FlamePINN-1D: 基于物理信息神经网络求解 1 维层流火焰的正向与逆向问题
摘要
鉴于燃烧研究与应用中存在各种正向与逆向问题且需采用不同方法求解，亟需一种统一的求解框架。一种前景灰明的做法是将机器学习方法与燃烧系统的控制方程相结合，该方法Compared to purely data-driven methods exhibits superior generality and few-shot learning ability. In this work, the FlamePINN-1D framework is proposed to solve the forward and inverse problems of 1D laminar flames based on physics-informed neural networks. Three cases with increasing complexity have been tested: Case 1 are freely-propagating premixed (FPP) flames with simplified physical models, while Case 2 and Case 3 are FPP and counterflow premixed (CFP) flames with detailed models, respectively. For forward problems, FlamePINN-1D aims to solve the flame fields and infer the unknown eigenvalues (such as laminar flame speeds) under the constraints of governing equations and boundary conditions. For inverse problems, FlamePINN-1D aims to reconstruct the continuous fields and infer the unknown parameters (such as transport and chemical kinetics parameters) from noisy sparse observations of the flame. Our results strongly validate these capabilities of FlamePINN-1D across various flames and working conditions. Compared to traditional methods, FlamePINN-1D is differentiable and mesh-free, exhibits no discretization errors, and is easier to implement for inverse problems. The inverse problem results also indicate the possibility of optimizing chemical mechanisms from measurements of laboratory 1D flames. Furthermore, some proposed strategies, such as hard constraints and thin-layer normalization, are proven to be essential for the robust learning of FlamePINN-1D. The code for this paper is partially available at https://github.com/CAME-THU/FlamePINN-1D.
引用
@article{arxiv.2406.09071,
title = {FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames},
author = {Jiahao Wu and Su Zhang and Yuxin Wu and Guihua Zhang and Xin Li and Hai Zhang},
journal= {arXiv preprint arXiv:2406.09071},
year = {2024}
}