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Towards design optimization of high-pressure gasoline injectors using Genetic Algorithm coupled with Computational Fluid Dynamics (CFD)

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Towards design optimization of high-pressure gasoline injectors using Genetic Algorithm coupled with Computational Fluid Dynamics (CFD)

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dc.contributor.author Hellmann, Robin es_ES
dc.contributor.author Jochmann, Paul es_ES
dc.contributor.author Stapf, Karl Georg es_ES
dc.contributor.author Schuenemann, Erik es_ES
dc.contributor.author Daróczy, László es_ES
dc.contributor.author Thévenin, Dominique es_ES
dc.date.accessioned 2018-01-31T10:58:34Z
dc.date.available 2018-01-31T10:58:34Z
dc.date.issued 2017-07-28
dc.identifier.isbn 9788490485804
dc.identifier.uri http://hdl.handle.net/10251/96263
dc.description.abstract [EN] The spray pattern of high-pressure multi-hole injectors as well as the atomization process are of uttermost importance regarding efficiency and emissions in gasoline combustion engines. Ensuring optimal homogenization while meeting the engine individual specifications regarding spray targeting and massflow is a crucial development goal. High effort is put on the layout of the nozzle seat to meet the engine requirements. Success is only possible with a deep knowledge of the influencing quantities, considering that many design parameters affect the inner nozzle flow. Based on this knowledge improvement in spray penetration length and atomization can be achieved. In the current investigation a segment model of the injector is considered. A fully automated, highly parallelized workflow enables a systematic examination of the constrained design space with acceptable computational time. The CFD workflow is implemented in the OPtimization Algorithm Library++ (OPAL++) developed at the “Otto von Guericke” University of Magdeburg. First, inner nozzle flow 3D-CFD calculations of two selected nozzle geometries are validated by comparison with shadowgraphy and Long-Distance-Microscope (LDM) measurements. Using these simulations, correlations between nozzle flow parameters and the key spray characteristics, serving as optimization objectives, are analyzed. Second, a Design-of-Experiment (DoE) is created to understand the interdependency between design variables and objectives. Based on the DoE, metamodels are constructed, validated, compared with each other and used for optimization. Afterwards, a direct 3D CFD-optimization is carried out for the nozzle geometry. It relies on a Genetic Algorithm in OPAL++ to identify the Pareto front of the multi-objective problem. Finally, the Pareto front is analyzed and conclusions are drawn for future research. es_ES
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Nozzle flow es_ES
dc.subject High Pressure Injector es_ES
dc.subject CFD es_ES
dc.subject Metamodeling es_ES
dc.subject Genetic Algorithm es_ES
dc.subject Optimization es_ES
dc.title Towards design optimization of high-pressure gasoline injectors using Genetic Algorithm coupled with Computational Fluid Dynamics (CFD) es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/ILASS2017.2017.4586
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Hellmann, R.; Jochmann, P.; Stapf, KG.; Schuenemann, E.; Daróczy, L.; Thévenin, D. (2017). Towards design optimization of high-pressure gasoline injectors using Genetic Algorithm coupled with Computational Fluid Dynamics (CFD). En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 880-887. https://doi.org/10.4995/ILASS2017.2017.4586 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename ILASS2017 - 28th European Conference on Liquid Atomization and Spray Systems es_ES
dc.relation.conferencedate September 06-08, 2017 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/ILASS/ILASS2017/paper/view/4586 es_ES
dc.description.upvformatpinicio 880 es_ES
dc.description.upvformatpfin 887 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\4586 es_ES


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