基于cad的气动外形优化自适应参数化

R. Jesudasan, J. Mueller
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引用次数: 0

摘要

. 非均匀理性b样条(NURBS)已经成为在CAD/CAE系统之间表示和交换CAD几何图形的工业标准。基于CAD的形状参数化使用CAD模型的参数来修改形状,从而允许将CAD模型集成到设计循环中。然而,典型的商业CAD系统的特征树是不开放的,并且不可能获得基于梯度的优化方法的精确导数。使用基于cad的NSPCC方法,设计人员可以在不违反几何和/或厚度约束的情况下变形设计循环中的多个NURBS补丁。NSPCC方法以CAD描述为输入,通过扰动NURBS边界表示的控制点来修改形状。在这项工作中,提出了一种自适应NSPCC方法,其中优化从更粗糙的设计空间开始,并在需要更多形状控制的设计过程中适应更精细的参数化。改进传感器是基于平滑的基于节点的灵敏度与它的投影到当前参数化的形状模式的比较。将静态参数化方法和自适应参数化方法结合到基于伴随的形状优化过程中,以降低涡轮叶片内冷却通道的总压损失。采用离散伴随流求解器STAMPS计算流场及其导数随时间变化的表面节点位移。将反模AD应用于NSPCC,得到了基于梯度优化的形状导数
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Cad-Based Adaptive Shape Parametrisation for Aerodynamic Shape Optimisation
. Non-Uniform Rational B-Splines (NURBS) have become the industrial standard to represent and exchange a CAD geometry between CAD/CAE systems. CAD-based shape parameterisation uses parameters of a CAD model to modify the shape which allows to integrate a CAD model into the design loop. However, feature-trees of typical commercial CAD systems are not open and obtaining exact derivatives for gradient-based optimisation methods is not possible. Using the CAD-based NSPCC approach a designer can deform multiple NURBS patches in the design loop without violating geometric and/or thickness constraints. The NSPCC approach takes CAD descriptions as input and perturbs the control points of the NURBS boundary representation to modify the shape. In this work, an adaptive NSPCC method is proposed where the optimisation begins with a coarser design space and adapts to finer parametrisation during the design process where more shape control is needed. The refinement sensor is based on a comparison of smoothed node-based sensitivity compared to its projection onto the shape modes of the current parametrisation. Both static and adaptive parametrisation methods are coupled in the adjoint-based shape optimisation process to reduce the total pressure loss of a turbine blade internal cooling channel. The discrete adjoint flow solver STAMPS is used to compute the flow fields and their derivatives w.r.t. surface node displacements. The shape derivatives for gradient-based optimisation are obtained by application of reverse mode AD to the NSPCC
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