面向碳中和与先进动力的燃烧反应动力学研究方法进展

杨斌; 刘仲铠; 林柯利; 廖万雄; 王乔

doi:10.16511/j.cnki.qhdxxb.2022.25.040

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清华大学学报（自然科学版） ›› 2022, Vol. 62 ›› Issue (4) : 663-677. DOI: 10.16511/j.cnki.qhdxxb.2022.25.040

综述

面向碳中和与先进动力的燃烧反应动力学研究方法进展

杨斌, 刘仲铠, 林柯利, 廖万雄, 王乔

作者信息 +

Towards carbon neutrality and advanced engines:Progress in combustion kinetics research methods

YANG Bin, LIU Zhongkai, LIN Keli, LIAO Wanxiong, WANG Qiao

Author information +

文章历史 +

摘要

氢、氨、电子燃料、生物燃料等零碳及低碳燃料的清洁燃烧是实现碳中和目标的客观选择;而合成航煤、稠环烃类、多元混合燃料的高效燃烧与先进空天动力的发展息息相关。这些新型燃料的燃烧反应动力学是深入理解其燃烧过程、发展新的燃烧组织模式及燃烧器的基础。当前,建立新型燃料的可预测性动力学模型依然存在诸多挑战：一方面迫切需要宽范围条件尤其是极端条件及多物理场条件下的准确的实验数据;另一方面需要高效的燃烧反应动力学模型分析和优化方法。该文综述了近年来本研究团队发展的燃烧反应动力学基础实验方法和模型分析及优化方法,包括更为详细的燃烧组分信息的获取、更低温度下燃料点火数据的测量、等离子辅助燃烧的组分诊断等实验方法,以及燃烧反动动力学模型的降维、灵敏性及不确定性分析、实验设计、模型优化和简化方法等。

Abstract

The combustion of zero-carbon and low-carbon fuels such as hydrogen, ammonia, electronic fuels, and biofuels is fundamental to achieving carbon neutrality. The efficient utilization of synthetic jet fuels, fused-ring hydrocarbons, and multiple mixed fuels is then important for developing advanced aerospace technologies. Combustion kinetics studies of these new fuels are essential for understanding the combustion process and for developing new combustion modes and burners. The development of predictive kinetics models for these new fuels presents many challenges. On one hand, accurate experimental data under a wide range of conditions, especially under extreme conditions and multi-physics conditions, are needed; on the other hand, effective tools for uncertainty qualification and model optimization are highly desired. This paper reviews the fundamental experimental methods and uncertainty quantification/reverse uncertainty quantification methods developed by the authors' group in recent years. These experimental methods include the acquisition of more detailed speciation information, measurements of fuel ignition data at lower temperatures and species diagnostics in plasma-assisted combustion systems. The analysis methods include model dimensionality reduction, global sensitivity analyses, uncertainty quantification, and model optimization of combustion kinetics models.

导出引用

杨斌, 刘仲铠, 林柯利, 廖万雄, 王乔. 面向碳中和与先进动力的燃烧反应动力学研究方法进展[J]. 清华大学学报（自然科学版）. 2022, 62(4): 663-677 https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.040

YANG Bin, LIU Zhongkai, LIN Keli, LIAO Wanxiong, WANG Qiao. Towards carbon neutrality and advanced engines:Progress in combustion kinetics research methods[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(4): 663-677 https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.040

