Abstract
CFD-based design optimization of turbulent flow scenarios is usually computationally expensive due to requirement of high-fidelity simulations. Previous studies prove that one way to reduce computational resource usage is to employ Machine Learning/Surrogate Modeling approaches for intelligent sampling of data points in the design space and is also an active area of research, but lacks enough experimental validation. Such a method has been used to optimize the shape of a U-bend channel for the minimization of pressure drop. U-bends are an integral part of serpentine cooling channels inside gas turbine blades but also contribute to total pressure drop by more than 20%. Reducing this pressure loss can help in more efficient cooling at low pumping power. A ‘U-bend’ or 180-degree bend shape has been used from literature, and a 16-dimensional design space has been created using parametrized spline curves, which creates a variety of shapes inside a given bounding box. A Latin Hypercube Sampling (LHS) was carried out for populating the initial design space with output data from the CFD simulation. After training a surrogate model on this data set, Bayesian updates were used to search for an optimum using an exploration vs exploitation approach. The resulting optimum shape showed that pressure drop was lowered by almost 30%, when compared to the baseline. The aim of this study is to experimentally validate this method using 3D printed models of the baseline and optimum channels respectively. Pressure taps placed across stream-wise locations on these channels helped to create a pressure profile for turbulent flow at a Reynolds number of 17000, for comparison to CFD results.
