  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.