参考文献

[1] 袁振宏, 罗文, 吕鹏梅, 等. 生物质能产业现状及发展前景 [J]. 化工进展, 2009, 28(10): 1687-1692. YUAN Z H, LUO W, LÜ P M, et al. Status and prospect of biomass energy industry [J]. Chemical Industry and Engineering Progress, 2009, 28(10): 1687-1692. (in Chinese)
[2] 陈为, 魏伟, 孙予罕. 二氧化碳光电催化转化利用研究进展 [J]. 中国科学: 化学, 2017, 47(11): 1251-1261. CHEN W, WEI W, SUN Y H. Recent progress in photoelectrocatalytic conversion of carbon dioxide [J]. Scientia Sinica Chimica, 2017, 47(11): 1251-1261. (in Chinese)
[3] KITTNER N, LILL F, KAMMEN D M. Energy storage deployment and innovation for the clean energy transition [J]. Nature Energy, 2017, 2(9): 17125.
[4] AZADI P, INDERWILDI O R, FARNOOD R, et al. Liquid fuels, hydrogen and chemicals from lignin: A critical review [J]. Renewable and Sustainable Energy Reviews, 2013, 21: 506-523.
[5] LIAO J C, MI L, PONTRELLI S, et al. Fuelling the future: Microbial engineering for the production of sustainable biofuels [J]. Nature Reviews Microbiology, 2016, 14(5): 288-304.
[6] SHERWIN E D. Electrofuel synthesis from variable renewable electricity: An optimization-based techno-economic analysis [J]. Environmental Science and Technology, 2021, 55(11): 7583-7594.
[7] VALERA-MEDINA A, XIAO H, OWEN-JONES M, et al. Ammonia for power [J]. Progress in Energy and Combustion Science, 2018, 69: 63-102.
[8] 伍一洲, 石家福, 丁菲, 等. 酶光耦合催化系统转化CO₂研究进展 [J]. 中国科学: 化学, 2017, 47(3): 315-329. WU Y Z, SHI J F, DING F, et al. Integrated enzyme-photocatalysis system for carbon dioxide conversion [J]. Scientia Sinica Chimica, 2017, 47(3): 315-329. (in Chinese)
[9] 周永浩, 张宗岭, 胡思彪, 等. NH₃/H₂预混旋流火焰稳定性及燃烧极限实验研究 [J]. 工程热物理学报, 2021, 42(1): 246-253. ZHOU Y H, ZHANG Z L, HU S B, et al. Experimental studies on flame stability and combustion limit of premixed NH₃/H₂ swirl combustion [J]. Journal of Engineering Thermophysics, 2021, 42(1): 246-253. (in Chinese)
[10] LU T F, LAW C K. Toward accommodating realistic fuel chemistry in large-scale computations [J]. Progress in Energy and Combustion Science, 2009, 35(2): 192-215.
[11] WANG H, XU R, WANG K, et al. A physics-based approach to modeling real-fuel combustion chemistry-I. Evidence from experiments, and thermodynamic, chemical kinetic and statistical considerations [J]. Combustion and Flame, 2018, 193: 502-519.
[12] TOMLIN A S. The role of sensitivity and uncertainty analysis in combustion modelling [J]. Proceedings of the Combustion Institute, 2013, 34(1): 159-176.
[13] WANG H, SHEEN D A. Combustion kinetic model uncertainty quantification, propagation and minimization [J]. Progress in Energy & Combustion Science, 2015, 47: 1-31.
[14] YANG B. Towards predictive combustion kinetic models: Progress in model analysis and informative experiments [J]. Proceedings of the Combustion Institute, 2021, 38(1): 199-222.
[15] DAGAUT P, REUILLON M, BOETTNER J C, et al. Kerosene combustion at pressures up to 40 atm: Experimental study and detailed chemical kinetic modeling [J]. Symposium (International) on Combustion, 1994, 25(1): 919-926.
[16] MATRAS D, VILLERMAUX J. Un réacteur continu parfaitement agité par jets gazeux pour l'étude cinétique de réactions chimiques rapides [J]. Chemical Engineering Science, 1973, 28(1): 129-137.
[17] LIU Z K, SUN W Y, HOU Q F, et al. Experimental and kinetic modeling investigation on 2, 5-hexanedione oxidation in a jet-stirred reactor [J]. Combustion and Flame, 2021, 234: 111648.
[18] FAN X F, LIU Z K, YANG J Z, et al. Pyrolysis of Lignocellulosic Biofuel Di-n-butyl Ether (DBE): Flow reactor experiments and kinetic modeling [J]. Energy and Fuels, 2021, 35(17): 14077-14086.
[19] SUN W Y, LIU Z K, ZHANG Y, et al. Comparing the pyrolysis kinetics of dimethoxymethane and 1, 2-dimethoxyethane: An experimental and kinetic modeling study [J]. Combustion and Flame, 2021, 226: 260-273.
