面向阻力发散的CRM机翼气动优化设计
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摘要
为改善CRM机翼在偏离巡航点阻力系数迅速增大的问题,同时为得到更加稳定的阻力发散特性,在满足工程实际的气动约束和几何约束条件下,基于RANS方程及其离散伴随方程、FFD几何参数方法、线弹性网格变形方法以及SNOPT梯度优化算法等,对其进行单点、多权重两点以及多点优化设计。结果表明:不同优化策略下优化后机翼在主设计点处阻力系数较初始构型均有不同程度降低,经多点优化后机翼的巡航马赫数提高,且阻力发散特性的鲁棒性得以提高。
In order to increase the drag coefficient of the CRM wing at a deviation from the cruising point,and look for a more stable drag divergence characteristic. Under the conditions of aerodynamic constrains and geometric constrains of engineering practice,based on the RANS equation and its discreteadjoint equation,and the FFD geometric parameterization and algebraic-linear elasticity method and SNOPT gradient optimization algorithm,a single point,multi-weight two points and multi points optimization design is carried out. The results show that the drag coefficient of the optimized wing at the main design point is lower than of the initial configuration under the different optimization strategies. The cruising Mach number of the wing is improved and the robustness of the resistance divergence is also improved after the multi-point optimization.
In order to increase the drag coefficient of the CRM wing at a deviation from the cruising point,and look for a more stable drag divergence characteristic. Under the conditions of aerodynamic constrains and geometric constrains of engineering practice,based on the RANS equation and its discreteadjoint equation,and the FFD geometric parameterization and algebraic-linear elasticity method and SNOPT gradient optimization algorithm,a single point,multi-weight two points and multi points optimization design is carried out. The results show that the drag coefficient of the optimized wing at the main design point is lower than of the initial configuration under the different optimization strategies. The cruising Mach number of the wing is improved and the robustness of the resistance divergence is also improved after the multi-point optimization.
引文
[1]Osusky L,Zingg D W. Application of an Efficient NewtonKrylov Algorithmfor Aerodynamic Shape Optimization Based on the Reynolds-Averaged Navier-Stokes Equations[C]//21st AIAA Computation Fluid Dynamics Conference,2013.
[2]Kenway G K W,Martins J. Multipoint Aerodynamic Shape Optimization Investigations on the Common Research Model Wing[C]//53rd AIAA Aerospace Sciences Meeting,2015:2015-0264.
[3]Vassberg J C,De Haan M A,Rivers S M,et al. Development of a Common Research Model for Applied CFD Validation Studies[C]//AIAA Applied Aerodynamics Conference,2008.
[4]Kenway G K,Kennedy G J,Martins J R R A. ACAD-free Approach to High-fidelity Aerostructural Optimization[C]//Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference,2010.
[5]Lyu Z,Xu Z,Martins J. Benchmarking Optimization Algorithms for Wing Aerodynamic Design Optimization[C]//8th International Conference on Computational Fluid Dynamics,2014.
[2]Kenway G K W,Martins J. Multipoint Aerodynamic Shape Optimization Investigations on the Common Research Model Wing[C]//53rd AIAA Aerospace Sciences Meeting,2015:2015-0264.
[3]Vassberg J C,De Haan M A,Rivers S M,et al. Development of a Common Research Model for Applied CFD Validation Studies[C]//AIAA Applied Aerodynamics Conference,2008.
[4]Kenway G K,Kennedy G J,Martins J R R A. ACAD-free Approach to High-fidelity Aerostructural Optimization[C]//Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference,2010.
[5]Lyu Z,Xu Z,Martins J. Benchmarking Optimization Algorithms for Wing Aerodynamic Design Optimization[C]//8th International Conference on Computational Fluid Dynamics,2014.