Attached is the final CDR report:
Lab Summary
Performance Test One – Design
The purpose of this performance test was to determine which of two AEV designs in consideration were the most efficiency and would best fulfill the goals laid out in the Mission Concept Review. After analyzing each design and how it performed, the team was better able to alter the current design and improve efficiency. Overall, this section of the performance test was specifically used to scrutinize the design and understand how different parts would affect the AEV’s final run.
Final results showed that the team’s current design (no added parts) tested best on the track. The second design the team utilized was essentially the same design, but with an added part on the head of the AEV. This design decreased efficiency because it added weight, throwing off the center of balance as well as requiring the AEV to use more power.
View both AEV designs here:
AEV with added part
Final AEV design
Major takeaways from this lab are a more informed approach to perfecting the design of the AEV and the knowledge that our code deviated greatly from its intended use when different parts were added, i.e. the part used in the second half of this test.
View the full executive summary here:
Performance Design Review (PDR)
Attached is the Performance Design Review (PDR), which is a memo describing the performance and design of our AEV.
Performance Test Two – Code
The purpose of this performance test was to analyze two different codes and decide which optimizes AEV efficiency. In this lab, the team constructed two separate codes, performed two runs (each with the same design), extracted the data and converted it in into numbers which correspond to each codes efficiency. With these numbers, the team could further pursue a code and achieve a more optimal efficiency.
Final results from this performance test show that the initial code used consistently outperformed the secondary code. This is due mainly in part to the fact that the initial code used was a perfected final code. Another reason that the initial code reliably performed better is because it utilized the ‘motorSpeed’ command in contrast to the ‘celerate’ command, which was used in the second code. This is because the code with the ‘motorSpeed’ command used less energy than the other despite an overall similarity.
Major takeaways from this lab are empirical data regarding energy consumption and efficiency in reference to the code used and the ability to better design and reach the final code.
View the full lab memo here:Memo 2
Performance Test Three – Energy
The purpose of this performance test was to complete the entire scenario described in the Mission Concept Review and ultimately through testing decide which combined AEV code and design will perform most efficiently overall. The group compiled all previously tested designs and codes and tested them together, ultimately reaching the best possible design and code combination.
Major takeaways from this performance test are the final design and code for the AEV, data representing the preliminary final run, and an overall completion of the Mission Concept Review.
View the full memo here:PT3
Lab 7 – Design Analysis Tool
The purpose of this lab was to become familiar with the MATLAB design analysis tool, a tool which helps to conduct a performance analysis of the AEV. Immediately following a run, data from the arduino was extracted on to the computer. Next, the data was translated into EEPROM analysis calculations and was uploaded to the design analysis tool in MATLAB, which plotted graphs of Power v. Distance and Power v. Time. Due to malfunction in the arduino board and a lengthy diagnosis period, the team was not able to complete Lab 7. However, the team still gained the knowledge required to continue perfecting the AEV through analysis of another team’s data and translation of that to fit their AEV*.
Major takeaways from this lab are the are the ability to further address issues in power consumption, improve design and code in order to achieve desired efficiency/power consumption, and concrete data for each AEV code and configuration — something which helped to narrow down options and reach a final design.
View the full executive summary here: ES 7
Note: Data in executive summary is not Group K’s, but executive summary is original.
*= instruction to do so was given by Lab TA’s.
Lab 5 – Systems Analysis 1
The purpose of this lab was to become familiar with different propeller configurations and quantify the efficiency of each. In this lab, data was pulled from four different wind tunnels, each with a different propeller configuration or diameter. With this, each member of the group compiled data and eventually calculated and graphed the overall efficiency and advance ratio for each propeller and configuration (pusher or tractor/puller). This lab related to our overall AEV design primarily because one of the main goals of this project is to create an efficient product. With these numbers, our group was able to make an informed decision as to which propeller length and configuration would be the most efficient for our design. Ultimately, our group decided to continue pursuing a ‘pusher’ propeller configuration with 3-inch blade diameters.
Major takeaways from this lab were an empirical understanding of how different propeller configurations/blade diameters test in categories such as propulsion efficiency, advance ratio, and power. With this knowledge, the group was again able to perfect their design and enhance efficiency.
View full executive summary here: AEV 5 ES
Our AEV utilizes the “pusher” configuration. Also pictured are the two 3″ propellers.
Lab 6 – System Analysis 2
The purpose of this lab is to measure the performance of the AEV through utilization of EEPROM data extracted from the arduino. To obtain this data, two different AEV configurations were assembled and tested on the track. Next, the EEPROM data (time, current, voltage, wheel counts) was extracted from the arduino board and uploaded into MATLAB where these numbers were converted into physical parameters. Graphs of power versus time were plotted for each of the two configurations, and lines of code were assigned to each phase. It was ultimately determined that the original design for our AEV is more efficient over time than the other.
Major takeaways from this lab are data about the power consumption of the AEV, how the code affects power consumption, and a knowledge about how different added parts (or lack thereof) affect power.
AEV supplied power graph with phases indicated.
Graph of supplied power versus time for the AEV.
View the full executive summary here: Executive Summary 6
Lab 4 – External Sensors
The purpose of this lab was to assemble and become familiar with the AEV’s reflectance sensors and how to integrate their abilities into our code. The reflectance sensors work in conjunction with the reflective tape on the wheel to accurately count the AEV’s movement on the track, providing means for more accurate coding and adaptability to many situations. This lab required the team to attach the senors to the arduino/AEV, and after doing so, test them in the arduino program. After confirmation that the senors worked properly (indicated in the arduino program with a repeating series of ‘1’), the team gained the ability to use arduino commands such as ‘goToRelativePosition(m)’ and ‘goToAbsolutePosition(m)’, which are dependent on information from the sensors regarding how many marks the AEV has traveled.
Major takeaways from this lab are a knowledge of how the AEV will travel along the track in units of marks, a more broad understanding of previously introduced arduino commands, and a more developed AEV design.
View the full executive summary here: ES 4
The reflectance sensors, zip-tied to the AEV arm and plugged in the arduino board.
The reflective tape on the wheel. This helps the sensors count the amount of marks traveled.
Lab 3 – Concept Screening and Scoring
The purpose of this lab was to test several of the designs from lab one by running them on a straight track and, using concept screening and a scoring matrix, assign each design a point grade to help ultimately choose which to further pursue. Using these methods, each design can be graded on several criteria (listed in executive summary) and given a score. The design with the highest score, in this case Design 1 (“Y-Wing”), was chosen as the preliminary AEV design. The team assembled 3 designs based on their concept designs and gave them a score based on how they performed in comparison to the reference design. From the score sheets, it is evident that design 1 is best suited to pursue as the potential AEV design, while design 2 and 3 fall behind slightly in each category.
Major takeaways from this lab include a basic understanding of concept screening and scoring matrices, a knowledge of how to rank designs in comparison to a reference design, and the ability to program the AEV to run on a straight track.
See the full Concept Scoring Scoresheet here.
View the full executive summary here: AEV 3 Exec Summary