author Jia, Ming es_ES
dc.contributor.author Monsalve-Serrano, Javier es_ES
dc.date.accessioned 2022-07-04T18:03:54Z
dc.date.available 2022-07-04T18:03:54Z
dc.date.issued 2021-10-15 es_ES
dc.identifier.issn 0196-8904 es_ES
dc.identifier.uri http://hdl.handle.net/10251/183794
dc.description.abstract [EN] Focusing on the dual-mode dual-fuel (DMDF) combustion concept, a combined optimization of the piston bowl geometry with the fuel injection strategy was conducted at various loads. An improved genetic algorithm was introduced in this study, which is superior in searching for the global optimal solutions. The optimal piston bowl shape coupled with the corresponding injection strategy was summarized at the various loads. The results show that the piston bowl geometry optimization can further improve the thermal efficiency with 1.4%, 4.4%, and 1.4% percentage points for the low, mid, and high loads, respectively. An indicated thermal efficiency up to 51.8% can be realized at mid load. Meanwhile, for all the optimal cases, NOx and soot emissions can meet the Euro VI limits. At low and mid loads, both the open and re-entrant type piston bowl can be equipped, while the high load only prefers the open type piston bowl for the DMDF mode. The re-entrant type or deep piston bowls are superior in organizing strong in-cylinder flow, which is beneficial for the fuel/air mixing. The open type or shallow piston bowls are helpful for reducing the heat transfer losses owing to the less heat transfer surface area. Furthermore, a correlation analysis was conducted to investigate the sensitivity of engine performance to the piston geometric parameters and injection parameters. It is concluded that the fuel injection event becomes more important for managing the engine performance as load increases. Among the injection parameters, the influence of the fuel injection timings and injection pressure on engine performance is more obvious. The piston geometric parameters play more significant roles in the heat transfer losses than the injection parameters for all loads. Among the geometric parameters, the most influential parameters are the width and open extent of the piston bowl. The heat transfer loss energy fraction can be well decreased with a wider and more open piston bowl. es_ES
dc.description.sponsorship This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 51961135105 and 91641117) and Postdoctoral Research Foundation of China (Grant Nos. 2019M661094 and 2020T130075). The experimental results used in this investigation were obtained in a project funded by VOLVO Group Trucks Technology. The authors also acknowledge FEDER and Spanish Ministerio de Economia y Competitividad for partially supporting this research through TRANCO project (TRA2017-87694-R) and the Universitat Politecnica de Val`encia for partially supporting this research through Convocatoria de ayudas a Primeros Proyectos de Investigacion (PAID-06-18). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Energy Conversion and Management es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Piston bowl geometry optimization es_ES
dc.subject Dual-mode dual-fuel (DMDF) es_ES
dc.subject Genetic algorithm es_ES
dc.subject Fuel efficiency es_ES
dc.subject Correlation analysis es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Computational optimization of the piston bowl geometry for the different combustión regimes of the dual-mode dual-fuel (DMDF) concept through an improved genetic algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enconman.2021.114658 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TRA2017-87694-R/ES/REDUCCION DE CO2 EN EL TRANSPORTE MEDIANTE LA INYECCION DIRECTA DUAL-FUEL DE BIOCOMBUSTIBLES DE SEGUNDA GENERACION/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/China Postdoctoral Science Foundation//2020T130075/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//51961135105/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//91641117/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/China Postdoctoral Science Foundation//2019M661094/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV-VIN//SP20180148//Estudio del potencial de la combustión Dual-Fuel Gas Natural/Diesel para la reducción de las emisiones de CO2 en vehículos destinados al transporte por carretera (DUGAS)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics es_ES
dc.description.bibliographicCitation Xu, G.; García Martínez, A.; Jia, M.; Monsalve-Serrano, J. (2021). Computational optimization of the piston bowl geometry for the different combustión regimes of the dual-mode dual-fuel (DMDF) concept through an improved genetic algorithm. Energy Conversion and Management. 246:1-15. https://doi.org/10.1016/j.enconman.2021.114658 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.enconman.2021.114658 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 246 es_ES
dc.relation.pasarela S\444733 es_ES
dc.contributor.funder Volvo Group Trucks Technology es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder UNIVERSIDAD POLITECNICA DE VALENCIA es_ES
dc.contributor.funder China Postdoctoral Science Foundation es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES


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