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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/167741

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Title: Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads
Author: Xu, Guangfu Monsalve-Serrano, Javier Jia, Ming García Martínez, Antonio
UPV Unit: Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics
Issued date:
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 ...[+]
Subjects: Dual-mode dual-fuel (DMDF) , Numerical simulation , Genetic algorithm , EURO VI emission standards , Fuel efficiency
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Energy Conversion and Management. (issn: 0196-8904 )
DOI: 10.1016/j.enconman.2020.112577
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.enconman.2020.112577
Project ID:
info:eu-repo/grantAgreement/UPV//PAID-06-18/
...[+]
info:eu-repo/grantAgreement/UPV//PAID-06-18/
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/
info:eu-repo/grantAgreement/UPV//SP20180148/
info:eu-repo/grantAgreement/NSFC//51961135105/
info:eu-repo/grantAgreement/NSFC//91641117/
info:eu-repo/grantAgreement/China Postdoctoral Science Foundation//2019M661094/
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Thanks:
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 ...[+]
Type: Artículo

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