火焰面方法进展及在燃机燃烧室模拟中的挑战

张归华, 吴玉新, 吴家豪, 张扬, 张海

清华大学学报（自然科学版） ›› 2023, Vol. 63 ›› Issue (4) : 505-520.

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清华大学学报（自然科学版） ›› 2023, Vol. 63 ›› Issue (4) : 505-520. DOI: 10.16511/j.cnki.qhdxxb.2023.25.026
综述

火焰面方法进展及在燃机燃烧室模拟中的挑战

  • 张归华, 吴玉新, 吴家豪, 张扬, 张海
作者信息 +

State of the art and challenges of flamelet method in gas turbine combustor simulation

  • ZHANG Guihua, WU Yuxin, WU Jiahao, ZHANG Yang, ZHANG Hai
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摘要

现代燃气轮机燃烧室参数持续提高,燃料种类不断丰富,燃烧技术深入开发,这对燃机燃烧过程数值模拟方法的发展提出了更高的要求。火焰面方法兼具准确性和计算效率高的特点,是燃气轮机燃烧室成熟数值模拟方法的主要选择之一。该文针对燃气轮机燃烧室未来多工况、高参数、低污染的发展趋势,从火焰面方法在自适应湍流燃烧模型中的应用、进度变量的优化选择、湍流燃烧耦合模型以及火焰面方法在污染物预测中的应用等4个方面,回顾了火焰面方法的相关模型及适用范围,分析了该方法在燃气轮机燃烧室中的应用及面临的挑战。在此基础上,对火焰面方法在未来燃气轮机燃烧室模拟中的发展方向提出了针对性建议。

Abstract

[Significance] Given the consistent increase in the number of evaluation parameters, enrichment of fuel types in gas turbine combustors, and in-depth investigation of the combustion technology, the numerical simulation of combustion processes in gas turbines has become crucial. In various turbulent combustion models, the flamelet method couples numerous chemical components with a small number of scalars by preconstructing a table, which can reduce the number of transport equations to be solved while considering the detailed chemical reactions. The flamelet method, owing to its accuracy and computational efficiency, provides a primary alternative to numerical simulation for gas turbine combustors. [Progress] The present study reviews the advancement of flamelet methods. Subsequently, in view of the future development trend of gas turbine combustors with multiple working conditions, numerous parameters, and low pollution, we reviewed the relevant models and application scope of flamelet methods and analyzed their application and challenges in gas turbine combustors considering the following four aspects: the application of the flamelet method in an adaptive turbulent combustion model, optimized selection of progress variables, coupling with the turbulent model, and its application in pollution analysis. The application of the flamelet method in an adaptive turbulent combustion model includes its coupling with other turbulent combustion models and the development of a multi-region flamelet method. Toward this end, exploring appropriate identification techniques of different combustion modes is crucial, and the machine learning method is a robust tool to address this challenge. The optimized selection of progress variables involves multi-phase flow combustion and multi-fuel and multi-jet problems. However, a universal progress variable that can act as a representative of all problems is lacking. The flamelet method requires different expressions of progress variables in different problems. The development of universal optimization methods form the primary research aim. The coupling with the turbulent model mainly includes the presumed and transport probability density function methods; however, the balance of accuracy and computation cost remains to be elucidated. The application of the flamelet method in pollutant analysis requires solving additional transport equations of pollutants and modifying the expressions of source terms. [Conclusions and Prospects] Based on the review of previous literature, we recommend developing specific validation methods for each submodel in the flamelet method, performing further studies on the coupling effect of different submodels, and obtaining more data on real gas turbine combustion chambers to guide the development of a flamelet method suitable for real gas turbine combustion chambers.

关键词

燃料与燃烧 / 火焰面方法 / 燃气轮机 / 进度变量 / 多尺度多模式

Key words

fuel and combustion / flamelet method / gas turbine / progress variable / multi-scale multi-regime

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张归华, 吴玉新, 吴家豪, 张扬, 张海. 火焰面方法进展及在燃机燃烧室模拟中的挑战[J]. 清华大学学报（自然科学版）. 2023, 63(4): 505-520 https://doi.org/10.16511/j.cnki.qhdxxb.2023.25.026
ZHANG Guihua, WU Yuxin, WU Jiahao, ZHANG Yang, ZHANG Hai. State of the art and challenges of flamelet method in gas turbine combustor simulation[J]. Journal of Tsinghua University(Science and Technology). 2023, 63(4): 505-520 https://doi.org/10.16511/j.cnki.qhdxxb.2023.25.026

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国家科技重大专项资助项目(2019-I-0022-0021)

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