论文标题
使用大型涡流模拟的跨性涡轮叶片的基于伴随的后缘形状优化
Adjoint-based trailing edge shape optimization of a transonic turbine vane using large eddy simulations
论文作者
论文摘要
燃气轮机喷嘴导灯的后边缘的形状对下游停滞压力损失和叶轮表面上的热传递具有显着影响。传统上，涡轮机械组件的基于伴随的设计优化方法使用了诸如Reynolds平均Navier-Stokes之类的低保真模拟。为了可靠地捕获涡轮叶片上湍流中涉及的复杂流动现象，需要高保真模拟，例如大型涡流模拟（LES）。在本文中，使用LES进行了基于伴随的后缘形状优化，以减少叶轮表面上的压力损失和热传递。混乱的湍流动力学限制了伴随方法对从LES计算的长期平均物镜函数的有效性。粘度稳定的不稳定伴随方法用于以合理的精度获得设计目标函数的梯度。利用贝叶斯优化的梯度用于在目标函数和梯度评估中稳健地处理噪声。使用$ 5 $凸面设计的线性组合对后边缘形状进行参数化。将优化的结果（在超级计算机MIRA上执行）与使用同一问题的无衍生设计优化生成的最佳设计进行了比较。
The shape of the trailing edge of a gas turbine nozzle guide vane has a significant effect on the downstream stagnation pressure loss and heat transfer over the surface of the vane. Traditionally, adjoint-based design optimization methods for turbomachinery components have used low-fidelity simulations like Reynolds averaged Navier-Stokes. To reliably capture the complex flow phenomena involved in turbulent flow over a turbine vane, high-fidelity simulations like large eddy simulation (LES) are required. In this paper, an adjoint-based trailing edge shape optimization using LES is performed to reduce pressure loss and heat transfer over the surface of the vane. The chaotic dynamics of turbulence limits the effectiveness of the adjoint method for long-time averaged objective functions computed from LES. A viscosity stabilized unsteady adjoint method is used to obtain gradients of the design objective function with reasonable accuracy. A gradient utilizing Bayesian optimization is used to robustly handle noise in the objective function and gradient evaluations. The trailing edge shape is parameterized using a linear combination of $5$ convex designs. Results from the optimization, performed on the supercomputer Mira, are compared with optimal designs generated using derivative-free design optimization of the same problem.
