Performance Test 2 spans over 3 lab days. In these labs, we worked on various tasks involving the AEV, including running tests, recording data, and finalizing the Arduino code. Specifically, we worked on testing the second prototype, which we named The Excalibur. Testing this prototype leads us to determine what our final AEV model will look like, as well as finding a code that will be the most efficient.
Performance Test 2
Continuing our work from Performance Test 1, we kept updating the AEV Project Portfolio, fixing the Arduino code, running more tests, and recording data for those tests. We finally conjured an Arduino code which allowed the AEV to travel and stop at the entrance gate for the Jurassic Park scenario and travel back to the Visitor’s Center, or the starting point. This is significant progress on our AEV, as that code basically completes 50% of the whole Jurassic Park scenario. The Excalibur is shown below.
Figure 1: The Excalibur
The data for our test run is shown below.
Figure 2: Propulsion Efficiency & Power Input vs. Distance for The Excalibur
It can be seen from the graph that the propulsion efficiency remained around 12.25% throughout a majority of the test but reached 15% on the second turn of the track and then leveled off again to around 12.25%. Similar to Prototype 1, the gaps in the graph represent moments when the AEV is expected to stop for 7 seconds according to the Jurassic Park scenario.
Figure 3: Propulsion Efficiency and Power Input vs. Time for The Excalibur
Like Prototype 1 from Performance Test 1, propulsion efficiency and power input vs. time and distance were found to be very similar to each other, with relatively the same values in each graph.
The Advance Ratio and the propulsion efficiency for The Excalibur is shown below, along with the propeller type.
Figure 4: Propulsion Efficiency vs. Advance Ratio for EP 3030 Propeller
Prototype 1 involved an EP 3030 Propeller. According to the graph above, the increasing and then decreasing values of propulsion efficiency correspond to a constant increase in advance ratio. As the curve moves from left to right, the voltage and the RPM both decrease because the AEV is doing less work as the rate of acceleration decreases.
The Excalibur weighed 0.213 kg.
The Concept Screening and the Concept Scoring for The Excalibur in comparison with the Sample AEV, the initial AEV drawing models, and Prototype 1 are shown below.
Table 1: Concept Screening
| Concept Screening | Group F | Instr. Annie | 2/6/2015 | ||||
| Success Criteria | Reference | Design 1 | Design 2 | Design 3 | Design 4 | Prototype 1 | Prototype 2 |
| Balance | 0 | 0 | + | + | + | + | + |
| Cost | 0 | 0 | + | + | – | + | + |
| Weight | 0 | – | – | – | – | + | + |
| Minimal Blockage | 0 | + | – | + | + | 0 | – |
| Maintenance | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Durability | 0 | 0 | + | + | + | + | + |
| Sum +’s | 0 | 1 | 3 | 4 | 3 | 4 | 4 |
| Sum -‘s | 0 | 1 | 2 | 1 | 2 | 0 | 1 |
| Sum 0’s | 6 | 4 | 1 | 1 | 1 | 2 | 1 |
| Net Score | 0 | 0 | 1 | 3 | 1 | 4 | 3 |
| Continue | Combine | No | Yes | Yes | Yes | Yes | Yes |
Table 2: Concept Scoring
| Concept Scoring | |||||||||||||
| Design 1 | Design 2 | Design 3 | Design 4 | Prototype 1 | Prototype 2 | ||||||||
| Success Criteria | Percent Weight | Rating | Weighted Score | Rating | Weighted Score | Rating | Weighted Score | Rating | Weighted Score | Rating | Weighted Score | Rating | Weighted Score |
| Balance | 0.15 | 3 | 0.45 | 3 | 0.45 | 3 | 0.45 | 3 | 0.45 | 3 | 0.45 | 3 | 0.45 |
| Cost | 0.15 | 2 | 0.3 | 2 | 0.3 | 3 | 0.45 | 2 | 0.3 | 2 | 0.3 | 4 | 0.6 |
| Weight | 0.15 | 4 | 0.6 | 2 | 0.3 | 4 | 0.6 | 2 | 0.3 | 2 | 0.3 | 4 | 0.6 |
| Minimal Blockage | 0.15 | 4 | 0.6 | 3 | 0.45 | 4 | 0.6 | 3 | 0.45 | 3 | 0.45 | 2 | 0.3 |
| Maintenance | 0.3 | 1 | 0.3 | 3 | 0.9 | 3 | 0.9 | 1 | 0.3 | 2 | 0.6 | 4 | 1.2 |
| Durability | 0.1 | 3 | 0.3 | 4 | 0.4 | 2 | 0.2 | 3 | 0.3 | 4 | 0.4 | 2 | 0.2 |
| Total Score | 2.55 | 2.8 | 3.2 | 2.1 | 2.5 | 3.35 | |||||||
| Continue | Yes | ||||||||||||
The detailed Arduino code for The Excalibur is shown below.
Table 3: Arduino Code for Prototype 2
| Code | Purpose | Supplied Energy | Total Energy |
| celerate(4,0,40,3);motorSpeed(4,40);goFor(2);brake(4); | Power burst then proceed to glide | 6 Watts | |
| goToAbsolutePosition(332);reverse(4);motorSpeed(4,40);goFor(1); | Breaking until it once it reaches 332 marks | 8 Watts | |
| brake(4);goFor(7); | Pause at station | 0 Watts | |
| reverse(4);celerate(4,0,40,3);motorSpeed(4,40);goFor(2);brake(4); | Goes to second station | 6 Watts | |
| goToAbsolutePosition(714);reverse(4);motorSpeed(4,40);goFor(1); | Break | 8 Watts | |
| brake(4);goFor(7); | Pausing at second station | 0 Watts |
It is important to realize that the propulsion efficiency is based off of the power input and the power output. Therefore, it shows that even though the power input for Prototype 1 was almost 2-fold greater than the power input for The Excalibur, it still resulted in a greater power efficiency. Meaning, that Prototype 1 is processing its power input much better than The Excalibur even though it is processing twice as much power.
The Arduino code for The Excalibur demonstrates that breaking uses more energy but it does not show that this energy is used for far less time. It seems that using bursts of energy and then letting the AEV glide, thus using zero Watts, is the most efficient. This strategy is the same as the one utilized for Prototype 1. The primary difference was the power was increased for the motor gliding because there was only one motor.
The cost of The Excalibur was $142.51. The reason that The Excalibur is $44.35 cheaper than Prototype 1, is because it has one less motor and propeller. Although it is slightly less expensive, the quality of the system holds greater significance than its quantitative cost. In addition, Prototype 1 will be better than The Excalibur at picking up and carrying an extra cart at the end of the track.
In conclusion, like for Performance Test 1, Performance Test 2 allowed the team to eliminate extraneous and detrimental aspects of the original prototype in order to help formulate a final prototype with 3D printed aspects. The knowledge gained from the AEV prototype testing allows for the analyzation of the positive and negative aspects of each design. The final design will combine the positive aspects in a cohesive and advantageous manner.