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低频和高频交流电场助燃对比及燃烧特征参数预测研究

  • Hao Duan
  • , Xiaojun Yin
  • , Hailiang Kou
  • , Meng Zhang
  • , Ke Zeng
  • Xi'an Jiaotong University

科研成果: 期刊稿件文章同行评审

摘要

This paper explores the internal combustion promotion mechanism of AC electric field with different frequencies. A comparison is made on the effect of low-frequency(40, 60, 80, 100 Hz)and high-frequency(15, 20, 25, 30 kHz)AC electric fields on methane/air lean combustion(excess air ratio of 1.2, 1.4, 1.6)flames with a constant volume combustion test platform. The machine learning method is applied to predict the combustion characteristic parameters of the mixture under various AC fields. The results show that, under low-frequency and high-frequency AC fields, the flame is stretched in the electric field direction; the effect in promoting flame propagation is of the same order of magnitude, but the flame front under low-frequency AC fields is more stable; the effect of high-frequency AC fields on combustion characteristic parameters(peak pressure and peak pressure rise rate)is more significant than under low-frequency ones; the prediction models built by the support vector machine method have excellent prediction performance and generalization ability, with the correlation coefficients of higher than 0.998, and the average absolute percentage error and Hill's coefficients of inequality of less than 1.093% and 0.007, respectively. The research further verified the combustion promotion mechanism of low-frequency and high-frequency AC fields and confirmed the feasibility of machine learning method in the prediction of fundamental combustion characteristic parameters, enriching the electric field combustion theory.

投稿的翻译标题Comparison of Combustion Assisted by Low- and High-Frequency AC Electric Fields and Prediction of Combustion Characteristic Parameters
源语言繁体中文
页(从-至)118-148
页数31
期刊Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
57
5
DOI
出版状态已出版 - 5月 2023

关键词

  • bi-ionic wind effect
  • electric field assisted combustion
  • electrical-chemical effect
  • machine learning
  • support vector machine

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