机器学习+小火焰库：一种用于提高层流火焰模拟精度的机器学习方法-

题目：一种用于提高-基于简化化学反应机理的-层流火焰模拟精度的机器学习方法
摘要：本文提出了一种可以快速生成层流火焰速度数据库的新方法，该方法将用于替代内燃机（Internal combustion engine, ICE）模拟中的经验公式。目前内燃机的燃烧模式主要是褶皱火焰模式，因此小火焰模型（flamelet model）是适用的，该模型需要对层流火焰速度和厚度进行先验估计。从实验数据得出的经验公式具有计算速度快的优点，但是其在远离实验工况条件下误差较大，而在ICE中经常会出现这种情况。另一方面，为了生成足够精细的用于插值的参考反应数据库，需要对自由传播的绝热火焰进行详细的化学反应模拟，这一过程可能需要数百小时的计算时间。使用简化的化学反应机理有可能将所需的时间减少几个数量级，但它将降低数据库准确性。本文利用优化的软件库，以减少计算时间的同时保持较高准确率为目标，评估了将机器学习算法和神经网络通过不同的方法集成到工作流中的效果。
要点
1.利用 Cantera 模拟一维绝热火焰建表
2.神经网络的输入：混合物当量比、当地废气质量分数、压力、未燃混合物温度；输出：层流火焰速度和厚度
3.采用所提出的方法生成层流火焰速度（和层流火焰厚度）数据库的精度与采用详细化学反应机理的精度（平均相对误差低于1％）相近，并且所需时间（与使用POLIMI方法相比）减少了80％。

Normalized profile of the mass fractions of C8H18, H, CO2 and O2 and normalized temperature of a freely propagating adiabatic flame at P = 40 bar, T = 600 K, φ=1 and EGR=0%.

Title: A machine learning methodology for improving the accuracy of laminar flame simulations with reduced chemical kinetics mechanisms
Abstract: The focus of the present work is to investigate a new methodology for the rapid generation of laminar flame speed lookup tables, to be used replacing correlation laws in internal combustion engine simulations. Current production engines run mostly under the thickened wrinkled flame combustion regime, which allows the application of a flamelet modelling approach, which requires the a-priori evaluation of the laminar flame speed and thickness. The use of correlation laws, derived from experimental data, has the advantage to be extremely fast to compute, but displays a lack of precision in conditions far from the experimental reference, as it usually happens for ICE applications. On the other hand, the detailed chemical simulation of a freely propagating adiabatic flame, performed for a sufficiently refined grid of reference points, to be interpolated during runtime, might require hundreds of hours of computing. The use of a reduced chemical mechanism can potentially cut by orders of magnitude the required time, but on the other hand it will decrease accuracy. In the present work, the potential of integrating machine learning algorithms and neural networks in the workflow with different approaches was valuated, leveraging the potential of new and optimized software libraries, to reduce simulation times while maintaining a high level of accuracy, with respect to the results obtained with the complete scheme.
原文链接, CNF2020, IF 4.12