A Discrete Adjoint Framework for Unsteady Aerodynamic and Aeroacoustic Optimization
A Discrete Adjoint Framework for Unsteady Aerodynamic and Aeroacoustic Optimization
复制标题
非定常气动和气动声学优化的离散伴随框架
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
J. Alonso
中科院分区：
文献类型：
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作者：
Beckett Y. Zhou;Tim Albring;N. Gauger;T. Economon;F. Palacios;J. Alonso
在本文中，我们提出了一个非定常空气动力学和气动声学优化框架，其中算法微分（AD）应用于开源多物理场求解器SU 2，以获得设计灵敏度。基于AD的一致性离散伴随求解器的开发，直接继承了由于整个非线性固定点迭代器的微分的原始流求解器的收敛特性。此外，耦合CFD-CAA远场噪声预测框架使用的可渗透表面Ffowcs WilliamsHawkings方法也被开发。由此产生的AD为基础的离散伴随求解器适用于阻力和噪声最小化问题。结果表明，由于整个设计链（包括动网格运动程序和各种湍流模型以及CFD-CAA混合模型）的算法差异，这种基于AD的离散伴随框架提供的非定常伴随信息是准确和鲁棒的。
In this paper, we present an unsteady aerodynamic and aeroacoustic optimization framework in which algorithmic differentiation (AD) is applied to the open-source multi-physics solver SU2 to obtain design sensitivities. An AD-based consistent discrete adjoint solver is developed which directly inherits the convergence properties of the primal flow solver due to the differentiation of the entire nonlinear fixed-point iterator. In addition, a coupled CFD-CAA far-field noise prediction framework using a permeable surface Ffowcs WilliamsHawkings approach is also developed. The resultant AD-based discrete adjoint solver is applied to both drag and noise minimization problems. The results suggest that the unsteady adjoint information provided by this AD-based discrete adjoint framework is accurate and robust, due to the algorithmic differentiation of the entire design chain including the dynamic mesh movement routine and various turbulence model, as well as the hybrid CFD-CAA model.
复杂高扬程配置的主动流量控制优化设计
DOI：
10.2514/6.2014-2515
发表时间：
2014
期刊：
影响因子：
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作者：
Anil Nemili;Emre Özkaya;Nicolas R. Gauger;Felix Kramer;Tobias Höll;Frank Thiele
通讯作者：
Frank Thiele
影响因子：
2.7
作者：
Hogan, Robin J.
通讯作者：
Hogan, Robin J.