MCQ Bank
The Euler number (Eu) is equal to _____.
- A) Eu = \frac{\rho V^2}{P_0-P_{\infty}}
- B) Eu = \frac{\rho (P_0-P_{\infty})}{V^2}
- C) Eu = \frac{P_0-P_{\infty}}{ V^2}
- D) Eu = \frac{P_0-P_{\infty}}{\rho V^2}
The Reynolds number (Re) is equal to _____.
- A) Re=\frac{\mu} {\rho VL}
- B) Re=\frac { VL}{\mu}
- C) Re=\frac { VL}{\rho\mu}
- D) Re=\frac {\rho VL}{\mu}
Let \(n\) be total parameters and\(j\) equal to number of primary dimensions, then expected number of \(\Pi\)’s (\(k\)) equal to_____.
- A) \(k=n-j\)
- B) \(k=2n-j\)
- C) \(k=j-n\)
- D) \(k=n+j\)
If \(g=1.63 m/s^2, w_0 = 1.83m/s\ and\ z_0 =2m\) , then Froude number is equal to ____.
- A) \(Fr=1.01\)
- B) \(Fr=1.05\)
- C) \(Fr=1\)
- D) \(Fr=1.03\)
\(m L^2 t^{-2}\) is dimension of ____.
- A) force
- B) energy
- C) linear momentum
- D) moment
The Euler number (\(Eu\)) is equal to _____.
- A) \(Eu = \frac{\rho V^2}{P_0-P_{\infty}}\)
- B) \(Eu = \frac{P_0-P_{\infty}}{ V^2}\)
- C) \(Eu = \frac{\rho (P_0-P_{\infty})}{V^2}\)
- D) \(Eu = \frac{P_0-P_{\infty}}{\rho V^2}\)
\(m L^{-1} t^{-2}\) is dimension of ____.
- A) force
- B) momentum
- C) pressure
- D) energy
The Strouhal number (\(St \)) is equal to _____.
- A) \(St=\frac{fL}{V}\)
- B) \(St=\frac{V}{fL}\)
- C) \(St=\frac{fL}{Vg}\)
- D) \(St=\frac{Vg}{fL}\)
The Froude number (\(Fr\)) is equal to _____.
- A) \(Fr=\frac {\sqrt{gL}}{ V}\)
- B) \(Fr=\frac {{gL}}{ V}\)
- C) \(Fr=\frac { V}{gL}\)
- D) \(Fr=\frac { V}{\sqrt{gL}}\)
\(m L^2 t^{-1}\) is dimension of ____.
- A) moment
- B) force
- C) angular momentum
- D) linear momentum
If \(t=3.01s, w_0=1.83m/s\ and\ z_0=2m\), then dimensionless time (\(t^*\)) is equal to________.
- A) \(t^*=2.79\)
- B) \(t^*=2.75\)
- C) \(t^*=2.7\)
- D) \(t^*=2.71\)
The Reynolds number (\(Re\)) is equal to _____.
- A) \(Re=\frac{\mu} {\rho VL}\)
- B) \(Re=\frac { VL}{\rho\mu}\)
- C) \(Re=\frac { VL}{\mu}\)
- D) \(Re=\frac {\rho VL}{\mu}\)
The Froude number (Fr) is equal to _____.
