The unsteady state and isothermal two dimensional numerical computations were carried out using Ansys Fluent-18 between the Reynolds number ranges 10 to 50. The blockage ratios (Domain height to the circular cylinder diameter) range 1.54–112. The flow characteristics such as drag coefficients and length of recirculation are optimized and correlated as a function of various Reynolds numbers at different blockage ratios. Gradual decrease in blockage ratio which means the increase in blockage effect postponed the flow separation, transition and reduces the length of recirculation and also makes the flow steady. In this study optimum flow characteristics exist at maximum blockage ratio, i.e. with minimum blockage effect and maximum Reynolds number. The artificial neural networks model proved to predict values of the total drag coefficient (R² = 0.979) and length of recirculation (R² = 0.992) closer to simulated data at 95% (α = 0.05) confident interval.

使用Ansys Fluent-18在雷诺数范围10到50之间进行了非稳态和等温二维数值计算。阻塞比（域高度与圆柱直径）在1.54–112之间。诸如阻力系数和再循环长度之类的流动特性经过优化，并根据在不同堵塞率下的各种雷诺数进行了关联。堵塞率逐渐降低，这意味着堵塞效应的增加会延迟分流，过渡并缩短回流时间，并使流量稳定。在这项研究中，最佳的流动特性存在于最大堵塞比处，即具有最小的堵塞效应和最大的雷诺数。事实证明，人工神经网络模型可以预测总阻力系数（R ²  = 0.979），循环长度（R ²  = 0.992）更接近于模拟数据， 置信区间为95％（α = 0.05）。