多环芳烃机制的热化学机器学习制表及其在层流和湍流烟尘火焰中的应用
IF 5.8
2区 工程技术
Q2 ENERGY & FUELS
引用次数: 0
摘要
本工作提出了一种新的机器学习制表方法，以加速燃烧中大多环芳烃（PAH）烟灰形成机制的计算。该方法将有向关系图误差传播（DRGEP）分析与先前的混合火焰/随机数据和多层感知器（HFRD-MMLP）方法（Ding et al., 2021）相结合，选择目标物种并确定每个目标的连接物种列表。通过这种方法，大大减少了人工神经网络（ann）的数量和大小。HFRD方法利用随机过程来扩展层流小火焰的数据，并具有完整的BPP机制，以涵盖实际湍流烟尘燃烧中的反应成分。MMLP方法针对不同量级的成分状态训练单独的人工神经网络，旨在提高预测精度。在不同应变率的一维层流火焰模拟中对人工神经网络进行了测试，与使用简化的多环芳烃机制直接集成相比，显示出更高的预测精度。通过层流共流（Santoro）和湍流提升（DLR）烟尘火焰的模拟进一步验证，除了在火焰下游区域观察到一些较小的物种和多环芳烃的误差外，在温度、烟尘体积分数和种类摩尔分数方面与直接积分方法基本一致。提出的PAH机制HFRD-MMLP-DRGEP方法与传统的积分算法相比，速度有显著提高。在层流共流情况下，与DVODE算法相比，该方法的反应步长和总计算时间分别提高了59倍和22倍。在LES-PDF-PBE湍流火焰模拟中，与欧拉后向算法相比，加速因子分别为4.0和2.3。如果与DVODE相比，加速比应该是50和27。这些加速因子远高于GRI-1.2 （Ding et al., 2022）和DME （Liu et al., 2024）等小型机构的加速因子，表明HRFD-MMLP-DRGEP方法在降低大型机构的计算成本方面具有很高的效率。新颖性和意义本研究提出了一种新的方法来加速实时计算烟尘形成模型中完整的多环芳烃（PAH）机制（Blanquart et al., 2009）。这种新方法将DRGEP分析与HFRD-MMLP方法（Ding et al., 2021, 2022）相结合，以减少训练的人工神经网络（ann）的数量和规模。这是人工神经网络方法在层流和湍流烟尘火焰模拟中首次应用于加速完整的多环芳烃机制本文章由计算机程序翻译，如有差异，请以英文原文为准。
Machine learning tabulation of thermochemistry for the PAH mechanism and applications to laminar and turbulent sooting flames
This work proposes a novel machine learning tabulation methodology to accelerate the computation of a large polycyclic aromatic hydrocarbon (PAH) mechanism for soot formation in combustion. This methodology combines the directed relation graph error propagation (DRGEP) analysis with previous hybrid flamelet/random data and multiple multilayer perceptrons (HFRD-MMLP) method (Ding et al., 2021), to select target species and determine connected species list for each target. By this means, the number and size of artificial neural networks (ANNs) are substantially reduced. The HFRD method utilises the random process to expand data from laminar flamelets with the full BPP mechanism to cover reactive compositions in practical turbulent sooting combustion. The MMLP approach trains separate ANNs for composition states of different magnitudes, aiming to improve the prediction accuracy. The ANNs are tested on 1-D laminar flame simulations with varying strain rates, demonstrating higher prediction accuracy compared to using the direct integration with the simplified PAH mechanism. Validation is further conducted on simulations of laminar co-flow (Santoro) and turbulent lifted (DLR) sooting flames, showing overall agreement with direct integration methods in terms of temperature, soot volume fraction and species mole fractions, except errors are observed at flame downstream regions for some minor species and PAHs. The proposed HFRD-MMLP-DRGEP method for the PAH mechanism achieves significant speed improvement compared to traditional integration algorithms. In the laminar co-flow case, the method achieves speed-up factors of 59 and 22 for the reaction step and total computation time, respectively, when compared to the DVODE algorithm. In the LES-PDF-PBE simulation of the turbulent flame, speed-up factors are 4.0 and 2.3, compared to the Euler backward algorithm. If compared to the DVODE, the speed-up ratios should be 50 and 27. These speed-up factors are much higher than those achieved with small-size mechanisms, like GRI-1.2 (Ding et al., 2022) and DME (Liu et al., 2024) mechanisms, indicating the high efficacy of the HRFD-MMLP-DRGEP method in reducing computational costs for large mechanisms.
Novelty and significance
This study presents a novel methodology for accelerating the real-time computation of a complete polycyclic aromatic hydrocarbon (PAH) mechanism (Blanquart et al., 2009) in soot formation modelling. This new methodology combines the DRGEP analysis with the HFRD-MMLP approach (Ding et al., 2021, 2022) to reduce the number and size of trained artificial neural networks (ANNs). This is the first application of the ANN method to accelerate a complete PAH mechanism in laminar and turbulent sooting flame simulations
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来源期刊
Combustion and Flame
工程技术-工程：化工
CiteScore
9.50
自引率
20.50%
发文量
631
审稿时长
3.8 months
期刊介绍：
The mission of the journal is to publish high quality work from experimental, theoretical, and computational investigations on the fundamentals of combustion phenomena and closely allied matters. While submissions in all pertinent areas are welcomed, past and recent focus of the journal has been on:
Development and validation of reaction kinetics, reduction of reaction mechanisms and modeling of combustion systems, including:
Conventional, alternative and surrogate fuels;
Pollutants;
Particulate and aerosol formation and abatement;
Heterogeneous processes.
Experimental, theoretical, and computational studies of laminar and turbulent combustion phenomena, including:
Premixed and non-premixed flames;
Ignition and extinction phenomena;
Flame propagation;
Flame structure;
Instabilities and swirl;
Flame spread;
Multi-phase reactants.
Advances in diagnostic and computational methods in combustion, including:
Measurement and simulation of scalar and vector properties;
Novel techniques;
State-of-the art applications.
Fundamental investigations of combustion technologies and systems, including:
Internal combustion engines;
Gas turbines;
Small- and large-scale stationary combustion and power generation;
Catalytic combustion;
Combustion synthesis;
Combustion under extreme conditions;
New concepts.
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