Lab 8: Solar Cell Experiment

In this experiment, we were asked to use a solar cell, a light, a ruler, and some colored gels to determine different aspects of light intensity. Specifically, we were asked to discover the relationship between the distance between the cell and a light source and light intensity, and also the color of light and how that effects intensity.

 

My partner and I started looking at the distance between our flashlight and our solar cell. Before we really began recording data, we tested the solar cell in the dark to determine its accuracy. We attempted to incorporate this data into our graph, but the data point made the trendline look as though it didn’t match at all so we removed it from our graph. We did get a small reading, but it was negligible. It was important for us to do this though, because after this step we were aware that our cell was not perfectly accurate. Luckily, it was still accurate enough that the experiment worked out for us.

 

To explore the relationship between distance between the cell and a light source and intensity, we took light intensity measurements at five different distances: 0cm, 3cm, 5cm, 7cm, and 9cm. Our measurements were recorded by the computer in intervals of ten seconds, and so we took the average of those recordings to get out data points. As is apparent from the graph, the farther away the light source is from the solar cell, the less intensity the cell records.

Graph-Distance

 

For the second half of the experiment, we used four different colored gels to look at how color has an impact on light intensity. We had red, blue, orange, and light pink, and of course white light without a gel. We took our measurements by holding the light source directly next to the solar cell with the gel in between, so distance would not be a factor in this experiment. Our findings were about what we expected: the darker the gel, the less intensity the solar cell will record. It was no surprise that white light gave the highest intensity, and the light pink one was not far behind. It also makes sense that orange and red came next, both because the orange was less opaque than the red and the blue, but because of the wavelengths of the colors. Red light has a much longer wavelength than blue light. The only thing that really surprised me about this experiment was that there wasn’t a more dramatic difference between the intensities of red and blue light, but I suppose that has something to do with the opacity of the gels. For a more perfect experiment, if I really wanted to look at how red light and blue light compare in terms of intensity, I would pick gels that all have the exact same amount of opacity.

Graph-color

Lab 2: Pulleys

My partner, Jennifer Straka, and I explored the relationships between force, mass, and acceleration in this lab about pulleys. The Lego Mindstorm car pulled in a string that went through a pulley and had a weight on the end of it, and when it finished moving, was able to measure a variety of factors within that action, including acceleration, battery discharge, and the speed at which the rope was pulled in. We were specifically paying attention to the power level of the motor, which was our force; the mass of our weight; and the acceleration.

 

In the first trial, we deliberately increased the mass and kept the force constant, expecting the acceleration to change accordingly. We did five different runs with five different masses. Luckily for us, our data was accurate enough that we could plot a trendline that confirmed our ideas about acceleration and mass being proportional.

Screen Shot 2015-10-02 at 12.40.16 PM

As we know, F=ma. Because force was constant, when we increased mass, acceleration decreased. The relationship between mass and acceleration is inversely proportional, which this graph shows to be true.

In the second half of our experiment, we kept a constant mass and incrementally increased the force over five different levels. Again keeping in mind that F=ma, we expected that if mass stays the same, increasing force will also increase the acceleration. We were happy to discover that our findings did confirm this as true, as evidenced by the proportional trendline in our data.

Screen Shot 2015-10-02 at 12.40.36 PM

Being told that force is the product of mass and acceleration is one thing, but to actually discover and prove that for ourselves is another thing completely. It’s much easier to see the evidence and understand it than to memorize a formula and assume it as true. This lab allowed us to comprehend exactly how force, mass, and acceleration relate to one another in a hands-on way.

Lego Mindstorm 1: Measuring Distance and Velocity

In this experiment, groups were asked to use the Lego cars to understand how to measure distance and velocity and also that through human error, these measurements may not always be perfectly accurate. The experiment was designed in a way such that the computer gave its measurement of distance traveled, which students could compare to the measurements they had taken themselves.

First, my partner and I measured the diameter of the wheel, which was necessary for us to find the circumference (pi*D) so that using the number of wheel turns, the computer could determine how far it traveled. We measured the wheel diameter at .055 m, so the circumference was .1728 m.

