AEV Research

Preliminary Research and Development

Exercise 1: Programming Basics

Deliverables

  • Motors ran smoothly and there was little to no delay that was observed during the test.
  • See “Background” for Basic Function Calls table

Exercise 2: Reflectance Sensors

Deliverables

  • The commands used in this test will affect future aspects. The general code that controls the movement of the AEV is dependent on the functionality of the sensor commands.
  • The reflectance sensors track the distance traveled by the AEV. The infrared LED and phototransistor on the reflectance sensors face a reflective tape on one of the AEV wheels. When on the non-reflective portions of the wheel, the Arduino records the time during which the voltage change is recorded as one mark. These marks are converted into inches so the change in location of the AEV can be tracked. This is important because the data collected from the reflectance sensors will then be utilized in the code that will run the motors. The speed and direction of the AEV can then be controlled by distance traveled.

Exercise 3: Creative Design Thinking

Deliverables

As speed increases aerodynamic drag increases substantially. Increased resistance not only leads to increased energy demands, but also greater traction requirements; The length to width ratio of carts generates a complex aerodynamic schematic when compared to cars and planes making it essential to analyze how energy consumption compares to distance and time. According to Yang, “the aerodynamic drag of the train is the main method for reducing the overall resistance and energy consumption of high-speed trains”. Expanding on this concept a economic and efficient AEV design is of utmost importance in reducing energy needs.

 

Yang, Mingzhi, et al. “Moving Model Test of High-Speed Train Aerodynamic Drag Based on Stagnation Pressure Measurements.” PLoS ONE, Public Library of Science, 2017, www.ncbi.nlm.nih.gov/pmc/articles/PMC5240972/.

Advanced Research and Development

Advanced R&D 1: Battery Testing

Voltage decreases as the number of runs increases

Distances traveled and the powers supplied to the AEV were relatively small, voltage changes were also small

Voltage changes were insignificant

Motor power has greatest correlation with distance

Potential errors involve an error in thrust levels and distance. These factors were not of sufficient value to notice a substantial change in voltage. Furthermore, code the AEV was initially set in the wrong direction; this was addressed by the addition of reverse within the code. Improvements in this lab could involve letting students know that the relationship will not be substantial due to the limited distance of the track. Knowing this ahead of time would improve code adjustments. Moving forward, we must note that in theory number of runs, distance traveled, and power supplied to the motors leads to a decrease in voltage. However, based on the data collected there is no discernible correlation between voltage and distance traveled. Our tests indicate that in small scales, the effect of voltage on distance traveled is negligible.

Advanced R&D 2: Energy Analysis

 

 

 

 

 

The reflectance sensors had a 21 mark error (about 3.53% error)

2.7 gmf friction force between wheels and track

11.4 gmf propeller force

0.655 meters/second average velocity

Potential errors and limitations of  analyzing energy usage by the AEV can be attributed to several factors. Constraints from the physical measuring tools permit a limited measurable accuracy. Human error could account for misreadings of the measuring tools. Additionally, any movement of the AEV wheel upon removal from the suspended track could lead to an error in distance traveled as calculated by the reflectance sensor. Systematic error within the reflectance sensor or the Arduino could potentially lead to an inaccurate calculation of the data when displayed in the analysis tool. Finally, random error between measurements and in between data samples taken by the Arduino could lead to inconsistencies between runs and variations in levels of variables even if they were programmed to remain constant.

Final Performance Test

The final run took 52 seconds and used 243.74 J of energy. The AEV did not have any issues passing through the gate when picking up the load and returning. The load was attached and did not hit the end of the track. The consistency and success of the final run was due to extensive testing and code alterations that were applied after each test run. Even so, the energy used in the final run was higher than expected. Potential factors affecting the energy usage include track inconsistencies from friction, wind, and gravitational forces. In addition, different batteries were used for testing and for the final run. The large spike in energy usage as seen in the Power vs. Time graph was because the AEV picks up the load, adding additional weight. Although this energy and time interval fell within budget, unexpected fines due to the track breaking and AEV falling resulted in a final cost of $637,277.38