Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Hu, Nao and Zhou, Peilin and Yang, Jianguo (2017) Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation. Energy Conversion and Management, 133. pp. 138-152. ISSN 0196-8904 (https://doi.org/10.1016/j.enconman.2016.11.066)

[thumbnail of Hu-etal-ECM2016-Comparison-and-combination-of-NLPQL-and-MOGA-algorithms-for-a-marine]
Preview
 Text. Filename: Hu_etal_ECM2016_Comparison_and_combination_of_NLPQL_and_MOGA_algorithms_for_a_marine.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (4MB)| Preview

Abstract

Seven engine design parameters were investigated by use of NLPQL algorithm and MOGA separately and together. Detailed comparisons were made on NOx, soot, SFOC, and also on the design parameters. Results indicate that NLPQL algorithm failed to approach optimal designs while MOGA offered more and better feasible Pareto designs. Then, an optimal design obtained by MOGA which has the trade-off between NOx and soot was set as the starting point of NLPQL algorithm. In this situation, an even better design with lower NOx and soot was approached. Combustion processes of the optimal designs were also disclosed and compared in detail. Late injection and small swirl were reckoned to be the main reasons for reducing NOx. In the end, RSM contour maps were applied in order to gain a better understanding of the sensitivity of import parameters on NOx, soot and SFOC.

ORCID iDs

Hu, Nao ORCID logoORCID: https://orcid.org/0000-0003-4094-3858, Zhou, Peilin ORCID logoORCID: https://orcid.org/0000-0003-4808-8489 and Yang, Jianguo;
CORE (COnnecting REpositories)