Parametric study of combustion characteristics of diesel/methanol dual fuel engine using global sensitivity analysis and multi-objective optimization

Yinjie Ma, Yuanhao Zhao, Dong Yang, Jiaqiang E, Jialuo Zhao and Mingzhang Pan

Renewable Energy, 2024, vol. 237, issue PB

Abstract: The pursuit of clean and efficient internal combustion engine power necessitates the substitution of fossil fuels with methanol and the adoption of advanced combustion strategies. This study examines the combustion and emission characteristics of methanol/diesel dual-fuel compression ignition engines. To understand the intricate interplay between critical parameters and engine performance, the global sensitivity analysis (GSA) method is employed, utilizing Sobol sensitivity as the quantitative metric. The investigated parameters include methanol substitution rate, diesel injection parameters, and five thermodynamics parameters concerning in-cylinder conditions. A validated computational fluid dynamics (CFD) model, coupled with a chemical kinetic mechanism, is developed to predict the combustion behavior of the dual-fuel engine accurately. This predictive capability forms the basis for utilizing the tree-based process optimization (TPOT) algorithm. By implementing TPOT, a significant reduction in the mean square error (MSE) of the predicted characteristics model is achieved, averaging a decrease of 73.59 %. Subsequently, a multi-objective optimization is conducted, exploring the impact of varying target weights on the optimal outcomes. Results show the initial pressure significantly impacts the engine's economy, power and emission characteristics, and NOx formation should be carefully considered in the combustion optimization of dual-fuel engines.

Keywords: Methanol/diesel dual fuel; Machine learning; Global sensitivity analysis; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:237:y:2024:i:pb:s0960148124017580

DOI: 10.1016/j.renene.2024.121690

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