Drag Force on a Coffee Filter

Purpose: To study the relationship between air drag forces and the velocity of a falling body.

This is the prediction for the position versus time graph. Before the fall, there is a constant position because it is being held in the same place. Once it falls, the coffee filters decrease in position moving to the floor (origin). The once it hits the floor, it stays in the same place because there is nothing causing it to come back up.

These are all of our trials for the different number of coffee filters. We started with nine filters and did five trials. Then from there we took one filter away after five trials with the same number of filters. We ended with one coffee filter.

This is the position versus time graph. This is the best one out of all of our trials. We found the best fit line and our slope was -2.004. The actual would be -2.

This is the curve from out plotted points. The y-axis is the number of coffee filters, and the x-axis is the absolute values of slope averages of the five trials for each value of filters. With the power fit line, the A = k and the B = n in the drag equation we used. Drag = k|v|^n. For our value of n, we have a 2.289. The actual is supposed to be a 2. Our percent error is 14.45%.




The equation for drag moving through a fluid is:

drag = .5*density*v^2*area*drag coefficient 

This is equation is similar to ours where velocity is squared. However, in the fluid equation are is used not surface area. To compare the drag fluid equation to the normal drag equation (drag = 1/4 * Av^2) you can see many more similarities.  

Conclusion: We learned that air slows down the fall of objects. Also, the less mass there is the more the air acts upon it. Our error could be from not dropping from the exact same height each time. Not dropping it at the same height does affect the experiment. The higher we drop the coffee filter, the longer it takes to reach the ground. If we had dropped the filters from the exact same height each time, then we would have better numbers. Next time, we can make sure we drop the filters at the exact same height and even do more trials to have more data.