Parametric analysis and multi-objective optimization of the ammonia/diesel dual-fuel engine for efficient and cleaner combustion

Jing Li, Xiaorong Deng, Siyu Liu, Yicheng Yu, Lifeng Li, Rui Liu, Xinyi Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

This study aims to conduct a parametric analysis and optimize the combustion and emissions of the ammonia/diesel dual fuel (ADDF) engine. To achieve this, a reliable computational model was first constructed and validated against experimental data. Subsequently, three parameters, namely the ammonia energy fraction (fNH3), start of injection (SOI) timing, and injection pressure (Pinj), were varied over wide ranges to perform the parametric and interaction analysis. A novel application of the dynamic equivalence ratio-temperature map technique was performed to provide deeper insights into pollutant formation mechanisms. Pearson correlation coefficient analysis was conducted to reveal the linear correlation between input and output parameters of the engine. The optimization of the ADDF engine was then achieved by integrating a surrogate model with the NSGA-II algorithm. The results indicate that the fNH3 shows the greatest influence on CO2 emission, primarily due to the replacement of diesel with ammonia. Adjusting Pinj and SOI timing can achieve the combustion mode with higher premixed combustion fraction, which consequently reduces CO and N2O emissions but increases CO2 and NOx emissions. It also reveals that the effects of the three parameters on the combustion and emissions characteristics are, in descending order, SOI timing, fNH3, and Pinj. Finally, the optimized case is identified with a fNH3 of 0.64, a Pinj of 460 bar for diesel, and an SOI timing of −11.2 °CA ATDC, which improves the indicated thermal efficiency from 42.81 % to 44.64 % and reduces the greenhouse gases by 34.5 %.

Original languageEnglish
Article number126048
JournalApplied Thermal Engineering
Volume269
DOIs
StatePublished - 15 Jun 2025

Keywords

  • ADDF engine
  • BP neural network
  • NSGA-II
  • Optimization
  • Parametric analysis

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