Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model
详细信息 查看全文
- 作者：Ali Navid ; Shahram Khalilarya ; Hadi Taghavifar ; h.taghavifar@urmia.ac.ir" class="auth_mail" title="E-mail the corresponding author ; haditaghavifar@yahoo.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：CFD ; computational fluid dynamics ; DDM ; discrete droplet model ; DI ; direct injection ; Di ; bowl middle diameter ; DoE ; design of experiment ; GA ; genetic algorithm ; GDI ; gasoline direct injection ; HPCR ; high pressure common rail ; HRR ; heat release rate ; ICE ; internal combustion engine ; IMEP ; indicated mean effective pressure ; ISFC ; indicated specific fuel consumption ; MOGA ; multi-objective genetic algorithm ; NLPQL ; nonlinear programming by quadratic lagrangian ; PDF ; probability density function ; PS
- 刊名：Energy Conversion and Management
- 出版年：2016
- 出版时间：15 October 2016
- 年：2016
- 卷：126
- 期：Complete
- 页码：385-399
- 全文大小：4982 K
文摘
- •
NLPQL algorithm with Latin hypercube and multi-objective GA were applied on engine.
- •
NLPQL converge to the best solution at RunID41, MOGA introduces at RunID84.
- •
Deeper, more encircled design gives the lowest NOx, greater radius and deeper bowl the highest IMEP.
- •
The maximum IMEP and minimum ISFC obtained with NLPQL, the lowest NOx with MOGA.