- A) Fr=\frac { V}{gL}
- B) Fr=\frac {\sqrt{gL}}{ V}
- C) Fr=\frac { V}{\sqrt{gL}}
- D) Fr=\frac {{gL}}{ V}
Which of the following discretized factor is utilized to check the stability of finite difference scheme in Fourier transform,
- A) data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJIAAAAsCAIAAAAB2XbcAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOASURBVHhe7ZhhseowEIWxgAYs4AEJaMACDnCAAxSgAAMYwAEe+r7h7O3stJCmj/TeBvb7wXRDmiZ7sieFRRNUSMhWJSFblYRsVRKyVUnIViUhW5WEbFUSslVJyFYlIVuVhGxV8qWyHQ6H5XJpQR6bzWbx4Hw+E55OJ4W3200dfpMvlQ0NwII87vc7Iu33e4ubhuux2pciTDKX6/WKbJfLxeKm2e122+3Wgt/l62Qj7w9vWyCDNeVxPB65y4IHq9UKs+WCT40pUXFRqpBvKVBCirL9qhQfJRtpIoPr9VpJJHdUQ//sUZYtyIahvK8yLINIDHYAz2JY6UQVckEL5x+a8UlL2SPwc2Qjd2xwNFMqSRxZJrNKpYdUjj3YQMJY8CgpBkcMadNXBamYDDVqcVE+RDbtbtLkRSKztFjgoJFEW5CHry0h6xNP/RaNmZIFpfkQ2XAw0pdzfqArPfUSn49e9/2eQHsequ3S3wQIqVov640tKdm0X8DvJu2yiWbzf6gUMn2vNTeLHSwTZ2O9dMBv/aqlgQU/2qMl17qFFjLDJz2lGdd6Z2EzqWdBXsqGKfMwHsn8vKezy5iNBYVQUtIkKkk7KTM1dH46f9UTKyXdgBLtPqDxMYUFCqnFa49IdGZMzZD21qt1YztOQQZMUrL5jPjZzwTSxKyUqUHo3J8/qWcEvztR4q9+k+UwIJv2S2spfRVBjYlqYBCyYMEE8HTEsODHM0Vnp6uk+lNFIc0Q7akk7iL0Jjk3BmRjAd5Snh5s/d+hHRgELCiNNk1nfJkYM29LUN1oeeqlfCVQi6FYUWbt/hUDsrESbykse4q6IVPKWoJX1ZyQzZteAo2Q2XkmpGTrrAf9kK3j+KiYWLNGgEkNh/G9SYJc4ZXSHfqyMVtGsGCWDMumBbAq7AWREA+T1E8W2nWQJBLEjXSwYBokkv6PYFYy7VEPZTu2r38sk3DOBxsMrE32xTKwHUIliMZ2VZJW10/hlo6DTQGbg0lqqlzDqPdAlqPXUeDGzuE9Q96tg0FVSIfqNSjIu7KlVcF22L+q1KAg78qGKhwGghB38m8HeqObv+dUR4FqQ5i24JCN00XXQLsPg1K8K1sH3jNRzoIX/yQF71NSNszQi4RtUoiZP56CURSuNqFfBdijDrygOJPIFkxNyFYlIVuVhGxVErJVSchWJSFblYRsFdI0/wDVDvrE5xxAHgAAAABJRU5ErkJggg==.
- B) data:image/png;base64,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.
- C) data:image/png;base64,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.
- D) data:image/png;base64,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.
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- B) data:image/png;base64,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 .
- C) data:image/png;base64,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 .
- D) data:image/png;base64,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 .
The Fourier method for stability analysis of PDEs is mainly used to:
- A) Solve the PDE analytically
- B) Optimize the grid spacing
- C) Approximate boundary conditions
- D) Determine whether numerical errors grow, decay, or remain bounded over time
In Jacobi’s method for a 2D Laplace equation on a uniform grid, the value at an interior point (i, j) is updated using:
- A) The average of its four nearest neighbors
- B) The average of its eight surrounding neighbors
- C) Its value from the previous step minus the residual
- D) Only the left and right neighbors
Which of the following mathematical expression represents the standard five-point formula for 2D Laplace partial differential equation for the uniform step size in both x and y direction?
- A) data:image/png;base64,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.
- B) data:image/png;base64,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.
- C) data:image/png;base64,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.
- D) data:image/png;base64,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.
Compared to Jacobi’s method, Gauss–Seidel’s method generally:
- A) Does not require initial guesses
- B) Converges more slowly
- C) Cannot be used for PDEs
- D) Converges faster because updated values are used immediately
Which type of PDE is typically used to model steady-state heat conduction in a solid?
- A) Elliptic PDE
- B) Hyperbolic PDE
- C) Parabolic PDE
- D) Ordinary differential equation