AN AUTOMATED GAUSSIAN PROCESS INTEGRATED WITH BAYESIAN OPTIMIZATION APPROACH TO DESIGNING SPLINE-BASED PIN-FIN ARRAYS

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationTurbomachinery - Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791887110
DOIs
StatePublished - 2023
EventASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, GT 2023 - Boston, United States
Duration: Jun 26 2023Jun 30 2023

Publication series

NameProceedings of the ASME Turbo Expo
Volume13D

Conference

ConferenceASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, GT 2023
Country/TerritoryUnited States
CityBoston
Period6/26/236/30/23

All Science Journal Classification (ASJC) codes

  • General Engineering

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