Lab 4- System Analysis 2 & Design Analysis Tool

Summary

The fourth week of the lab involved downloading, uploading, and interpreting data from the automatic control system after running various tests with the AEV.  The purpose of this lab was to learn how to download data from the control system, to be able to convert EEPROM Arduino data to physical parameters, and to learn how to calculate performance characteristics using physical parameters.  Additional goals were to become more familiar with the MATLAB based design analysis tool, to learn how to upload wind tunnel and Arduino data into the design analysis tool, to be able to conduct performance analysis of the AEV operation, and to be able to export plots created by the analysis tools for analysis.  

After studying the lab manual and figuring out the correct distance that the AEV should travel in order to travel from the starting point to the first stop, the team developed a program.   A trial run was done on the monorail and then the Arduino EEPROM data was downloaded to a computer using a mini-USB cord.  After the data was collected, the team wrote a MATLAB script file that loaded the EEPROM data and converted the various components to physical parameters using a variety of conversions.  Next, performance analysis was done and the power and energy supplied to the vehicle was calculated. Two plots were made: one of power versus time and the other of power versus phases of time.  A table with a breakdown of the supplied energy per line of code was also created.  The next part of the lab involved using the design analysis tool to evaluate the AEV test run.   The team downloaded the EEPROM data from the Arduino motor and performed an analysis.  

Results and Analysis

From the experimentation in the lab, the team was able to collect very useful information. The first important piece of information is the relationship between power and time.

Figure 1: Power vs Time

Figure 1, above, shows the relationship between power supplied to the AEV and the time during the test run on the track. While this may look like provides very little useful information, when compared to the Arduino Code the team programed, the graph can be broken into phases that correspond with the code.

Figure 2: Power vs Time Phase Breakdown

As seen above in Figure 2, the run could be broken down into seven phases based on the correspondence to the Arduino code. Important conclusions and specific data can be taken from the graph. Since power is the rate at which energy is used, the area under the curve on the graph is equal to the energy used during that phase. Using a midpoint approximation, the incremental energy can be calculated for each datapoint, see Appendix A for sample calculations. By summing up the incremental energies for each data point and then for each phase, the total energy consumption can be found.

Table 1: Phase Breakdown Data

Phase Arduino Code Time (seconds) Total Energy (Joules)
1 motorSpeed(4,40); goToAbsolutePosition(50); 0.12 1.804723
2 motorSpeed(4,40); goToAbsolutePosition(50); 1.621 16.930507
3 celerate(4,40,50,3); 3.002 33.91562
4 brake(4); goFor(1.5); 1.44 0
5 reverse(4); 0.12 0.813
6 motorSpeed(4,30); goFor(4); 3.96 29.74793
7 brake(4); 3 0
Total Energy Used: 83.21178

 

The data from Figures 1 and 2 is summed up in Table 1, above, and is juxtaposed next to the Arduino Code. The data in Table 1 is useful for the future programming of the Arduino code and allowed the team to learn a couple things about how the code works in the physical world. The first important piece of information is that the ‘brake’ command uses no power at all. Secondly, the ‘celerate’ command provided the most efficient energy consumption rate. These two bits of information are useful because the coasting that the brake command allows could be coupled with the celerate command to create a period where the AEV can travel a sizable distance without using any power and uses minimal power when achieving the initial velocity. For example, in Figure 3, see Appendix A, the AEV was able to travel a full two meters with a zero energy consumption.

As the AEV project becomes broader, it is important for each team member to have specifically assigned roles for the future. Specific roles are included under the sample calculations for each team member.