-
散射截面和反应速率系数是阐明分子气体态-态碰撞传能机制的重要参数，也是进行非平衡气体动力学建模的重要依据。本文采用动力学模拟中的准经典轨迹方法（ QCT）计算了90个不同初始振动态组合的N2(v)+O2(w)碰撞过程，详细讨论了各个振动激发、解离反应通道的贡献和演变趋势。研究发现，O2和N2在振动-振动能量交换（ VV）通道的贡献比较接近，振动-平动跃迁（ VT）通道主要以O2为主；总解离截面主要来自O2单解离通道，交换解离其次，N2单解离和双解离通道的贡献相对较小。基于QCT数据集，训练了性能良好的神经网络模型（相关系数R值达到0.99） ，可用于预测N2+O2态-态碰撞的总解离截面。和仅采用动力学模拟方法相比，计算成本降低了约91.94%。在5000-30000K高温范围内，给出了VV/VT速率系数的解析表达式。The scattering cross-sections and reaction rate coefficients are crucial parameters for elucidating the energy transfer mechanism of state-to-state collisions between molecular gases and also serve as a fundamental basis for modeling the non-equilibrium flow field. However, the database of kinetic processes related to nitrogen shock flows is still being developed. In this work, a detailed kinetic study of the N2+O2 collision is carried out by combining the quasi-classical trajectory method (QCT) and neural network model (NN). Firstly, QCT is used to calculate 90 N2(v)+O2(w) processes with various initial vibrational states (v,w), and the contributions of all vibrational excitation and dissociation reaction channels are discussed. The following conclusions were drawn: i) the contributions of the vibration-vibration (VV) energy exchange channel of O2 and N2 are similar, while the vibration-translational (VT) transition mainly occurs on O2; and ii) the total dissociation cross-section primarily results from the O2 single-dissociation channel, followed by the exchange-dissociation channel, with relatively minor contributions from the N2 single- and double-dissociation channels. Then, based on the QCT dataset, a high-performance NN model (R-value of 0.99) is trained to predict the total dissociation cross-section caused by N2(v)+O2(w) collisions. Compared to the method using only QCT, the computational cost is reduced by approximately 91.94%. Finally, to facilitate use in kinetic modeling, Arrhenius-type fits for the VV/VT rate coefficients are provided over the temperature range of 5000-30000K, and an exponential form related to the translational energy Et was used to fit the total dissociation cross-section.
-
Keywords:
- State-to-state reaction rate coefficient /
- Vibration relaxation /
- Collision dissociation /
- Neural network model
-
[1] Wang Q Y, Cong K L, Liu L L, Lu H Z, Xu S J 2017 Phys. of Gases 24 (in Chinese) [王庆洋， 丛堃林， 刘丽丽， 陆宏志， 徐胜金2017气体物理2 4]
[2] Lu D R, Chen Z Y, Guo X, Tian W S 2017 Advance in Mechanics 396 (in Chinese) [吕达仁， 陈泽宇， 郭霞， 田文寿2017力学进展396]
[3] Dong W Z, DIing M S, Gao T S, Jiang T 2013 Acta Aerodynamica Sinica 36 (in Chinese) [董维中， 丁明松， 高铁锁， 江涛2013空气动力学学报3 6]
[4] Guo Y J, Zeng L, Zhang H Y, Dai G Y, Wang A L, Qiu B, Zhou S G, Liu X 2017 Acta Aerodynamica Sinica 354 (in Chinese) [国义军， 曾磊， 张昊元， 代光月， 王安龄， 邱波， 周述光， 刘骁2017空气动力学学报354]
[5] M. Cacciatore 1996 Molecular Physics and Hypersonic Flows 48221
[6] Pavlov A V 2011 Geomagn. Aeron. 51143
[7] Treanor C E 1965 J. Chem. Phys. 43532
