A Deep Learning Model for Predicting the Laminar Burning Velocity of NH[sub.3]/H[sub.2]/Air

Both NH[sub.3] and H[sub.2] are considered to be carbon-free fuels, and their mixed combustion has excellent performance. Considering the laminar burning velocity as a key characteristic of fuels, accurately predicting the laminar burning velocity of NH[sub.3]/H[sub.2]/Air is crucial for its combust...

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Bibliographic Details
Published in:Applied sciences 2024-10, Vol.14 (20)
Main Authors: Yue, Wanying, Zhang, Bin, Zhang, Siqi, Wang, Boqiao, Xia, Yuanchen, Liang, Zhuohui
Format: Article
Language:English
Subjects:
Ammonia
Combustion
Online Access:Get full text
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Summary:Both NH[sub.3] and H[sub.2] are considered to be carbon-free fuels, and their mixed combustion has excellent performance. Considering the laminar burning velocity as a key characteristic of fuels, accurately predicting the laminar burning velocity of NH[sub.3]/H[sub.2]/Air is crucial for its combustion applications. The study made improvements to the XGBoost model and developed NH[sub.3]/H[sub.2]/Air Laminar Burning Velocity Net (NHLBVNet), which adopts a composite hierarchical structure to connect the functions of feature extraction, feature combination, and model prediction. The dataset consists of 487 sets of experimental data after the exclusion of outliers. The correlation coefficient (R[sup.2] > 0.99) of NHLBVNet is higher than that of the XGBoost model (R[sup.2] > 0.93). Robustness experiment results indicate that this model can obtain more accurate prediction results than other models even under small sample datasets.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14209603