와동강도분포를 이용한 자동차용 냉각홴의 다충실 설계

초록

In the research a multi-fidelity strategy for generating fan blade geometry in the design space and developing computational algorithm is implemented to achieve optimal design configurations. Our approach is based on combining selected high-fidelity CFD simulations with a large number of lower-fidelity computationally inexpensive models to achieve the maximum Merit Index based on both the circulation strength and the fan static efficiency near an operation point. In order to generate low-fidelity CFD models satisfying the proposed performances given as inputs, a 2D meanline design method is combined with total pressure and sweep-angle distribution functions set by the Taguchi method allowing radial streamlines, which is equivalent to a 2.5D design method. The outcome model from the low-fidelity optimization is found to have a multi-curvature blade surface resulting in the high energy transfer accompanying with a strong vortex strength with near the mid radial station, not near the tip of the blade on the contrary. The high-fidelity optimization model is searched through a reinforced machine-learning A.I. algorithm. This strategy significantly reduces the overall design cost while maintaining design quality, thus providing a practical and robust framework for optimizing axial fan performance in terms of both efficiency and noise reduction.

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