Evolutionary optimization of transonic airfoils for static and dynamic trim performance
Evolutionary optimization of transonic airfoils for static and dynamic trim performance
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DOI：
10.1177/1045389x16679019
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发表时间：
2017-05
影响因子：
2.7
通讯作者：
M. Candon;R. Carrese;N. Joseph;H. Ogawa;P. Marzocca
中科院分区：
文献类型：
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作者：
M. Candon;R. Carrese;N. Joseph;H. Ogawa;P. Marzocca
优化框架在飞机设计中的发展和加速使用证明了它们能够识别最佳的和通常非直观的形状，作为多学科设计目标的结果。翼型设计是一个不断修正的多学科问题，是关键，以说明性能的优化框架，包括数值模拟，灵活的形状参数化，和智能进化算法。这个经典问题中经常被忽略的一个部分是考虑配平条件下的动态气动弹性行为，它可以产生飞行包线的显式边界。配平引入了与控制静态性能的目标（例如，空气动力学和/或结构）的显著强耦合，从而导致高度非线性和不连续的设计空间。本文提出了一种多学科性能改进的多目标粒子群优化框架，涉及配平条件下的气动、结构和气动弹性设计准则。该框架是由自适应克里格代理人，这是协同使用的数值求解器，以确定最佳的解决方案内的计算约束的建设援助。设计师的偏好被引入，以反映目标之间的最佳折衷。优化过程的结果表明，设计变量的影响和相互作用有很大的范围，通过获得的最终候选翼型的目录，说明了所有目标之间微妙而清晰的区别。
The development and accelerated use of optimization frameworks in aircraft design is a testament to their ability to identify optimal and often non-intuitive shapes as a result of multi-disciplinary design objectives. Airfoil design is a continuously revised multi-disciplinary problem, and is pivotal to illustrate the performance of optimization frameworks involving numerical simulation, flexible shape parametrization, and intelligent evolutionary algorithms. An often overlooked component of this classic problem is to consider the dynamic aeroelastic behavior under trim conditions, which can generate explicit boundaries to the flight envelope. Trim introduces a significantly strong coupling with objectives governing static performance, e.g. aerodynamic and/or structural, thereby resulting in a highly nonlinear and discontinuous design space. In this paper, a multi-objective particle swarm optimization framework for multi-disciplinary performance improvement is presented, pertaining to aerodynamic, structural and aeroelastic design criteria at trim conditions. The framework is assisted by the construction of adaptive Kriging surrogates, which is cooperatively used with the numerical solver to identify optimal solutions within a computational constraint. Designer preferences are introduced to reflect the optimal compromise between the objectives. Results of the optimization process indicate a large spread in design variable influence and interaction, and a subtle yet clear distinction between all objectives is illustrated through the catalog of final airfoil candidates obtained.