Advanced R&D 1
For our first advanced research and development activity, we tested how different speeds of the motor (changed by changing the power sent to the motor) effect the energy consumption of the belt and wheel system. This test allowed us to find the optimal speed to run the motor at in order to run our direct drive AEV as efficient as possible. This efficiency saves money by reducing the energy needed to power the AEV. In order to this, we held constant the distance the AEV ran and changed the power going to the motor. From there, the data told us a bunch of things. We were able to see the energy used for each power level, the resulting time it took to travel the distance, and then the amount the AEV slid when coming to a stop.
Graph of the Average Slide Distance at each Power Level
The sliding distance happened just as expected, with the greater motor power causing the greatest sliding distance. Noting that 1 mark is about 0.4875 in, the maximum slide distance for any of the power levels is just about 3 feet.
Graph of the Energy Consumption at each Power Level
The noticeable thing in this graph is how the energy consumption drastically increases from 30% to 35% power level while from 35% to 40% the energy barely increases at all.
Cost Table with Final Total Costs for Each Power Level
The cost of the energy used and the time used for each power level was calculated in the table above. The resulting two numbers were added to attain a singular total cost for each power level. The 35% power level had the lowest total cost, meaning that it is the most cost effective power level to run the AEV at without a load. That means that when the AEV is running down the length of the track for the first time to pick up the passenger car, it was coded with 35% going to the motor in order to save money.
Advanced R&D 2
For our second advanced research and development activity, we tested how different speeds of the motor (changed by changing the power sent to the motor) effect the energy consumption of the belt and wheel system while pulling a load. This test is very similar to the first one, but while the first told the optimal speed to run the AEV at going down the track, this test told the optimal speed to run the AEV at coming back up the track with the passenger car. The added load of the passenger car will change the speed, energy consumption, and sliding distance of the AEV; therefore, we couldn’t assume that the optimal motor power level for the first test will be the same optimal motor power level for this test with a load. This test allowed us to find the optimal speed to run the motor at in order to run our direct drive AEV as efficiently as possible. This efficiency saves money by reducing the energy needed to power the AEV. In order to this, we held constant the distance the AEV ran and changed the power going to the motor. From there, the data told us a bunch of things. We were able to see the energy used for each power level, the resulting time it took to travel the distance, and then the amount the AEV slid when coming to a stop. Below is the code ran for this test:
Graph of the Average Slide Distance at each Power Level with the Passenger Car
Much like the graph for the first aR&D, as the power level of the motor increases so does the slide distance; however, when the caboose is attached, the maximum slide is much less than without, most likely due to the added friction and weight the caboose adds to the AEV.
Graph of Energy Consumption at each Power Level with the Passenger Car
The oddest part about this section of data is how the motor power level of 45% uses less energy than 35% or 40% power level. The most likely cause is how anything less than 45% struggles to actually move the AEV with the added load of the AEV so lots of time is wasted moving the AEV which adds to the increased energy usage. On the other hand, anything greater than 45% will easily move the AEV but it uses too many Joules getting the AEV to a high speed.
Cost Table with Final Total Costs for Each Power Level with Passenger Car
As seen in the above table, factoring in the time cost and the energy cost for each power level showed that that 45% power level was the most cost effective. Thus, when the AEV is coming back up the track with the passenger car, it was coded with 45% power going to the motor in order to save as much money as possible while still completing the course.
Advanced R&D 3
This third research and development session focused on fine tuning things about the AEV. For this aR&D, we tried to decrease the distance the AEV slide, as well as eliminate the wheel jumping/slipping when the passenger car is attached that was seen in performance test #2. Three methods for either decreasing slide distance or eliminating the slipping were tested with data collected on both the sliding and wheel slipping. Each method was implemented with the passenger attached and ran for five seconds. The first method was putting a strip of latex on the drive wheel to increase friction, the second method was slowly accelerating the AEV to allow for the drive wheel to catch on the track, and the third method was to transfer the weight of the passenger car above the drive wheel to force it down into the track and thus increase friction, through a new attachment method.
Graph of AEV Slide Distance for each Strategy
The tire strategy where the latex was wrapped around the drive wheel decreased the slide distance much more than all the other strategies. An important note is that every single strategy decreased the slide distance when compared the the slide distance without any of the strategies (the control), meaning any combination of these strategies should definitely decrease the slide distance.
Table Displaying Visual Observations in Regards to the Wheel Bouncing and the AEV Sliding
The bouncing/slipping of the drive wheel when attached to the AEV is hard to model quantitatively; therefore, visual observations were made along with video aid to see if the strategies affected the drive wheel slipping. The tire strategy with the latex very clearly eliminated the slipping of the drive wheel with no slipping be evident to the naked eye or in the video below.
As soon as the wheel starts to turn, the AEV starts to move and with good speed as well, indicating the drive wheel isn’t slipping. Almost as good as the tire strategy in terms of eliminating slipping was the alternate attachment strategy. Just as with the tire, no visible slipping was seen, with the video being shown below.
In this video, the white cross piece simulates the alternative passenger car attachment as the final attachment was in the process of being printed, but the distribution of the weight of the passenger car is still on the drive wheel even with this attachment. The AEV moves as soon as the wheel starts to spin due to the increased friction on the drive wheel, effectively eliminating the slipping. The acceleration strategy which would in theory slowly accelerate to allow for the drive wheel to catch on the track and thus not slip, definitely decreased the slipping to some degree as seen below.
The most important thing to note is how slow the AEV went compared to the other strategies. This low speed is due to the slow acceleration. This increased time causes an increase in energy consumption, both of which factor into the overall cost of the AEV; therefore, this strategy was discontinued while the other two strategies were both implemented into the final AEV design.