Directed Relation Graph-Based Species Rank (DRGSR): An efficient mechanism reduction algorithm

IF 5.8 2区工程技术 Q2 ENERGY & FUELS

Combustion and Flame Pub Date : 2025-05-15 DOI:10.1016/j.combustflame.2025.114226

Yiru Wang , You Wu , Shengqiang Lin , Chung K. Law , Bin Yang

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引用次数: 0

Abstract

While kinetic mechanisms play a pivotal role in simulating complex combustion problems, their extended scale often results in prohibitive computational cost, particularly when integrated with computational fluid dynamics simulations. This paper introduces the Directed Relation Graph Species Rank (DRGSR) algorithm, an efficient mechanism reduction technique designed to retain the essential species and reaction pathways while minimizing computational demands. Specifically, it incorporates a two-step approach: the first utilizes a directed relation graph to map species interactions and transform the kinetic information into a graph structure, and the second employs the PageRank algorithm and directed interaction coefficients to rank species based on their importance within the network. The DRGSR algorithm is validated through case studies involving both large- and small-scale, high-temperature and low-temperature mechanisms, specifically focusing on the ignition delay times for ethylene (C₂H₄) and n-heptane (C₇H₁₆). The algorithm demonstrates superior performance in reducing the number of species significantly while maintaining accuracy; for ethylene, it retains only 31 species with an error under 8 %, while for n-heptane, it achieves comparable precision with fewer species compared to existing methods. The validation is extended to predicting the laminar flame speeds, and further affirms the algorithm's reliability and generalizability. A comparative analysis of the computational cost reveals that the DRGSR algorithm not only is less time-consuming, but it also simplifies the reduction process by eliminating the iterative threshold adjustments required by methods such as Directed Relation Graph (DRG), Directed Relation Graph with Error Propagation (DRGEP) and Directed Relation Graph with Error Propagation and Sensitivity Analysis (DRGEPSA). These findings indicate that the DRGSR algorithm offers a robust, efficient and reliable approach for kinetic mechanism reduction, suitable for wide ranges of engineering applications.

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基于有向关系图的物种排序（DRGSR）：一种高效的机制约简算法

虽然动力学机制在模拟复杂的燃烧问题中发挥着关键作用，但其规模的扩大往往会导致高昂的计算成本，特别是在与计算流体动力学模拟相结合时。本文介绍了一种有效的机制约简技术——有向关系图物种排序算法（DRGSR），该算法旨在保留基本的物种和反应路径，同时最小化计算需求。具体来说，它采用了两步方法：第一步利用有向关系图绘制物种相互作用并将动力学信息转换为图结构；第二步采用PageRank算法和有向相互作用系数根据物种在网络中的重要性对其进行排序。DRGSR算法通过大型和小型、高温和低温机制的案例研究进行了验证，特别是关注乙烯（C2H4）和正庚烷（C7H16）的点火延迟时间。该算法在保持准确率的同时，显著减少了物种数量；对于乙烯，它只保留了31个物种，误差低于8%，而对于正庚烷，与现有方法相比，它只保留了更少的物种，达到了相当的精度。将验证扩展到层流火焰速度的预测，进一步验证了算法的可靠性和通用性。计算成本的对比分析表明，DRGSR算法不仅耗时更少，而且通过消除有向关系图（DRG）、有向关系图误差传播（DRGEP）和有向关系图误差传播和灵敏度分析（DRGEPSA）等方法所需的迭代阈值调整，简化了约简过程。这些结果表明，DRGSR算法提供了一种鲁棒、高效、可靠的动力学机构还原方法，适用于广泛的工程应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.