椭圆槽气膜冷却结构的优化研究
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摘要
针对椭圆槽气膜冷却结构展开优化研究,优化参数为椭圆长轴长度L1、椭圆短轴长度L2和槽深h,优化目标为最大化气膜绝热冷却效率。首先对椭圆槽气膜冷却物理模型进行计算流体力学方法求解,获得一定容量的数据样本;并基于数据样本训练支持向量机参数,建立优化代理模型;最后引入遗传算法在优化区间内进行寻优,获得最佳的椭圆槽结构。在吹风比为0.6的工况下,优化后的L1,L2和h分别为2.22D,2.65D和0.74D (D为气膜孔径),冷却效率较参考结构提升了42%;在吹风比为1.2的工况下,优化后的L1,L2和h分别为3.60D,2.70D和0.67D,冷却效率较参考结构提升了73%。通过优化,气膜孔下游截面肾型涡对得到有效抑制,而反肾型涡对则被强化,气膜展向覆盖能力明显增强。优化结果表明了支持向量机代理模型和遗传算法在气膜冷却结构优化的有效性。
In order to optimize the film cooling structure of the cratered cylindrical hole,an optimization study was conducted. Some influence factors,such as the length of the major axis of the ellipse L1,the length of the minor axis L2 and the depth of the cratered h,are taken into considerations. The optimization objective is to maximize the adiabatic cooling efficiency of the film. Firstly,the physical model of film cooling in cratered cylindrical hole was solved by CFD simulation. A certain capacity data sample was obtained. Then,based on the data samples,support vector machine(SVM)parameters are trained,and the optimal agent model can be established. Finally,genetic algorithm is introduced to optimize the interval and get the best cratered cylindrical hole structure. Under the condition of blowing ratio of 0.6,the optimized L1,L2 and h are 2.22 D,2.65 D and 0.74 D,respectively(D is the diameter of the film). The cooling efficiency increased by 42% compared with the reference model. Under the condition of blowing ratio of 1.2,the optimized L1,L2 and h are 3.60 D,2.70 D and 0.67 D,respectively. And the cooling efficiency is improved by 73% compared with the reference model. After optimization,the kidney vortex at the downstream of the film hole was effectively suppressed,while the anti-kidney vortex was strengthened,and the coverage of the film was significantly enhanced. The results show the feasibility of SVM agent model and genetic algorithm in the film cooling structure optimization.
In order to optimize the film cooling structure of the cratered cylindrical hole,an optimization study was conducted. Some influence factors,such as the length of the major axis of the ellipse L1,the length of the minor axis L2 and the depth of the cratered h,are taken into considerations. The optimization objective is to maximize the adiabatic cooling efficiency of the film. Firstly,the physical model of film cooling in cratered cylindrical hole was solved by CFD simulation. A certain capacity data sample was obtained. Then,based on the data samples,support vector machine(SVM)parameters are trained,and the optimal agent model can be established. Finally,genetic algorithm is introduced to optimize the interval and get the best cratered cylindrical hole structure. Under the condition of blowing ratio of 0.6,the optimized L1,L2 and h are 2.22 D,2.65 D and 0.74 D,respectively(D is the diameter of the film). The cooling efficiency increased by 42% compared with the reference model. Under the condition of blowing ratio of 1.2,the optimized L1,L2 and h are 3.60 D,2.70 D and 0.67 D,respectively. And the cooling efficiency is improved by 73% compared with the reference model. After optimization,the kidney vortex at the downstream of the film hole was effectively suppressed,while the anti-kidney vortex was strengthened,and the coverage of the film was significantly enhanced. The results show the feasibility of SVM agent model and genetic algorithm in the film cooling structure optimization.
引文
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[3]姚玉,张靖周,何飞,等.涡轮叶片吸力面气膜冷却效率的数值研究[J].航空动力学报,2010,25(6):1245-1250.
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[5] Teng S,Han J C,Poinsatte P E. Effect of Film-Hole Shape on Turbine-Blade Film-Cooling Performance[J].Journal of Thermophysics and Heat Transfer,2001,15(3):257-265.
[6] Lu Y,Dhungel A,Ekkad S V,et al. Effect of Trench Width and Depth on Film Cooling from Cylindrical Holes Embedded in Trenches[J]. Journal of Turbomachinery,2009,131(1).
[7] Lu Y,Nasir H,Ekkad S V. Film Cooling from a Row of Holes Embedded in Transverse Slots[R]. ASME GT2005-68598.
[8] Yiping L,Ekked S V. Predictions of Film Cooling from Cylindrical Holes Embedded in Trenches[R]. AIAA2006-3401.
[9] Kim J H,Kim K Y. Surrogate-Based Optimization of a Cratered Cylindrical Hole to Enhance Film-Cooling Effectiveness[J]. Journal of Thermal Science and Technology,2016,11(2).
[10]章大海,陈秋炀,曾敏,等.不同横槽结构对气膜绝热冷却效率影响的数值研究[J].航空动力学报,2008,23(4):611-616.
[11]李广超,陈钰恺,刘永泉,等.利用W型槽提高气膜冷却效率机理[J].推进技术,2016,37(3):520-526.(LI Guang-chao,CHEN Yu-kai,LIU Yong-quan,et al. Mechanism on Increasing Film Cooling Effectiveness by W Shape Slots[J]. Journal of Propulsion Technology,2016,37(3):520-526.)
