基于离散伴随的跨声速构型气动优化反设计
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摘要
为了有效控制跨声速构型的表面压力分布,利用离散伴随方法精确、快速求解目标函数对大规模设计变量梯度的优势,采用CST和FFD几何参数法,分别构建了二维、三维气动反设计优化系统。通过NACA0012翼型和CRM机翼对构建的反设计系统进行验证,优化目标分别为RAE2822翼型和多点优化后的CRM构型的压力分布。结果表明:通过压力分布反设计得到的构型与目标压力分布对应的构型在几何外形上一致,迭代收敛性上气动反设计优化效率优于多点优化。
In order to effectively control the surface pressure distribution of transonic configurations,and the advantages of discrete adjointmethod in solving the gradient of large-scale design variables accurately and quickly are discussed,two-dimensional and three-dimensional aerodynamic inverse design optimization systems are constructed by using CST parameterization and FFD parameterization method respectively.The two-dimensional airfoil NACA0012 and three-dimensional CRM wing are verified by inverse design,the target goal is RAE2822 airfoil and multi-point optimized CRM′s pressure respectively.The results show that the configuration obtained by inverse design of pressure distribution is consistent with the configuration corresponding to the target pressure distribution in geometric shape,and the optimization efficiency of the aerodynamic inverse design is better than multi-points optimization on the iteration convergence.
In order to effectively control the surface pressure distribution of transonic configurations,and the advantages of discrete adjointmethod in solving the gradient of large-scale design variables accurately and quickly are discussed,two-dimensional and three-dimensional aerodynamic inverse design optimization systems are constructed by using CST parameterization and FFD parameterization method respectively.The two-dimensional airfoil NACA0012 and three-dimensional CRM wing are verified by inverse design,the target goal is RAE2822 airfoil and multi-point optimized CRM′s pressure respectively.The results show that the configuration obtained by inverse design of pressure distribution is consistent with the configuration corresponding to the target pressure distribution in geometric shape,and the optimization efficiency of the aerodynamic inverse design is better than multi-points optimization on the iteration convergence.
引文
[1] Kevin A Lane,David D Marshall.Inverse Airfoil Design Utilizing CST Parameterization[C].Orlando,Florida:48th AIAA Aerospace Sciences Meeting,2010.
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[4] David Koo,David W Zingg.Progress in Aerodynamic Shape Optimization Basedon the Reynolds- Averaged Navier- Stokes Equations[C].California,USA:54rd AIAA Aerospace Sciences Meeting,2016.
[5] 刘峰博,郝海兵,李典,等.离散伴随方法在气动优化设计中的应用[J].航空计算技术,2017,47(2):33-36,40.
[6] Kufan B M.Universal Parametric Geometry Representation Method[J].Journal of Aircraft,2008,45(1):142-158.
[7] Gill P,Murray W,Saunders M A.SNOPT:an SQP Algorithm for Large- Scale Constrained Optimization[J].SIAM Review,2005(47):99-131.
[8] Lyu Z,Xu Z,Martins J.Benchmarking Optimization Algorithms for Wing Aerodynamic Design Optimization[C].Chengdu,China:8th International Conference on Computational Fluid Dynamics(ICCFD8),2014.
[2] 刘俊,宋文萍,韩中华,等.Kriging模型在翼型反设计中的应用研究[J].空气动力学报,2014,32(4):518-526.
[3] Lyu Z,KenwayG K W,Martins J.Multipoint Aerodynamic Shape Optimization Investigations on the Common Research Model Wing[J].AIAA Journal,2014,54(1):61-73.
[4] David Koo,David W Zingg.Progress in Aerodynamic Shape Optimization Basedon the Reynolds- Averaged Navier- Stokes Equations[C].California,USA:54rd AIAA Aerospace Sciences Meeting,2016.
[5] 刘峰博,郝海兵,李典,等.离散伴随方法在气动优化设计中的应用[J].航空计算技术,2017,47(2):33-36,40.
[6] Kufan B M.Universal Parametric Geometry Representation Method[J].Journal of Aircraft,2008,45(1):142-158.
[7] Gill P,Murray W,Saunders M A.SNOPT:an SQP Algorithm for Large- Scale Constrained Optimization[J].SIAM Review,2005(47):99-131.
[8] Lyu Z,Xu Z,Martins J.Benchmarking Optimization Algorithms for Wing Aerodynamic Design Optimization[C].Chengdu,China:8th International Conference on Computational Fluid Dynamics(ICCFD8),2014.