用机器学习优化甲烷燃烧的火焰面模型，大幅降低内存占用。
Optimizing Flamelet Generated Manifold Models: A Machine Learning Performance Study
- 用四种机器学习算法重建火焰面数据集，提升计算效率。
- 最佳模型达99.81%精度，误差率仅2.30%。
- 适合燃烧模拟与高性能计算研究者参考。
在化学表征和火焰面燃烧模型中，火焰面生成流形（FGM）以高精度和物理可解释性著称，但其实际应用需大量内存资源。本文针对甲烷燃料，利用机器学习技术构建层流火焰面（Laminar FGM）数据库。研究采用四种算法：多层感知机（MLP）、随机森林（Random Forest）、线性回归（Linear Regression）和支撑向量机（SVM），基于七组合适的数据库进行训练，整体误差率为2.30%。通过对比各方法默认架构，选定MLP为最优方案，并经超参数调优进一步优化。最终模型含四层隐藏层，神经元数分别为10、15、20、25，实现99.81%的预测精度。
原文摘要 · Abstract (English)
In chemistry tabulations and Flamelet combustion models, the Flamelet Generated Manifold (FGM) is recognized for its precision and physical representation. The practical implementation of FGM requires a significant allocation of memory resources. FGM libraries are developed specifically for a specific fuel and subsequently utilized for all numerical problems using machine learning techniques. This research aims to develop libraries of Laminar FGM utilizing machine learning algorithms for application in combustion simulations of methane fuel. This study employs four Machine Learning algorithms to regenerate Flamelet libraries, based on an understanding of data sources, techniques, and data-driven concepts. 1. Multi-Layer Perceptron; 2. Random Forest; 3. Linear Regression; 4. Support Vector Machine. Seven libraries were identified as appropriate for constructing a database for training machine learning models, giving an error rate of 2.30%. The default architectures of each method were evaluated to determine the optimal approach, leading to the selection of the MLP method as the primary choice. The method was enhanced through hyperparameter tuning to improve accuracy. The quantity of hidden layers and neurons significantly influences method performance. The optimal model, comprising four hidden layers with 10, 15, 20, and 25 neurons respectively, achieved an accuracy of 99.81%.
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