• 제목/요약/키워드: 래디얼배이스 신경회로망 기법

검색결과 5건 처리시간 0.023초

신경회로망기법을 사용한 엇갈린 딤플 유로의 최적설계 (Design Optimization of a Staggered Dimpled Channel Using Neural Network Techniques)

  • 신동윤;김광용
    • 한국유체기계학회 논문집
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    • 제10권3호
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    • pp.39-46
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    • 2007
  • 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.

신경회로망기법을 사용한 원통형 막냉각 홀의 최적설계 (Design Optimization of a Cylindrical Film-Cooling Hole Using Neural Network Techniques)

  • 이기돈;김광용
    • 대한기계학회논문집B
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    • 제32권12호
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    • pp.954-962
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    • 2008
  • This study presents a numerical procedure to optimize the shape of cylindrical cooling hole to enhance film-cooling effectiveness. 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 turbulent model. The hole length-to-diameter ratio and injection angle are chosen as design variables and film-cooling effectiveness is considered as objective function which is to be maximized. Twelve training points are obtained by Latin Hypercube Sampling for two design variables. In the sensitivity analysis, it is found that the objective function is more sensitive to the injection angle of hole than the hole length-to diameter ratio. Optimum shape gives considerable increase in film-cooling effectiveness.

열전달성능 향상을 위한 엇갈린 딤플 유로의 최적설계 (DESIGN OPTIMIZATION OF A STAGGERED DIMPLED CHANNEL TO ENHANCE TURBULENT HEAT TRANSFER)

  • 신동윤;김광용
    • 한국전산유체공학회:학술대회논문집
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    • 한국전산유체공학회 2007년도 춘계 학술대회논문집
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    • pp.159-162
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    • 2007
  • This study presents a numerical procedure to optimize the shape of a staggered dimpled surface to enhance the turbulent heat transfer in a rectangular channel. A optimization technique based on neural network is used with Reynolds-averaged Navier-Stakes analysis of the fluid flow and heat transfer with Shear Stress Transport turbulence model. The dimple depth-to-dimple print diameter ratio, channel height-to-dimple print diameter ratio, and dimple print diameter-to-pitch ratio are chosen as design variables. The objective function is defined as a linear combination of terms related to heat transfer and friction loss with a weighting factor. Latin Hypercube Sampling is used to determine the training points as a mean of the Design of Experiment. Optimal values of the design variables were obtained in a range of the weighting factor.

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익형의 형상최적화를 통한 고효율 축류송풍기 설계 (High-Efficiency Design of Axial Flow Fan through Shape Optimization of Airfoil)

  • 이기상;김광용;최재호
    • 한국유체기계학회 논문집
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    • 제11권2호
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    • pp.46-54
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    • 2008
  • This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.

신경회로망기법을 사용한 타원형 딤플유로의 냉각성능 최적화 (Optimization of a Cooling Channel with Staggered Elliptical Dimples Using Neural Network Techniques)

  • 김현민;문미애;김광용
    • 한국유체기계학회 논문집
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    • 제13권6호
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    • pp.42-50
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    • 2010
  • The present analysis deals with a numerical procedure for optimizing the shape of elliptical dimples in a cooling channel. The three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis is employed in conjunction with the SST model for predictions of the turbulent flow and the heat transfer. Three non-dimensional geometric design variables, such as the ellipse dimple diameter ratio, ratio of the dimple depth to the average diameter, and ratio of the distance between dimples to the pitch are considered in the optimization. Twenty-one experimental points within design space are selected by Latin Hypercube Sampling. Each objective function values at these points are evaluated by RANS analysis and producing optimal point using surrogate model. The linear combination of heat transfer coefficient and friction loss related terms with a weighting factor is defined as the objective function. The results show that the optimized elliptical dimple shape improves considerably the heat transfer performance than the circular dimple shape.