• Wind and Structures
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• v.6 no.5
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• pp.347-356
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• 2003

Experimental studies on drag reduction of a circular cylinder of diameter D were conducted in the subcritical flow regime at Reynolds numbers in the range $4{\times}10^4{\leq}Re{\leq}10^5$. To shield the cylinder rear surface from the pressure deficit of the unsteady vortex generation in the near wake, two shield plates were attached downstream of the separation points to form a cavity at the base region. The chord of the shield plates, L, ranged from 0.22 to 1.52 D and the cavity width, G, was in the range from 0 to 0.96 D. It is concluded that significant drag reductions from that of a plain cylinder may be achieved by proper sizing of the shield plates and the base cavity. The study shows that using a pair of shield plates at G/D of 0.86 and angular position ${\theta}$ of ${\pm}121^{\circ}$ results in a configuration with percentage drag reduction of 40% for L/D of 0.5, and 55% for L/D of 1.0.

• Wind and Structures
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• v.11 no.5
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• pp.377-389
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• 2008

An experimental investigation has been conducted to determine the effectiveness of base shield plates in reducing the drag of a rough circular cylinder in a cross flow at Reynolds numbers in the range $3{\times}10^4{\leq}Re{\leq}10.5{\times}10^4$. Three model configurations were investigated and compared: a plane cylinder (PC), a cylinder with a splitter plate (MC1) and a cylinder fitted with base shield plates (MC2). Each configuration was studied in the sub and supercritical flow regimes. The chord of the plates, L, ranged from 0.22 to 1.50D and the cavity width, G, between the plates was in the range from 0 to 0.93D. It is recognized that base shield plates can be employed more effectively than splitter plates to reduce the aerodynamic drag of circular cylinders in both the sub- and supercritical flow regimes. For subcritical flow regime, one can get 53% and 24% drag reductions for the MC2 and MC1 models with L/D=1.0, respectively, compared with the PC model. For supercritical flow regime however, the corresponding drag reductions are 38% and 7%.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.