[20] PRUCKER S, MEIER W, STRICKER W. A flat flame burner as calibration source for combustion research: Temperatures and species concentrations of premixed H₂/air flames [J]. Review of Scientific Instruments, 1994, 65(9): 2908-2911.
[21] LIAO H D, TAO T, SUN W Y, et al. Isomer-specific speciation behaviors probed from premixed flames fueled by acetone and propanal [J]. Proceedings of the Combustion Institute, 2021, 38(2): 2441-2448.
[22] LIAO H D, CHEN H D, LIU Z K, et al. MBMS study on plasma-assisted low-temperature oxidation of n-heptane and iso-octane in a flow reactor [J]. International Journal of Chemical Kinetics, 2021, 53(3): 428-439.
[23] STREIBEL T, ZIMMERMANN R. Resonance-enhanced multiphoton ionization mass spectrometry (REMPI-MS): Applications for process analysis [J]. Annual Review of Analytical Chemistry, 2014, 7(1): 361-381.
[24] JU Y G, SUN W T. Plasma assisted combustion: Dynamics and chemistry [J]. Progress in Energy and Combustion Science, 2015, 48: 21-83.
[25] ZHANG R Z, LIAO H D, YANG J Z, et al. Exploring chemical kinetics of plasma assisted oxidation of dimethyl ether (DME) [J]. Combustion and Flame, 2021, 225: 388-394.
[26] HE X, ZIGLER B T, WALTON S M, et al. A rapid compression facility study of OH time histories during iso-octane ignition [J]. Combustion and Flame, 2006, 145(3): 552-570.
[27] CLARKSON J, GRIFFITHS J F, MACNAMARA J P, et al. Temperature fields during the development of combustion in a rapid compression machine [J]. Combustion and Flame, 2001, 125(3): 1162-1175.
[28] UDDI M, DAS A K, SUNG C J. Temperature measurements in a rapid compression machine using mid-infrared H₂O absorption spectroscopy near 7.6 μm [J]. Applied Optics, 2012, 51(22): 5464-5476.
[29] KARWAT D M A, WAGNON S W, TEINI P D, et al. On the chemical kinetics of n-butanol: Ignition and speciation studies [J]. The Journal of Physical Chemistry A, 2011, 115(19): 4909-4921.
[30] JI W Q, ZHANG P, HE T J, et al. Intermediate species measurement during iso-butanol auto-ignition [J]. Combustion and Flame, 2015, 162(10): 3541-3553.
[31] TRANTER R S, GIRI B R, KIEFER J H. Shock tube/time-of-flight mass spectrometer for high temperature kinetic studies [J]. Review of Scientific Instruments, 2007, 78(3): 034101.
[32] DÜRRSTEIN S H, AGHSAEE M, JERIG L, et al. A shock tube with a high-repetition-rate time-of-flight mass spectrometer for investigations of complex reaction systems [J]. Review of Scientific Instruments, 2011, 82(8): 084103.
[33] SELA P, SHU B, AGHSAEE M, et al. A single-pulse shock tube coupled with high-repetition-rate time-of-flight mass spectrometry and gas chromatography for high-temperature gas-phase kinetics studies [J]. Review of Scientific Instruments, 2016, 87(10): 105103.
[34] LYNCH P T, TROY T P, AHMED M, et al. Probing combustion chemistry in a miniature shock tube with synchrotron VUV photo ionization mass spectrometry [J]. Analytical Chemistry, 2015, 87(4): 2345-2352.
[35] KANG S Q, LIAO W X, CHU Z H, et al. A rapid compression machine coupled with time-resolved molecular beam mass spectrometry for gas-phase kinetics studies [J]. Review of Scientific Instruments, 2021, 92(8): 084103.
[36] SUN W T, GAO X, WU B, et al. The effect of ozone addition on combustion: Kinetics and dynamics [J]. Progress in Energy and Combustion Science, 2019, 73: 1-25.
[37] ZHAO H, YANG X L, JU Y G. Kinetic studies of ozone assisted low temperature oxidation of dimethyl ether in a flow reactor using molecular-beam mass spectrometry [J]. Combustion and Flame, 2016, 173: 187-194.
[38] LIAO H D, KANG S Q, HANSEN N, et al. Influence of ozone addition on the low-temperature oxidation of dimethyl ether in a jet-stirred reactor [J]. Combustion and Flame, 2020, 214: 277-286.
