Generator Experiment

According to Faraday’s Law, changing magnetic fluxes through coiled wires generate more current and more voltage and according to Faraday, the greater the “change in magnetic flux”, the greater the currents and voltages.

The purpose of this experiment is to show that the faster we shake the magnetic tube, the greater the voltage and current should be.  Our task was to “correlate the number of shakes of the generator in a time span” of thirty seconds, with the sum of the square of the voltages that the generator generates.

For this experiment, we used relatively simple equipment such as the ones listed below:

  • One generator
  • One voltage probe
  • One NXT adaptor
  • NXT
  • Labview VI
  • Excel sheet
example of generator

Procedure:

  1. Shake the tube at a particular rate (20, 30, 40, 50, & 60.)
  2. Count the number of shakes in the 30 second data collecting interval
  3. Calculate in Excel the sum of the squares of the voltages (SSV’s) (the voltage is logged after each second)
  4. Repeat 1-3 four more times at a different shake rate and calculate the sum of the square for each new shake rate.
  5. Plot the SSQV’s as a function of # of shakes and fit the result to a linear curve
  6. Explain your results in your blog.

Results:

 

Shakes Sum of Squares
20 64.34439143
30 43.20274805
40 37.46300507
50 8.289315577
60 87.67968062

Screen Shot 2016-02-19 at 7.50.26 AM

Conclusion:

Though Faraday’s law states that voltage and current should increase as the change in magnetic flux increases, our results did not reflect this.  In fact, our results, for the most part, showed an inverse relationship between the increasing change in the magnetic flux and the voltage.  However, our results also had an anomaly as our last data point at 60 shakes did not follow the trend.  This result can be due to several reasons, however, it may simply have come down to the fact that the program we used malfunctioned in some way.  Our data was very inconsistent and the kept accumulating even after resetting the experiment making it difficult to know which data points to use.

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