Young-Seok Kang

Aerodynamic Optimization of Axial Turbine Tip Cavity With Approximation Model

Design optimization of unshrouded rotor tip cavity of a high pressure turbine stage with low aspect ratio was carried out to maximize the turbine stage efficiency. Cavity shapes were parameterized with 4 design variables including rim thickness, cavity depth, cavity front blend radius and cavity aft blend radius. Initially the CCD method was utilized for sampling experimental points and the Kriging method was chosen to construct an approximation model. The optimum points derived from the approximation model were assessed by CFD analyses to verify the approximation model. The approximation model was refined repeatedly by adding more experiment points to minimize difference of CFD result and predicted value from the approximation model at the optimum point.The optimization result showed that there is an optimum ratio of cavity depth to tip clearance height, while the optimum design suggests cavity front blend radius and cavity aft blend radius be as small as possible within the design range. As the tip clearance height increases, the optimized tip cavity depth increases. However, the rim thickness has little effect on the optimum tip cavity depth. Without the tip cavity, leakage flow at fore part of the blade suction surface develops large vortex flow from the starting point of the unguided turning region due to adverse pressure gradient. The tip cavity prevents the early leakage flow from flow to the suction surface, which suppresses the leakage flow dissipation to the loss. It results in efficiency improvement. The effect of the tip cavity on the efficiency increases at the larger tip clearance.On the other hand, the cavity rim thickness effect on the efficiency becomes noticeable when the tip cavity depth is over than the optimum value. The rim thickness effect mainly appears on the tip leakage flow after the blade throat. The leaked flow after the blade throat generates a high loss region near the blade tip, especially when the rim thickness is small. The loss from the thick tip cavity rim gradually increases as the tip clearance increases. However, the rim thickness effect is most sensitive when the tip clearance is small. The loss generation mechanism due to the rim thickness is totally different to the tip cavity depth effects on the total pressure loss.Copyright

Multi Disciplinary Design Optimization and Performance Evaluation of a Single-Stage Transonic Axial Compressor

The multi-disciplinary optimization (MDO) method, which integrates aerodynamic performance and structural stability, was utilized in the development of a single-stage transonic axial compressor. Numerical simulations and compressor tests were also carried out to evaluate the aerodynamic performance and safety factor of the optimized compressor. The rotor has 60 design parameters with twelve most sensitive design variables selected for design optimization. The stator was redesigned according to the rotor outlet flow angle variation to match the stator incidence angle by −1∼0 degrees, while maintaining the stage outlet flow angle. The design goal is to maximize both the stage efficiency and the safety factor from the baseline scratch compressor design. The object function is composed of the normalized efficiency and safety factor with weight factors. Initially, an approximation model was created to search for the global optimization within given ranges of variables and considering several design constraints. The genetic algorithm was used to explore the Pareto front of the optimization to find the maximum objective function values. The final design was chosen after a second stage gradient-based optimization process to improve the accuracy of the optimization.The CFD results showed that more blade loading is burden to the hub region by increasing the incidence angle. The fore part blade loading gradually decreases along the span-wise direction. In addition, normal shock, which spreads along the hub to the blade tip, is confined in the rotor flow passage and pressure surface shock coincidence point moves to be closer to the blade leading edge, indicating an increase in the amount of blade loading. FEA analyses showed that the blade root stress has been drastically relieved, because the optimized blade has trapezoid-shaped hub design relative to the baseline design. The final design achieved efficiency gain of 3.69% and showed a higher safety factor by 2.3 times relative to the baseline model, while maintaining its stage mass flow rate and total-to-total pressure within the design constraints. The compressor performance test data showed good agreement with the optimized design and CFD results. However, there is room for improvement in the optimization process to reflect off-design performance so as to secure more stable compressor operation ranges.Copyright

Optimization Framework Using Surrogate Model for Aerodynamically Improved 3D Turbine Blade Design

In simple optimization problem, direct searching methods are most accurate and practical enough. However, for more complicated problem which contains many design variables and demands high computational costs, surrogate model methods are recommendable instead of direct searching methods. In this case, surrogate models should have reliability for not only accuracy of the optimum value but also globalness of the solution. In this paper, the Kriging method was used to construct surrogate model for finding aerodynamically improved three dimensional single stage turbine. At first, nozzle was optimized coupled with base rotor blade. And then rotor was optimized with the optimized nozzle vane in order. Kriging method is well known for its good describability of nonlinear design space. For this reason, Kriging method is appropriate for describing the turbine design space, which has complicated physical phenomena and demands many design variables for finding optimum three dimensional blade shapes. To construct airfoil shape, Prichard topology was used. The blade was divided into 3 sections and each section has 9 design variables. Considering computational cost, some design variables were picked up by using sensitivity analysis. For selecting experimental point, D-optimal method, which scatters each experimental points to have maximum dispersion, was used. Model validation was done by comparing estimated values of random points by Kriging model with evaluated values by computation. The constructed surrogate model was refined repeatedly until it reaches convergence criteria, by supplying additional experimental points. When the surrogate model satisfies the reliability condition and developed enough, finding optimum point and its validation was followed by. If any variable was located on the boundary of design space, the design space was shifted in order to avoid the boundary of the design space. This process was also repeated until finding appropriate design space. As a result, the optimized design has more complicated blade shapes than that of the baseline design but has higher aerodynamic efficiency than the baseline turbine stage.Copyright