TY - GEN
T1 - AN AUTOMATED GAUSSIAN PROCESS INTEGRATED WITH BAYESIAN OPTIMIZATION APPROACH TO DESIGNING SPLINE-BASED PIN-FIN ARRAYS
AU - Dharmadhikari, Susheel
AU - Berdanier, Reid A.
AU - Thole, Karen A.
AU - Basak, Amrita
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Pin-fins are imperative in the cooling of turbine blades. The design of pin-fins, therefore, has 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) piece-wise 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 degrees of input data. The results reveal that convergence to a 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.
AB - Pin-fins are imperative in the cooling of turbine blades. The design of pin-fins, therefore, has 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) piece-wise 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 degrees of input data. The results reveal that convergence to a 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.
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U2 - 10.1115/GT2023-102018
DO - 10.1115/GT2023-102018
M3 - Conference contribution
AN - SCOPUS:85177477663
T3 - Proceedings of the ASME Turbo Expo
BT - Turbomachinery - Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, GT 2023
Y2 - 26 June 2023 through 30 June 2023
ER -