Then, we were asked to experiment with the power settings to see how far the car would travel. We experimented with three power settings, 75, 55, and 65, and performed three trials for each power setting to ensure that our data would be as accurate as possible. We kept our time at 1 second, both because our ruler was not very long and because it would make measuring velocity easy (velocity is measured in m/s, and if the distance is being measured over the time of 1 second, then the value for distance and velocity is the same).

 

This is the data we gathered on our very first trial:

Setting 1 (power=75) (time=1):

Trial 1, VI:  

    Rotation: 497 degrees

    Wheel Turns: 1.38056

    Distance: .238836 m

    Velocity: .238836 m

Trial 1, Student

    Distance .26 m

    Margin of Error: 8%

 

Through this data, some connections are clear. For example, the wheels rotated 497 degrees, which is just under 1.5 complete turns. The data shows that the wheels actually turned roughly 1.38 times. The wheel’s circumference was about .17 m, so it makes sense that the distance (according to the computer) was around 1.38 times that. All of this computer-generated data correlates. We were also asked to measure how far we thought the car traveled using a ruler, and each time we were slightly off. We used the difference between our measurement of distance and the computer’s measurement of distance to find the margin of error, which in this case was 8%. In some other cases the number was smaller (.614% which we were very proud of) but it was also larger in some trials (12.47% which we were less proud of). Our measurements were far from exact; we used a pen to try and align the ruler with the backs of the wheels, and our eyesight is not nearly as good at measuring as a computer is. We did constantly try to improve our exactness, but as is visible in the data below, this is a difficult thing to improve within such a short window of time. This experiment helped us to understand through hands-on learning how distance, wheel circumference, number of wheel turns, velocity, and timing were all related. Below you will find all of our data including the data I posted above:

 

Wheel Diameter= .055 m    Circumference= .1728 m

 

Setting 1 (power=75) (time=1):

Trial 1, VI:  

    Rotation: 497 degrees

    Wheel Turns: 1.38056

    Distance: .238836 m

    Velocity: .238836 m

Trial 1, Student

    Distance .26 m

    Margin of Error: 8%

 

Trial 2, VI:

    Rotation: 490 degrees

    Wheel Turns: 1.36111

    Distance: .2352 m

    Velocity: .2352 m/s

Trial 2, Student:

    Distance: .26m

        Margin of Error: .614%

 

    Trial 3, VI:

        Rotation: 488 degrees

        Wheel Turns: 1.35556

        Distance: .23424 m

        Velocity: .23424 m/s

    Trial 3, Student:

        Distance: .257 m

        Margin of Error: 9.266 %

 

Setting 2 (power=55) (time=1):

    Trial 1, VI:

        Rotation: 338 degrees

        Wheel Turns: .938889

        Distance: .16224 m

        Velocity: .16224 m/s

    Trial 1, Student:

        Distance: .184 m

        Margin of Error: .377%

   

   

 

Trial 2, VI:

        Rotation: 341 degrees

        Wheel Turns: .947222

        Distance: .16368 m

        Velocity: .16368 m/s

    Trial 2, Student:

        Distance: .182 m

        Margin of Error: 10.599%

 

    Trial 3, VI:

        Rotation: 342 degrees

        Wheel Turns: .95

        Distance: .16416 m

        Velocity: .16416 m/s

    Trial 3, Student:

        Distance: .186 m

        Margin of Error: 12.474%

 

Setting 3 (power=65) (time1):

    Trial 1, VI:

        Rotation: 419 degrees

        Wheel Turns: 1.16389

        Distance: .20112 m

        Velocity: .20112 m/s

    Trial 1, Student:

        Distance: .223 m

        Margin of Error: 10.318%

 

    Trial 2, VI:

        Rotation: 422 degrees

        Wheel Turns: 1.17222

        Distance: .20256 m

        Velocity: .20256 m/s

    Trial 2, Student:

        Distance: .22 m

        Margin of Error: 8.254%

 

    Trial 3, VI:

        Rotation: 427 degrees

        Wheel Turns: 1.18611

        Distance: .20496 m

        Velocity: .20496 m/s

    Trial 3, Student:

        Distance: .216 m

        Margin of Error: 5.245%