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| Application of artificial neural networks to supercritical flamelet model |
Zheng-wei GAO1( ),Tai JIN2,Chang-cheng SONG1,Kun LUO1,Jian-ren FAN1,*() |
1. State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
2. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China |
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Abstract 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.
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Received: 29 October 2020
Published: 27 October 2021
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| Fund: 国家重点研发计划资助项目（2016YFB0600102） |
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Corresponding Authors:
Jian-ren FAN
E-mail: gaozw@zju.edu.cn;fanjr@zju.edu.cn
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基于人工神经网络的超临界小火焰模型研究
为了解决超临界小火焰燃烧模型数据库过于庞大，导致计算机内存不足和取值性能下降的问题，提出使用人工神经网络（ANN）进行建库的超临界小火焰/过程变量模型FPV-ANN. 在先验性分析及在超临界水热火焰的大涡模拟计算中发现，FPV-ANN方法在温度、组分和其他目标变量的分布与传统FPV方法得到的结果吻合，说明FPV-ANN方法的准确性与传统FPV方法一致. 由于人工神经网络小火焰库大小只有传统库的1%，FPV-ANN方法在大规模并行计算中消耗更少的计算机内存. FPV-ANN方法的计算速度比传统FPV方法提升了30%. 可以看出，提出的FPV-ANN方法具有更好的计算性能.
关键词：
小火焰模型,
燃烧模拟,
人工神经网络(ANN),
小火焰库建库方法,
计算性能
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