Tag Archives: Labview

Solar Energy Experiment

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Since the discovery and implementation of photovoltaic cells our world has been revolutionized by this renewable energy source. Solar Panels convert sunlight into DC electricity. The more sunlight the solar panel is exposed to, it results in the electrons in the solar panel to be “excited” more aggressively, thus resulting in a higher voltage. Many companies and home owners have turned to solar panels to power several appliances being used throughout the building/house. Although solar panels are a huge investment, in the long run they become economically beneficial. During our Freshman Seminar class on October 28, we performed a photovoltaic experiment. During this experiment we measured the voltage output of a small solar panel while changing the height of the light source and the color filter above the solar panel. We used a NXT microcontroller and a voltage probe in order to collect data through a Labview prewritten code. A copy of the code can be seen below.

 

The first task of the experiment that we tested was determining the relationship between distance and voltage output. It is important to remember that as the distance between the light source and the solar panel increase, the light intensity decreases because the photons spread out more with greater distance. We tested this by using a flashlight (in my group we used a iPhone) to serve as our sunlight. We changed the distance between the light source and the solar panel during each trial. We did three trials, each having a distance of 1 cm, 5 cm, and 10 cm respectively. If you look at the graph below, you can conclude that the relationship between light intensity and voltage output is linear. Both variables are inversely proportional to each other. As the distance increases the voltage output will decrease. Our coefficient of determination was 0.9062 thus proving there is a obvious correlation between distance and voltage output.

 

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The second task of the experiment that we tested was discovering how a colored filter affects the voltage output, while keeping the distance constant. We used a green, red, and purple/blue clear transparent filters. We compared the voltage output of each filter to the voltage output with no filter. As can be seen in the bar graph below, as the color depth got darker, the voltage output was getting lower. This is a result of the darker colors absorbing more light and allowing a smaller amount of light through it.

 

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Overall this activity was extremely enjoyable and very educational. This activity connects directly to renewable energy sources such as solar panels. As we observed as the light intensity increases the voltage output increases simultaneously. Solar panels with tracking systems that tilt/rotate the solar panel towards the sun are applying the very basic observation we saw in the experiment. If the solar panel is exposed to the maximum light intensity available it will output the most voltage it can at that moment.

Generator Experiment

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During our Freshman Seminar class on October 26, we performed a Generator Experiment. Ultimately, we were testing the creditability of Faraday’s Law. Faraday’s Law states that if you change the magnetic flux through a coil it will result in a voltage to be “induced” in the coil. This would result it in a voltage and current to be created. We simulated this statement by having a shaking flashlight. The outer casing of the flashlight is transparent and inside this outer shell there is a copper coil with many turns. Also inside the flashlight is a magnet. The magnet is loose and can move freely vertically. By shaking the flashlight the magnet would move through the coil thus producing a voltage and current. We collected the data (voltage being produced) by using a NXT microcontroller, which was running Labview code. A copy of the code can be seen below:

 

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I have provided a copy of our data points that we collected during each 30-second trial. In each trial we would increase the number of shakes. In addition, a scatterplot is provided displaying the best-fit line and the coefficient of determination (R2).

 

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As can be observed in the graph as the number of shakes increased, the sum of voltage squared increased as well. The faster the magnet crossed through the coil the more electricity that was being produced. At times the voltage reading was negative and this is because the polarity was changing from positive to negative. In conclusion, our R2 value was 0.96 thus concluding that there is a very strong relationship between the number of shakes and sum of voltage squared.

Introduction to Lego Mindstorm

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FullSizeRenderRobots have numerous functionalities that vary based on the objectives that the robot is trying to accomplish. Robots can either be a simple design with a drive train and motors or they can be very complex systems that implement the usage of sensors to take digital/analog data and use it to better complete the task. During our last 2 seminar classes we built a basic car with a Lego Mindstorm set. We began building our robot on 9/9/15 by using the different parts in our kits and following the instructions from an online manual. Building the robot was not as obvious as you might have thought! You needed to make sure that pieces were connecting into the proper holes and that the orientation of different pieces was actually correct. It was a little bit tedious but the satisfaction of seeing the final result was definitely rewarding! After we successfully built the robot, we proceeded to test the robot by using prewritten code through a programming language called Labview. The first code made the robot drive move depending on the power output of each motor. On our second class on 9/16/15 we used another prewritten code to test how far the car would travel in 1 second at different power outputs. For any technical readers, the code looked this:

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We used a ruler that was placed parallel to the robot to observe how far the robot moved in the allotted time. We compared our human measurement to the distance that the program in Labview calculated. We tested the robot at different power outputs and calculated our margin of error.

Excel

  1. We measured the diameter of wheel and resulted with 5.5 cm or 0.055 meters. We calculated the circumference and got 0.172788 meters.
  1. The relationship between the degrees the wheel rotated and the number of turns of wheel is that the degree the wheel rotated is equal to the product of number of turns times 360.
  1. Seconds are related to milliseconds because 0.001 seconds is equal to one millisecond or 1000 milliseconds are equal to 1 second.
  1. The distance is related to the number of turns because the distance is the product of the circumference times the number of turns.
  1. At a power level of 75 we measured a distance of 0.275 meters and Labview calculated a distance of 0.2736. The margin of error could be calculated by using the formula: ME = 100 x (dmeasured – dlabview)/daverage. Our margin of error was 0.52%.

 

There are two things that I believe could account for these discrepancies. The first one is the braking of the robot once the allotted time passed. The robot would accelerate and just come to a stop after one second. As a result of this abrupt stop the robot would break so aggressively it would jolt back slightly. I think this effect could definitely account for the difference in the measurement. In addition, I noticed throughout all trials the robot would never drive forward in a perfect straight line. It would always sway towards one side even though the power outputs were equal for both motors. As a result, the distance calculated by Labview differs from the distance we measured since we assumed it traveled in a near perfect line.

 

In my opinion this activity connected to the idea of energy consumption and how it can apply to real vehicles. As we observed during the activity, the higher the power output the more distance the  robot travelled in that one second period. In actual vehicles the same idea occurs. The greater the power, the faster the vehicle travels thus covering more distance. But unlike robotics which don’t release any greenhouse gases, real vehicles actually produce them. This presents the idea of how can we be more energy efficient as a society. We must develop cars that release little to no Carbon Dioxide regardless the power output of the engine. We as a society must look for alternative ways to power cars such as with electricity, which can prove to be advantageous.