Using multi-objective NLPQL optimization of diesel engine

Junming Zhang

Abstract


Optimization of engines is a delicate process due to its nonlinear nature and combustion that has to be care with finesse and extra care. In this regard, the study is programmed to address the need to fulfill the requirement for having the ideals of maximized power and spray quality and better-atomized spray. To accomplish this matter, a DoE setup and statistical approach is undertaken that the design variables include nozzle geometry and injection tilting angle in combustion chamber. The sub-objectives are considered to be indicate power (IP) and Sauter mean diameter (SMD) with equal weight in optimization process. The optimum case point is determined Case_3 with -3.813 and the injection angle is decided 162.2°, nozzle diameter of 0.156 mm. NLPQL-optimized solution has led to 2.34% increase in indicated power and 3.43% decrease of droplet size.


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References


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