[8] Nagnibeda E, Papina K, Kunova O 2018 AIP Conf. Proc. 1060012
[9] Laux C O, Pierrot L, Gessman R J 2012 Chem. Phys. 39846
[10] Zhao X, Xu X, Xu H 2024 J. Chem. Phys. 161231101
[11] Hong Q, Bartolomei M, Pirani F, Sun Q, Coletti C 2025 J. Chem. Phys. 162114308
[12] Feng D, Song Y, Wang Z, Yang L, Zhang Z, Yang Y 2025 J. Chem. Phys. 162114107
[13] He D, Liu T, Li R, Hong Q, Li F, Sun Q, Si T, Luo X 2024 J. Chem. Phys. 161244302
[14] Andrienko D, Boyd I D 201755th AIAA Aerospace Sciences Meeting Grapevine Texas, January 9-13, 2017 p0659
[15] Kurnosov A K, Napartovich A P, Shnyrev S L, Cacciatore M 2010 Plasma Sources Sci. Technol. 19045015
[16] Esposito F, Garcia E, Laganà A 2017 Plasma Sources Sci. Technol. 26045005
[17] Lino Da Silva M, Loureiro J, Guerra V 2012 Chem. Phys. Lett. 53128
[18] Varga Z, Meana-Pañeda R, Song G, Paukku Y, Truhlar D G 2016 J. Chem. Phys. 144024310
[19] Garcia E, Verdasco J E, Laganà A 2020 J. Phys. Chem. A 1246445
[20] Andrienko D A, Boyd I D 2018 J. Chem. Phys. 148084309
[21] Garcia E, Pirani F, Laganà A, Martí C 2017 Phys. Chem. Chem. Phys. 1911206
[22] Garcia E, Laganà A, Pirani F, Bartolomei M, Cacciatore M, Kurnosov A 2016 J. Phys. Chem. A 1205208
[23] Billing G D and Jolicard G 1982 Chem. Phys. 65323
[24] Billing G D 1994 Chem. Phys. 179463
[25] Garcia E, Kurnosov A, Laganà A, Pirani F, Bartolomei M, Cacciatore M 2016 J. Phys. Chem. B 1201476
[26] Koner D, Unke O T, Boe K, Bemish R J, Meuwly M 2019 J. Chem. Phys. 150211101
[27] Chen J, Li J, Bowman J M, Guo H 2020 J. Chem. Phys. 153054310
[28] Hong Q, Storchi L, Bartolomei M, Pirani F, Sun Q, Coletti C 2023 Eur. Phys. J. D 77128
[29] Gu K M, Zhang H, Cheng X L 2023 J. Chem. Phys. 158244302
[30] Huang X, Gu K M, Guo C M, Cheng X L 2023 Phys. Chem. Chem. Phys. 2529475
[31] Guo C M, Zhang H, Cheng X L 2024 J. Phys. Chem. A 1285435
[32] Bernstein R B, Bederson B 1980 Phys. Today 3379
[33] Fernández-Ramos A, Miller J A, Klippenstein S J, Truhlar D G 2006 Chem. Rev. 1064518
[34] Hu X, Hase W L, Pirraglia T 1991 J. Comput. Chem. 121014
[35] Gutzwiller M C 1990 Chaos in classical and quantum mechanics (Berlin: Springer)
[36] Chaudhry R S, Bender J D, Valentini P, Schwartzentruber T E, Candler G V 201646th AIAA Thermophysics Conference Washington, June 13-17, 2016 p4319
[37] Mankodi T K, Bhandarkar U V, Myong R S 2020 Phys. Fluids 32036102
[38] Andrienko D, Boyd I D 201747th AIAA Thermophysics Conference Denver, Colorado, June 5-9, 2017 p3163
[39] Andrienko D, Boyd I D 2018 J. Thermophys Heat Transfer Atlanta 324
[40] Rumelhart D E, Hintont G E, Williams R J 1986 Nature 3236088
[41] Moré J J 1978 Numerical Analysis Berlin, Heidelberg, 1978 pp105
[42] Chaudhry R S, Candler G V 2019 AIAA Scitech Forum San Diego, California, January 7-11, 2019 p0789
[43] Mankodi T K, Bhandarkar U V, Puranik B P 2018 J. Chem. Phys. 148144305
计量
- 文章访问数: 151
- PDF下载量: 9
- 被引次数: 0
下载:
