한국유체기계학회 논문집 (The KSFM Journal of Fluid Machinery)

한국유체기계학회 (Korean Society for Fluid machinery)
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신경회로망기법을 사용한 엇갈린 딤플 유로의 최적설계

Design Optimization of a Staggered Dimpled Channel Using Neural Network Techniques

  • 신동윤 (인하대학교 대학원 기계공학과) ;
  • 김광용 (인하대학교 기계공학부)
  • 발행 : 2007.06.30
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초록

This study presents a numerical procedure to optimize the shape of staggered dimple surface to enhance turbulent heat transfer in a rectangular channel. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter (d/D), channel height-to-dimple print diameter ratio (H/D), and dimple print diameter-to-pitch ratio (D/S) are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. Latin Hypercube Sampling (LHS) is used to determine the training points as a mean of the design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

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참고문헌

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