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十分発達したNS方程式の離散化
図0013 傾斜平板に沿う流れ
選択肢
(1) | \(\Large \frac{u_{i+1} - 2 u_i +u_{i-1}}{(\Delta y)^2} \normalsize\) | (2) | \(- \Large \frac{g (\Delta y)^2}{\nu } \normalsize \cos{\theta} \) | (3) | \(\Large \frac{h}{n} \normalsize\) |
(4) | \(後退差分\) | (5) | \(\Large \frac{u_{n+1}-u_{n}}{\Delta y} \normalsize = 0\) | (6) | \(\Large \frac{u_{n}-u_{n-1}}{\Delta y} \normalsize = 0\) |
(7) | \(u_{3} - 2 u_2 +u_{1}\) | (8) | \(- \Large \frac{g }{\nu (\Delta y)^2} \normalsize \cos{\theta} \) | (9) | \(u_{4} - 2 u_3 +u_{2}\) |
(10) | \(\Large \frac{u_{n+1}-u_{n-1}}{2 \Delta y} \normalsize = 0\) | (11) | \(u = 0\) | (12) | \(y_0 + n \times \Delta y\) |
(13) | \(\Large \frac{d u}{d y} \normalsize = 0\) | (14) | \(中心差分\) | (15) | \(前進差分\) |
(16) | \(\Large \frac{h}{2 n} \normalsize\) | (17) | \(u_{2} - 2 u_1 +u_{0}\) | (18) | \(y_0 + i \times \Delta y\) |