Inclusion of entropy generation minimization in multi-objective CFD optimization of diesel engines

M. Jafari, M.J. Parhizkar, E. Amani and H. Naderan

Energy, 2016, vol. 114, issue C, 526-541

Abstract: This paper describes the optimization of operating conditions of a direct injection diesel engine by the response surface method targeting the first and second law efficiencies and a merit function accounting for emission. To the best of our knowledge, this is the first time that entropy generation minimization (EGM) is considered as one of the objectives of 3D CFD optimization of internal combustion engines. Therefore, the effects of different design parameters, i.e. SOI (start of injection), DOI (duration of injection) and spray angle, on the entropy generation response surface are investigated in detail. Various methods of optimization such as MOGA (multi-objective Genetic algorithm), MOSA (multi-objective simulated annealing), MOPSO (multi-objective particle swarm optimization) and NSGA (non-sorted Genetic algorithm) are used and compared. It is observed that among the design parameters, SOI is the most important parameter influencing the first law efficiency and emission while spray angle is the least important one. Also, it is found that DOI has the strongest impact on the second law efficiency. The most fitting error relates to response surface of the second law efficiency since, compared to the other objectives, it shows a much more complex behavior and changes nonlinearly with the change in design parameters.

Keywords: CFD optimization; Diesel engine; Entropy generation minimization; Response surface (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421631129X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:114:y:2016:i:c:p:526-541

DOI: 10.1016/j.energy.2016.08.026

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().