Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and the fluid's viscosity. These variables are grouped into dimensionless terms to form a relationship, but experimental data is needed to determine the exact nature of this relationship.
Researchers measure drag force while varying velocity to correlate the dimensionless groups, such as the Reynolds number. This correlation allows the relationship between drag and velocity to be expressed as a constant. Multiple tests verify this constant, confirming the dependency of drag on velocity.
If drag depends only on diameter, velocity, and viscosity, the relationship should be universal for any spherical particle in any fluid under consistent conditions. Multiple tests with different particles and fluids verify this universality, reducing the need for further experiments, as the derived constant remains valid under the specified conditions.
From Chapter 20:
Now Playing
Dimensional Analysis, Similitude, and Modeling
6 Views
Dimensional Analysis, Similitude, and Modeling
21 Views
Dimensional Analysis, Similitude, and Modeling
20 Views
Dimensional Analysis, Similitude, and Modeling
10 Views
Dimensional Analysis, Similitude, and Modeling
15 Views
Dimensional Analysis, Similitude, and Modeling
12 Views
Dimensional Analysis, Similitude, and Modeling
6 Views
Dimensional Analysis, Similitude, and Modeling
14 Views
Copyright © 2025 MyJoVE Corporation. All rights reserved