Linearising Graph of Mass and Frequency

In summary, the conversation discusses an experiment about the loading of tuning forks, where one prong was attached with blue tac and then weighed. The different frequencies for each mass were measured and plotted on a graph, resulting in an exponential decay curve. The equations of simple harmonic motion were used to try and linearize the curve, but the results were still a curve. The conversation also brings up the idea of only attaching weight to one prong and how that may affect the results.
  • #1
Tikiboom1
2
0

Homework Statement


I did an experiment about the loading of tuning forks. I added blue tac to one of the prongs on a tuning fork and then weighed it. I then measured the different frequencies for each mass and plotted a graph which ended up looking like a exponential decay graph. The results are shown below :

Mass(kg): Frequency(Hz):
0.05179 307.83
0.05219 305
0.05269 302.83
0.05335 301.17
0.05378 299.83
0.05572 299.17

Homework Equations


ω = 2πf
ω = √(k/m)

The Attempt at a Solution


When a tuning fork oscillates it is under SHM as when the prongs oscillate there is a restoring force towards the equilibrium. Using the equations of SHM, I did :
2πf = √(k/m)
∴ f = √(k/m) x 1/2π.

As, k and π are constants, we can rearrange the equation so that f = √k/2π x √1/m.
So if I plot f on the y-axis, and √1/m on the x-axis, I should get a straight line with √k/2π, being the gradient. However, I still get a exponential decay looking curve when I try to do that! I've then gone on to try the usual logging both sides and then plotting a graph of lg(frequency) and lg(mass) but I still get a curve which is extremely strange? I tried plotting frequency to (1/mass)². But still I get a curve? Could anybody please point out where I have gone wrong or done something careless? I can't seem to spot the problem and can't linearise the curve!

Thank you so much in advance, your help and advice is greatly appreciated!
 
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  • #2
Hello Tikiboom, :welcome:

For your m you seem to take the mass of the entire fork. But not the entire fork vibrates ! So you want to work around your formula in such a way that you can plot your observation versus m. That way you can estimate m0, the mass that doen's vibrate.

One other remark: why attach a weight to one prong only ? What frequency do you expect for the other prong ? How does that influence your measurement ?
 

1. What is the purpose of linearising a graph of mass and frequency?

The purpose of linearising a graph of mass and frequency is to create a linear relationship between the two variables, making it easier to analyze and interpret the data. This allows for more accurate predictions and conclusions to be drawn from the graph.

2. How do you linearise a graph of mass and frequency?

To linearise a graph of mass and frequency, you need to plot the data on a graph and then determine the relationship between the variables. Once the relationship is determined, you can apply a mathematical transformation, such as taking the logarithm or square root, to create a linear relationship between the variables.

3. What are the benefits of linearising a graph of mass and frequency?

Linearising a graph of mass and frequency can make the data easier to interpret and analyze, as well as allow for more accurate predictions and conclusions to be drawn. It can also help to identify any outliers or anomalies in the data.

4. What types of graphs can be linearised for mass and frequency?

Most commonly, line graphs, scatter plots, and histograms can be linearised for mass and frequency. However, depending on the data and the relationship between the variables, other types of graphs may also be able to be linearised.

5. Can linearising a graph of mass and frequency change the interpretation of the data?

Yes, linearising a graph of mass and frequency can change the interpretation of the data. This is because linearising can change the relationship between the variables and reveal patterns or trends that may not have been apparent before. It is important to consider the original data and the transformation used when interpreting the results.

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