# 102 Unit 2/3 Lab: Testing a Terminal Speed Hypothesis

## Terminal Speed

### Materials:

- lab sheet and writing utensil
- calculator
- 10 Coffee filters
- step ladder allowing you (or a partner) to reach 2
**m** - ruler with
**cm**units - scale with at least 0.1 gram
- spreadsheet and graphing software
- for distance learners, access to online forums, videos, and help features for the spreadsheet software will likely be necessary
- one of the following equipment sets:
- motion sensor + computer with control and analysis software
- video motion analysis app (example)
- camera (slow motion mode preferred) + stopwatch with 0.01
**s**precision

**Course Outcome 5, Unit Outcome 2-1**

### Observation

We observe that when a body falls through the air it eventually reaches a maximum , known as , which is roughly 200 **mph**.

### Question

This phenomenon raises the question: What determines the value of the ?

### Search Existing Knowledge

Find an answer for what determines the value of the . Write the answer below and also list your source.

### Hypothesis

Our search of existing knowledge told us that one factor affecting was the of the object.

Provide a on how the terminal speed depends on the mass of an object. That means to state if you think the terminal speed will increase or decrease when mass increases. Explain your reasoning.

### Test

To test your hypotheses, without jumping out of airplanes, we will measure the of coffee filters with varying . The terminal speed for coffee filters is much slower than for bodies and they will typically reach terminal speed in less than 2 meters of drop distance. These properties will make our experiment reasonable to perform in the lab. Your hypothesis was about an object’s terminal speed and mass in general, not about bodies specifically, so a coffee filter experiment will still test your hypothesis.

Measure the of the coffee filter and record here:___________

Our method will be to drop coffee filters from a height of at least 2 **m ** and measure the . You will need a step ladder and a partner to make the measurements. You can measure the terminal speed using photogates or acoustic or laser based motion sensor if you have access to those in your lab. If not, you can measure the terminal speed by using a video motion analysis app, or by simply filming the last 0.1 **m **(10** cm) **of the fall while holding a ruler and a running stopwatch to be visible in the video frame.

If using the motion sensor, be sure to only use the section of the speed data after the speed has become and before impact. Your instructor will help you find this section of data. Record your terminal speed here:______________

If using the filming method, be sure to film straight on to the ruler, which should be standing up straight on the floor. Read off the time off the stopwatch in the video when the filter passes the 10 **cm** mark and again when it hits the floor. Subtract the first time from the second to find the difference between these times. Divide 0.10 **m **by the time difference to get the terminal speed. Record your terminal speed here:______________

Repeat this experiment for two nested (one inside another) coffee filters. Nesting the coffee filters increases the , but doesn’t change the shape of the filters, allowing us to change only one at at time. Record your for two filters in the chart. Also measure the of the two filters and record in the chart as well.

Repeat the experiment until you have measured terminal speed and mass for at least 5 nested coffee filters. Record the number of filters and for each in the table below:

Number of Filters | Terminal speed (m/s) | Mass (g) |

1 | ||

2 | ||

3 | ||

4 | ||

5 | ||

6 | ||

7 | ||

8 | ||

9 |

### Analyze

Enter your data into a spreadsheet and create an x-y graph of terminal speed vs. mass. Mass should be on the horizontal (x-axis) because mass is the (what you are purposefully changing). Speed should be on the vertical axis (y-axis), because speed is the (what is changing in response to the independent variable).

Be sure to give your graph a title and label the axes with the names and the units of measure.

Check with your instructor to find out if they want you to enter your group’s data into the class-data spreadsheet.

### Conclusion

Was your supported by the data? Explain.

Be sure to save your spreadsheet and graph. We may use them again during this course.

### Hypothesis Testing Including Uncertainty*

In order to really answer the question about whether or not the experimental results support the we need to think about . (Unit outcome 3-4)

Let’s do a little experiment to determine how affects the of your results. Repeat the final filter set measurement 6 more times and record the results, including the first value you found above, in a chart:

Use your spreadsheet software (or some other method) to take the average and the of the seven values. Record both below:

The value will serve as an estimate of the in our experiment. A new measurement should be within the standard deviation of the average value 68 **%** of the time. We will use the precision provided by the standard deviation as our estimate of the in our final measurement. Ideally we would base our average and standard deviation on more than seven values, but we will use only seven in this learning situation for the sake of time.

Add error bars to the terminal speed data in your graph, setting the size equal to the you calculated.

Considering the error bars, does the data support your ? Explain your reasoning.

So far we have ignored systematic error. can be difficult to recognize and even more difficult to quantify. We must always be on the look-out for sources of systematic error.

Can you provide a possible source of systematic error in your experiment? Explain. (Unit outcome 3-2)

Can you estimate how large the error might be (provide an upper bound) Explain. (Unit outcome 3-3)

### Modeling

As a class we will fit a curve to our data of terminal speed vs. mass of filters and use the equation of that curve to predict the terminal speed for a higher number of filters. (Outcome 2-2)

What type of model is this? Explain.

Write the fit equation we found here:

Record the number of filters and mass of the filter set you will predict/test:

Show your work in calculating the predicted terminal speed for additional filters.

Drop your new filter set and record the terminal velocity you measure:

Did the prediction agree with the experimental test within your uncertainty? Does your result add validity to your model? Explain.

refers to the closeness of two or more measurements to each other

distance traveled per unit time

the speed at which restive forces such as friction and drag balance driving forces and speed stops increasing, e.g. the gravitational force on a falling object is balanced by air resistance

a measurement of the amount of matter in an object made by determining its resistance to changes in motion (inertial mass) or the force of gravity applied to it by another known mass from a known distance (gravitational mass). The gravitational mass and an inertial mass appear equal.

describing what happens, but not how much happens

a proposed explanation made on the basis evidence that can be supported or refuted by the result of experimentation

not changing, having the same value within a specified interval of time, space, or other physical variable

a factor, property, or condition that can change during an experiment

the factor, property, or condition that is purposefully changed within an experiment

a factor, condition, or property that changes in response to purposeful changes in the independent variable

Amount by which a measured, calculated, or approximated value could be different from the actual value

random errors are fluctuations (in both directions) in the measured data due to the precision limitations of the measurement device. Random errors usually result from the experimenter's inability to take the same measurement in exactly the same way to get exact the same number

is a measure that is used to quantify the amount of variation or dispersion of a set of data values

an error having a nonzero mean (average), so that its effect is not reduced when many observations are averaged. Usually occurring because

there is something wrong with the instrument or how it is used.