[12]蒋永健,何立明,于锦禄,等.利用横向槽改善气膜冷却效率的数值研究[J].推进技术,2008,29(3):286-289.(JIANG Yong-jian,HE Li-ming,YU Jinlu,et al. Numerical Investigation on Improving Film Cooling Effectiveness with Transverse Slots[J]. Journal of Propulsion Technology,2008,29(3):286-289.)
[13] Dorrington J R,Bogard D G,Bunker R S,et al. Film Effectiveness Performance for Coolant Holes Imbedded in Various Shallow Trench and Crater Depressions[R].ASME GT 2007-27992.
[14]渠立红,张靖周,谭晓茗.狭缝喷注-发散冷却的综合冷却效果数值研究[J].推进技术,2018,39(4):849-856.(QU Li-hong,ZHANG Jing-zhou,TAN Xiao-ming. Numerical Investigation on Overall Cooling Effectiveness for a Combined Scheme of Slot Injection and Effusion Cooling[J]. Journal of Propulsion Technology,2018,39(4):849-856.)
[15] Silieti M,Kassab A J,Divo E. Film Cooling Effectiveness from a Single Scaled-up Fan-Shaped Hole:a CFD Simulation of Adiabatic and Conjugate Heat Transfer Models[R]. ASME GT 2005-68431.
[16] Immarigeon A,Hassan I. An Advanced Impingement/Film Cooling Scheme for Gas Turbines–Numerical Study[J]. International Journal of Numerical Methods for Heat&Fluid Flow,2006,16(4):470-493.
[17] Harrison K,Bogard D. Comparison of RANS Turbulence Models for Prediction of Film Cooling Performance[R]. ASME GT 2008-50366.
[18] Cortes C,Vapnik V. Support-Vector Networks[J]. Machine Learning,1995,20(3):273-297.
[2]王文三,唐菲,赵庆军,等.新型双射流冷却孔对气膜绝热冷却效率影响的研究[J].工程热物理学报,2011,32(8):1291-1294.
[3]姚玉,张靖周,何飞,等.涡轮叶片吸力面气膜冷却效率的数值研究[J].航空动力学报,2010,25(6):1245-1250.
[4] Bunker R S. A Review of Shaped Hole Turbine FilmCooling Technology[J]. Journal of Heat Transfer,2005,127(4).
[5] Teng S,Han J C,Poinsatte P E. Effect of Film-Hole Shape on Turbine-Blade Film-Cooling Performance[J].Journal of Thermophysics and Heat Transfer,2001,15(3):257-265.
[6] Lu Y,Dhungel A,Ekkad S V,et al. Effect of Trench Width and Depth on Film Cooling from Cylindrical Holes Embedded in Trenches[J]. Journal of Turbomachinery,2009,131(1).
[7] Lu Y,Nasir H,Ekkad S V. Film Cooling from a Row of Holes Embedded in Transverse Slots[R]. ASME GT2005-68598.
[8] Yiping L,Ekked S V. Predictions of Film Cooling from Cylindrical Holes Embedded in Trenches[R]. AIAA2006-3401.
[9] Kim J H,Kim K Y. Surrogate-Based Optimization of a Cratered Cylindrical Hole to Enhance Film-Cooling Effectiveness[J]. Journal of Thermal Science and Technology,2016,11(2).
[10]章大海,陈秋炀,曾敏,等.不同横槽结构对气膜绝热冷却效率影响的数值研究[J].航空动力学报,2008,23(4):611-616.
[11]李广超,陈钰恺,刘永泉,等.利用W型槽提高气膜冷却效率机理[J].推进技术,2016,37(3):520-526.(LI Guang-chao,CHEN Yu-kai,LIU Yong-quan,et al. Mechanism on Increasing Film Cooling Effectiveness by W Shape Slots[J]. Journal of Propulsion Technology,2016,37(3):520-526.)
[12]蒋永健,何立明,于锦禄,等.利用横向槽改善气膜冷却效率的数值研究[J].推进技术,2008,29(3):286-289.(JIANG Yong-jian,HE Li-ming,YU Jinlu,et al. Numerical Investigation on Improving Film Cooling Effectiveness with Transverse Slots[J]. Journal of Propulsion Technology,2008,29(3):286-289.)
[13] Dorrington J R,Bogard D G,Bunker R S,et al. Film Effectiveness Performance for Coolant Holes Imbedded in Various Shallow Trench and Crater Depressions[R].ASME GT 2007-27992.
[14]渠立红,张靖周,谭晓茗.狭缝喷注-发散冷却的综合冷却效果数值研究[J].推进技术,2018,39(4):849-856.(QU Li-hong,ZHANG Jing-zhou,TAN Xiao-ming. Numerical Investigation on Overall Cooling Effectiveness for a Combined Scheme of Slot Injection and Effusion Cooling[J]. Journal of Propulsion Technology,2018,39(4):849-856.)
[15] Silieti M,Kassab A J,Divo E. Film Cooling Effectiveness from a Single Scaled-up Fan-Shaped Hole:a CFD Simulation of Adiabatic and Conjugate Heat Transfer Models[R]. ASME GT 2005-68431.
[16] Immarigeon A,Hassan I. An Advanced Impingement/Film Cooling Scheme for Gas Turbines–Numerical Study[J]. International Journal of Numerical Methods for Heat&Fluid Flow,2006,16(4):470-493.
[17] Harrison K,Bogard D. Comparison of RANS Turbulence Models for Prediction of Film Cooling Performance[R]. ASME GT 2008-50366.
[18] Cortes C,Vapnik V. Support-Vector Networks[J]. Machine Learning,1995,20(3):273-297.