Prediction of ignition delay times of Jet A-1/hydrogen fuel mixture using machine learning

Huang, Yunzhe; Wan, Kaidi; Gao, Zhenxun; Domingo, Pascale; He, Yong; Wang, Zhihua; Lee, Chun‐Hian; Cai, Qingsheng; Liu, Jieping

doi:10.1016/j.ast.2022.107675

Cited by 19 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the BP-MRPSO model combines the particle swarm optimization algorithm and MapReduce parallel processing technology, effectively improving prediction accuracy and processing efficiency. By constructing an ANN neural network model, Huang et al [14], Bounaceur et al [15] predicted the ignition delay time of hydrocarbon mixtures. Data-driven surrogate models based on artificial neural networks (ANN) have limitations in capturing the full complexity of the aviation fuel ignition process, with maximum local relative errors of up to 10%, indicating poor predictive performance of data-driven surrogate models under certain conditions, especially when the IDT is short.…”

Section: Introductionmentioning

confidence: 99%

Study on Hydrocarbon Fuel Ignition Characterization Based on Optimized BP Neural Network

Chen,

Wei,

et al. 2024

Energies

View full text Add to dashboard Cite

The investigation of the ignition delay of hydrocarbon fuel is highly valuable for enhancing combustion efficiency, optimizing fuel thermal efficiency, and mitigating pollutant emissions. This paper has developed a BP-MRPSO neural network model for studying hydrocarbon fuel ignition and clarified the novelty of this model compared to the traditional BP and ANN models from the literature. The model integrates the particle swarm optimization (PSO) algorithm with MapReduce-based parallel processing technology. This integration improves the prediction accuracy and processing efficiency of the model. Compared to the traditional BP model, the BP-MRPSO model can increase the average correlation coefficient, from 0.9745 to 0.9896. The R2 value for predicting fire characteristics using this model can exceed 90%. Meanwhile, when the two hidden layers of both the BP and BP-MRPSO models consist of 9 and 8 neurons, respectively, the accuracy of the BP-MRPSO model is increased by 38.89% compared to the BP model. This proved that the new BP-MRPSO model has the capacity to handle large datasets while achieving great precision and efficiency. The findings could provide a new perspective for examining the properties of fuel ignition, which is expected to contribute to the development and assessment of aviation fuel ignition characteristics in the future.

show abstract

Section: Introductionmentioning

confidence: 99%