[39] LIAO W X, KANG S Q, CHU Z H, et al. Exploring the low-temperature oxidation chemistry with ozone addition in an RCM: A case study on ethanol [J]. Combustion and Flame, 2021: 111727.
[40] HALTER F, HIGELIN P, DAGAUT P. Experimental and detailed kinetic modeling study of the effect of ozone on the combustion of methane [J]. Energy and Fuels, 2011, 25(7): 2909-2916.
[41] WANG Z H, YANG L, LI B, et al. Investigation of combustion enhancement by ozone additive in CH₄/air flames using direct laminar burning velocity measurements and kinetic simulations [J]. Combustion and Flame, 2012, 159(1): 120-129.
[42] VU T M, WON S H, OMBRELLO T, et al. Stability enhancement of ozone-assisted laminar premixed Bunsen flames in nitrogen co-flow [J]. Combustion and Flame, 2014, 161(4): 917-926.
[43] WON S H, JIANG B, DIÉVART P, et al. Self-sustaining n-heptane cool diffusion flames activated by ozone [J]. Proceedings of the Combustion Institute, 2015, 35(1): 881-888.
[44] ROUSSO A C, HANSEN N, JASPER A W, et al. Low-temperature oxidation of ethylene by ozone in a jet-stirred reactor [J]. The Journal of Physical Chemistry A, 2018, 122(43): 8674-8685.
[45] ROUSSO A C, JASPER A W, JU Y G, et al. Extreme low-temperature combustion chemistry: Ozone-initiated oxidation of methyl hexanoate [J]. The Journal of Physical Chemistry A, 2020, 124(48): 9897-9914.
[46] HE X, HANSEN N, MOSHAMMER K. Molecular-weight growth in ozone-initiated low-temperature oxidation of methyl crotonate [J]. The Journal of Physical Chemistry A, 2020, 124(39): 7881-7892.
[47] ALSHEYAB M A T, MUÑOZ A H. Optimisation of ozone production for water and wastewater treatment [J]. Desalination, 2007, 217(1-3): 1-7.
[48] WANG H, SHEEN D A. Combustion kinetic model uncertainty quantification, propagation and minimization [J]. Progress in Energy & Combustion Science, 2015, 47: 1-31.
[49] LI S, YANG B, QI F. Accelerate global sensitivity analysis using artificial neural network algorithm: Case studies for combustion kinetic model [J]. Combustion and Flame, 2016, 168: 53-64.
[50] WANG J X, ZHOU Z J, LIN K L, et al. Facilitating Bayesian analysis of combustion kinetic models with artificial neural network [J]. Combustion and Flame, 2020, 213: 87-97.
[51] LIN K L, ZHOU Z J, LAW C K, et al. Dimensionality reduction for surrogate model construction for global sensitivity analysis: Comparison between active subspace and local sensitivity analysis [J]. Combustion and Flame, 2021, 232: 111501.
[52] ZIEHN T, TOMLIN A S. GUI-HDMR-A software tool for global sensitivity analysis of complex models [J]. Environmental Modelling & Software, 2009, 24(7): 775-785.
[53] LI S, TAO T, WANG J X, et al. Using sensitivity entropy in experimental design for uncertainty minimization of combustion kinetic models [J]. Proceedings of the Combustion Institute, 2017, 36(1): 709-716.
[54] WANG J X, LI S, YANG B. Combustion kinetic model development using surrogate model similarity method [J]. Combustion Theory and Modelling, 2018, 22(4): 777-794.
[55] SHANNON C E. A mathematical theory of communication [J]. The Bell System Technical Journal, 1948, 27(3): 379-423.
[56] LIN S Q, XIE M, WANG J X, et al. Chemical kinetic model reduction through species-targeted global sensitivity analysis (STGSA) [J]. Combustion and Flame, 2021, 224: 73-82.
[57] LIN S Q, ZHOU W X, WU Y, et al. Evaluation of reduced combustion kinetic mechanisms using global sensitivity-based similarity analysis (GSSA) [J]. Proceedings of the Combustion Institute, 2021, 38(1): 1081-1088.

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收稿日期	出版日期
2021-10-16	2022-04-15
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