Table of Contents

Abstract

Purpose/Hypothesis

Literature Review

Experimental Design


Materials/
Equipment

Test Station Construction

Procedure

Observations

Calculations

Results

Discussion
Sources of Error

Applications


Glossary of Terms

Acknowledgements
Bibliography

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Statistical Analysis


Statistics are a way of assessing the quality of the data collected. Standard Deviation (SD) is a mathematical calculation used on a set of data to assess the amount of scatter or dispersion from the mean, or average. It is an indication of accuracy. If all data points are exactly the same, the SD would be equal to 0.

Coefficient of Variation (CV) is a mathematical calculation (standard deviation x 100 /mean) that provides information that can be used to compare different sets of data. A CV of 10% is considered acceptable.

Wind Speed Measurements: The differences in wind speed were insignificant with a highest SD of 0.19 and a highest CV of 3.0%. The chart below is a summary of all wind speed measurements and statistical analysis.

Grand Mean of Wind Speed Measurements:
A grand mean of all medium wind speed measurements was calculated as 5.6 with a SD of .12 and CV of 2.14%.
A grand mean of all high wind speed measurements was calculated as 6.44 with a SD of 0.089 and CV of 1.38%.

The chart below shows the grand mean calculations of wind speed. Overall, differences in the wind speed at each of the fan speeds for each set of observations was insignificant.

mAmp Measurements:

The highest SD was 2.73 and CV was 10.74%. There was only one observation set with a CV for mAmps greater than 5%. Overall, the measurements were quite reproducible. The chart below shows the means and statistics for all mAmp measurements.

mVolt Measurements: The highest SD was 13.12 and CV was 7.08%. The chart below shows all of the mVolt statistics.

RPM Measurements: The highest SD was 20.98 and the highest CV was 6.86%. The chart below shows the statistics for the RPM measurements.

Summary of Statistical Analysis

The coefficient of variation for all the data collected was acceptable (i.e. < 10%), with the exception of the mAmp results for one rotor variable at medium fan speed. The rotational speed of that particular rotor was relatively low and produced slightly more erratic results.

Overall, the data collected was accurate and precise.

Conclusions

My engineering objectives were met and the design of the laboratory scale model accomodated my experimental design. The results supported my hypothesis as follows.

I predicted that the number of rotors and their size, placement and orientation on a single horizontal axis windmill would affect the amount of torque and electrical energy produced. I predicted that the size of the DC motor would affect the amount of electrical energy produced and that some rotor variables would be unable to start and continuously turn the axle of larger DC motors.

1. Larger rotors produce more torque and electrical energy than smaller rotors.

2. Increasing the number of rotors from one to two increases the amount of electrical energy generated by over 2000%.

3. Single rotors were unable to start and continuously turn larger motors .

4. Rotor size, number, placement and blade orientation affected the amount of torque and electrical energy produced.

Overall, three 28-cm rotors, placed 0 cm apart with all of the blades offset 40 degrees, produced the most torque and electrical energy, and the highest tip speed ratios. This rotor variation produced 5448 % more electrical energy at medium fan speed, and 2593% more electricity at high fan speed than a single 28-cm rotor.

However, adding a third rotor increased the amount of electricity generated by two rotors an average of 28%.

The largest increase in electricity generated was from increasing the number of rotors from one to two.

The results show that multiple rotors operated at lower wind speeds and had a higher conversion efficiency.

Discussion/Sources of Error