19.09.2019 Views

fluid_mechanics

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

358 Chapter 7 ■ Dimensional Analysis, Similitude, and Modeling<br />

COMMENT This same model could also be used to predict<br />

the drag per unit length, d / 1lbft or Nm2, on the prototype, since<br />

the drag would depend on the same variables as those used for the<br />

frequency. Thus, the similarity requirements would be the same<br />

and with these requirements satisfied it follows that the drag per<br />

unit length expressed in dimensionless form, such as d /DrV 2 ,<br />

would be equal in model and prototype. The measured drag per<br />

unit length on the model could then be related to the corresponding<br />

drag per unit length on the prototype through the relationship<br />

d / a D b a r b a V 2<br />

b d<br />

D m r m V /m<br />

m<br />

7.8.2 Model Scales<br />

It is clear from the preceding section that the ratio of like quantities for the model and prototype<br />

naturally arises from the similarity requirements. For example, if in a given problem there are two<br />

length variables / 1 and / 2 , the resulting similarity requirement based on a pi term obtained from<br />

these two variables is<br />

so that<br />

/ 1<br />

/ 2<br />

/ 1m<br />

/ 2m<br />

The ratio of a<br />

model variable to<br />

the corresponding<br />

prototype variable<br />

is called the scale<br />

for that variable.<br />

/ 1m<br />

/ 2m<br />

/ 1 / 2<br />

We define the ratio / 1m/ 1 or / 2m/ 2 as the length scale. For true models there will be only one length<br />

scale, and all lengths are fixed in accordance with this scale. There are, however, other scales such<br />

as the velocity scale, V mV, density scale, r mr, viscosity scale, m mm, and so on. In fact, we can define<br />

a scale for each of the variables in the problem. Thus, it is actually meaningless to talk about<br />

a “scale” of a model without specifying which scale.<br />

We will designate the length scale as l / , and other scales as l V , l r , l m , and so on, where the<br />

subscript indicates the particular scale. Also, we will take the ratio of the model value to the prototype<br />

value as the scale 1rather than the inverse2. Length scales are often specified, for example,<br />

1<br />

as 1 : 10 or as a 10 scale model. The meaning of this specification is that the model is one-tenth<br />

the size of the prototype, and the tacit assumption is that all relevant lengths are scaled accordingly<br />

so the model is geometrically similar to the prototype.<br />

F l u i d s i n t h e N e w s<br />

“Galloping Gertie” One of the most dramatic bridge collapses<br />

occurred in 1940 when the Tacoma Narrows bridge, located near<br />

Tacoma, Washington, failed due to aerodynamic instability. The<br />

bridge had been nicknamed “Galloping Gertie” due to its tendency<br />

to sway and move in high winds. On the fateful day of the collapse<br />

the wind speed was 65 kmhr. This particular combination of a<br />

high wind and the aeroelastic properties of the bridge created large<br />

oscillations leading to its failure. The bridge was replaced in 1950,<br />

and plans are underway to add a second bridge parallel to the ex-<br />

isting structure. To determine possible wind interference effects<br />

due to two bridges in close proximity, wind tunnel tests were run<br />

in a 9 m 9 m wind tunnel operated by the National Research<br />

Council of Canada. Models of the two side-by-side bridges, each<br />

having a length scale of 1 : 211, were tested under various wind<br />

conditions. Since the failure of the original Tacoma Narrows<br />

bridge, it is now common practice to use wind tunnel model studies<br />

during the design process to evaluate any bridge that is to be<br />

subjected to wind-induced vibrations. (See Problem 7.72.)<br />

7.8.3 Practical Aspects of Using Models<br />

Validation of Model Design. Most model studies involve simplifying assumptions with<br />

regard to the variables to be considered. Although the number of assumptions is frequently less<br />

stringent than that required for mathematical models, they nevertheless introduce some uncertainty<br />

in the model design. It is, therefore, desirable to check the design experimentally whenever<br />

possible. In some situations the purpose of the model is to predict the effects of certain proposed<br />

changes in a given prototype, and in this instance some actual prototype data may be available.<br />

The model can be designed, constructed, and tested, and the model prediction can be compared

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!