Application of artificial neural networks to supercritical flamelet model

被引：0

作者：

Gao Z.-W. ^{[1
]}

Jin T. ^{[2
]}

Song C.-C. ^{[1
]}

Luo K. ^{[1
]}

Fan J.-R. ^{[1
]}

机构：

[1] State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou

[2] School of Aeronautics and Astronautics, Zhejiang University, Hangzhou

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2021年 / 55卷 / 10期

关键词：

Artificial neural network (ANN); Combustion simulation; Computational performance; Flamelet library construction method; Flamelet model;

D O I：

10.3785/j.issn.1008-973X.2021.10.019

中图分类号：

学科分类号：

摘要：

Artificial neural networks (ANN) were utilized to build the library for the flamelet/progress variable (FPV) model and develop the FPV-ANN approach aiming at the problem that the enlarged lookup tables of the flamelet-based combustion model make the computer memory insufficient and slow down the interpolation process. Both the priori analysis and the large-eddy simulation of supercritical hydrothermal flames show that the distributions of temperature, species and other target variables obtained by FPV-ANN and classical FPV method achieve overall good agreement, verifying the accuracy of the FPV-ANN approach. Since the size of the ANN library is only 1% of the classical library, the use of FPV-ANN approach can produce a significant reduction in computer memory consumption during the large-scale parallel simulation. The computational speed of FPV-ANN approach is 30% faster than the classical FPV approach, which confirms that FPV-ANN approach has better computational performance. © 2021, Zhejiang University Press. All right reserved.

引用

页码：1968 / 1977

页数：9

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