Resumen:
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[EN] It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort and drivability. One of the critical aspects in this regard is combustion noise control. ...[+]
[EN] It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOx) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes and in-cylinder swirl. An objective merit function based on the emissions, ISFC and combustion noise, was constructed to quantify the strength of the engine designs, and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained, showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOx emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. This computational study is a first of its kind (to the best of the authors¿ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.
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Agradecimientos:
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The equipment used in this work was partially supported by FEDER and the Spanish Government through grant no. DPI2015-70464-R and by Fondo Europeo de Desarrollo Regional (FEDER) project funds "Dotacion de infraestructuras ...[+]
The equipment used in this work was partially supported by FEDER and the Spanish Government through grant no. DPI2015-70464-R and by Fondo Europeo de Desarrollo Regional (FEDER) project funds "Dotacion de infraestructuras cientifico tecnicas para el Centro Integral de Mejora Energetica y Medioambiental de Sistemas de Transporte (CiMeT), (FEDER-ICTS-2012-06)," framed in the operational program of unique scientific and technical infrastructure of the Spanish Ministerio de Economia y Competitividad. J. Gomez-Soriano was partially supported through contract FPI-S2-2016-1353 of the "Programa de Apoyo para la Investigacion y Desarrollo (PAID-01-16)" of Universitat Politecnica de Valencia.
The submitted manuscript was created partly by UChicago Argonne, LLC, Operator of Argonne National Laboratory. Argonne, a US Department of Energy (DOE) Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. This research was partly funded by the US DOE Office of Vehicle Technologies, Office of Energy Efficiency and Renewable Energy under Contract No. DE-AC02-06CH11357. The authors wish to thank Gurpreet Singh and Leo Breton, program managers at DOE, for their support.
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