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Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads

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Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads

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Nombre: Xu;Monsalve-Serrano;Ja ...
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dc.contributor.author Xu, Guangfu es_ES
dc.contributor.author Monsalve-Serrano, Javier es_ES
dc.contributor.author Jia, Ming es_ES
dc.contributor.author García Martínez, Antonio es_ES
dc.date.accessioned 2021-06-10T03:32:04Z
dc.date.available 2021-06-10T03:32:04Z
dc.date.issued 2020-03-15 es_ES
dc.identifier.issn 0196-8904 es_ES
dc.identifier.uri http://hdl.handle.net/10251/167741
dc.description.abstract [EN] The diesel/gasoline dual-mode dual-fuel (DMDF) combustion concept was optimized in a compression-ignition engine by combining the computational fluid dynamics (CFD) simulations with the genetic algorithm. Seven operating parameters with remarkable influences on the engine performance were chosen as the variables to be optimized for simultaneously minimizing the fuel efficiency, nitrogen oxides (NOx), and soot emissions. Moreover, the potential of the further improvement of the DMDF combustion concept was discussed, and the rationality of this strategy was demonstrated. The results indicate that, at low load, simultaneous improvement of the fuel economy and emissions can be realized by strengthening the homogeneous combustion. At mid load, the fuel economy can be improved by reducing the heat transfer losses, while the NOx emissions are sacrificed to some extent. At high load, improved fuel economy can be realized by transferring a part of diffusion combustion to premixed reactivity-controlled compression ignition (RCCI) combustion. Concerning the operating parameters, lower intake temperature is beneficial to decrease the transfer losses, and the control of intake temperature is crucial for the stable operation of DMDF combustion under wide load conditions. Overall, gross indicated thermal efficiency above 45% is achieved, and the NOx and soot emission can be maintained under the Euro 6 standard for the test load range. es_ES
dc.description.sponsorship This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 51961135105 and 91641117) and China Postdoctoral Science Foundation (Grant No. 2019M661094). 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 Valencia 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 Dual-mode dual-fuel (DMDF) es_ES
dc.subject Numerical simulation es_ES
dc.subject Genetic algorithm es_ES
dc.subject EURO VI emission standards es_ES
dc.subject Fuel efficiency es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.enconman.2020.112577 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-18/ 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/UPV//SP20180148/ 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.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.; Monsalve-Serrano, J.; Jia, M.; García Martínez, A. (2020). Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads. Energy Conversion and Management. 208:1-13. https://doi.org/10.1016/j.enconman.2020.112577 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.enconman.2020.112577 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 208 es_ES
dc.relation.pasarela S\402435 es_ES
dc.contributor.funder Volvo Group Trucks Technology es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund 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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