摘要
为了更好地在小火焰燃烧模型框架内实施燃烧热化学流形表征,采用多任务学习领域中的多门控混合专家网络(MMoE).通过对三维层流喷雾射流火焰构型进行详细化学(DC)模拟,构建原始数据集.原始数据集经过Box-Cox转换和标准化处理,以应对燃烧数据的多尺度分布问题.对数据集进行Pearson相关系数分析,结果表明部分化学组分之间无明显的相关性.分别构建同等参数量规模的MMoE和前馈神经网络(FNN)模型,对比分析结果表明,2种模型取得的损失值和决定系数相近,但相比FNN模型,MMoE模型在训练过程中更加稳定,且取得的定量预测结果更加准确.
The multi-gate mixture of experts(MMoE)for multi-task learning was applied to better implement the representation of combustion thermochemical manifolds in the framework of flamelet-based combustion models.Detailed chemistry(DC)simulation of a three-dimensional laminar spray jet flame was conducted to generate the original dataset.The original dataset was preprocessed by Box-Cox transformation and Z-score normalization to deal with the multi-scale distributions of combustion data.Correlation analysis was performed on the dataset using the Pearson correlation coefficient,and the results showed that there was no significant correlation between some chemical species.The MMoE and feedforward neural network(FNN)models were constructed respectively,and the comparison and analysis showed that although both models achieved similar loss values and coefficients of determination,the MMoE model was more stable during the training process and achieved more accurate quantitative prediction results than FNN model.
作者
王意存
邵长孝
金台
邢江宽
罗坤
樊建人
WANG Yi-cun;SHAO Chang-xiao;JIN Tai;XING Jiang-kuan;LUO Kun;FAN Jian-ren(State Key Laboratory of Clean Energy Utilization,Zhejiang University,Hangzhou 310027,China;Center for Turbulence Control,Harbin Institute of Technology,Shenzhen Campus,Shenzhen 518055,China;School of Aeronautics and Astronautics,Zhejiang University,Hangzhou 310027,China;Shanghai Institute for Advanced Study of Zhejiang University,Shanghai 200120,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第12期2401-2411,共11页
Journal of Zhejiang University：Engineering Science
基金
国家杰出青年科学基金资助项目(51925603)
国家自然科学基金资助项目(52236002)。
