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Adjoint-Based Design Optimisation of an Internal Cooling Channel U-Bend for Minimised Pressure Losses

机译:基于附件的内部冷却通道U型弯的设计优化,可最大程度地减少压力损失

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The success of shape optimisation depends significantly on the parametrisation of the shape. Ideally, it defines a very rich variation in shape, allows for rapid grid generation of high quality, and expresses the shape in a standard Computer Aided Design (CAD) representation. While most existing parametrisation methods fail at least one of these criteria, this work introduces a novel parametrisation method, which satisfies all three. A tri-variate B-spline volume is used to define the volume to be optimised. The position of the external control points are used as design parameters, while the internal control points are repositioned to ensure regularity of the transformation. The grid generation process transforms a Cartesian grid (defined in parametric space) to the physical space using the tri-variate net of control points. This process guarantees a high grid quality even for large deformations, and has extremely low computational cost as it only involves a transformation from parameter space to physical space. This allows the computation of the grid sensitivities with respect to the design variables at a fraction of the cost of a Computational Fluid Dynamics (CFD) iteration, therefore allowing the use of one-shot methods. This novel parametrisation is applied to the shape optimisation of a U-bend passage of a turbine-blade serpentine-cooling channel with the objective to minimise pressure losses. A steady state, Reynolds-Averaged, density-based Navier-Stokes solver is used to predict the pressure losses at a Reynolds number of 40,000. The sensitivities of the objective function with respect to the control points are computed using a hand-derived adjoint solver and geometry generation system. A one-shot approach is used to simultaneously converge flow, gradient and design, resulting in a rapid design approach with a design time equivalent to approximately 10 normal CFD runs, while still maintaining a CAD representation of the geometry. A large reduction in pressure loss is obtained, and the flow in the optimal geometry is analysed in detail.
机译:形状优化的成功很大程度上取决于形状的参数化。理想情况下,它定义了非常丰富的形状变化,可以快速生成高质量的网格,并以标准的计算机辅助设计(CAD)表示形式来表达形状。尽管大多数现有的参数化方法至少不能满足这些条件之一,但是这项工作引入了一种新颖的参数化方法,该方法可以满足这三个条件。三变量B样条曲线体积用于定义要优化的体积。外部控制点的位置用作设计参数,而内部控制点的位置重新定位以确保转换的规律性。网格生成过程使用控制点的三变量网将笛卡尔网格(在参数空间中定义)转换为物理空间。该过程即使在较大的变形下也可确保较高的网格质量,并且由于仅涉及从参数空间到物理空间的转换,因此具有极低的计算成本。这允许以设计流体动力学(CFD)迭代成本的一小部分来计算相对于设计变量的网格敏感度,因此允许使用一次性方法。该新颖的参数化技术应用于涡轮叶片蛇形冷却通道的U形弯曲通道的形状优化,目的是使压力损失最小化。雷诺数平均的基于密度的Navier-Stokes求解器是一种稳态,用于预测40,000雷诺数下的压力损失。目标函数相对于控制点的敏感度是使用手动衍生的伴随解算器和几何图形生成系统来计算的。一键式方法可同时收敛流动,梯度和设计,从而产生一种快速的设计方法,其设计时间大约相当于10次常规CFD行程,同时仍保持几何图形的CAD表示。大大降低了压力损失,并详细分析了最佳几何形状中的流动。

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