Pin fins are imperative in the cooling of turbine blades. The designs of pin fins, therefore, have seen significant research in the past. With the developments in metal additive manufacturing, novel design approaches toward complex geometries are now feasible. To that end, this article presents a Bayesian optimization approach for designing inline pins that can achieve low pressure loss. The pin-fin shape is defined using featurized (parametrized) piecewise cubic splines in 2D. The complexity of the shape is dependent on the number of splines used for the analysis. From a method development perspective, the study is performed using three splines. Owing to this piece-wise modeling, a unique pin fin design is defined using five features. After specifying the design, a computational fluid dynamics-based model is developed that computes the pressure drop during the flow. Bayesian optimization is carried out on a Gaussian processes-based surrogate to obtain an optimal combination of pin-fin features to minimize the pressure drop. The results show that the optimization tends to approach an aerodynamic design leading to low pressure drop corroborating with the existing knowledge. Furthermore, multiple iterations of optimizations are conducted with varying degree of input data. The results reveal that a convergence to similar optimal design is achieved with a minimum of just twenty five initial design-of-experiments data points for the surrogate. Sensitivity analysis shows that the distance between the rows of the pin fins is the most dominant feature influencing the pressure drop. In summary, the newly developed automated framework demonstrates remarkable capabilities in designing pin fins with superior performance characteristics.

翻译：针肋在涡轮叶片冷却中不可或缺，因此其设计方法在过去得到了广泛研究。随着金属增材制造技术的发展，面向复杂几何形状的新型设计方法成为可能。为此，本文提出了一种基于贝叶斯优化的直列式针肋设计方法，旨在实现低压力损失。通过二维参数化分段三次样条对针肋形状进行特征化定义，其复杂度取决于分析所用样条的数量。从方法开发角度，本研究采用三条样条进行建模。基于这种分段建模方式，每个独特的针肋设计由五个特征参数定义。确定设计后，建立基于计算流体力学的模型以计算流动过程中的压降。利用基于高斯过程的代理模型执行贝叶斯优化，获得使压降最小的最优针肋特征组合。结果表明，优化趋向于低压力损失的气动设计，与现有认知相符。此外，采用不同输入数据量进行多次优化迭代，结果显示仅需最少25个初始实验设计数据点即可使代理模型收敛到相似的最优设计。敏感性分析表明，针肋行间距是影响压力降的最关键特征。综上所述，本文开发的自动化框架在具有卓越性能特征的针肋设计方面展现出显著能力。