Preliminary Design Report

Submitted to:

Dr Parris

GTA Sheena Marston

Created by: Team G

Jack Zhang

Jordan Miller

Laurence Liu

Nathan Radomski

Engineering 1182

The Ohio State University

March 25, 2017

Table of Contents

Executive Summary 2

Introduction 3

Experimental Methodology 4

Results 5

Discussion 9

Conclusion and Recommendations 9

Appendix 10

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Executive Summary During the experiments over the semester, students mainly worked on designing, building, testing and analyzing the Advanced Energy Vehicle(AEV). The objective of the experiments is to successfully pick up and bring back the cargo with a R2 robot on it. The energy management and operational efficiency are the main features that are focused on. As the background, the empire is building a new army after the Death Star. The rebels are producing R2D2 to prepare for the war secretly on remote planets, so the energy is limited. The AEV and monorail system are chosen to deliver the robot from one side to another. The goal of the operation is to let AEV travel from one side of the rail to other side to pick up a cargo there with the robot on it. In the whole process, the energy usage should be limited and the efficiency and consistency should be maximized. The project consists of several practical experiments. Based on the previous experiments, the AEV with code uploaded is put on the rail and will perform based on the code. After the performance, the data can be upload to computer to use as a performance test. For the last two experiments. The two prototypes out of total four designs have been tested on the rail. By comparing the performance of these two, the AEV with better performance is selected as final design and is tested with two sets of codes. The sources of error do exist in the experiments. One possible source of error is caused by the way sensors work. The sensors are used to determine how many rounds do the wheels turn, so that the distance travelled can be determined. However, the sensors are not very sensitive sometimes and may not be very precise. Thus, it’s difficult to control the position of the vehicle precisely. Another possible source of error is that the battery cannot provide consistent power. The charge of battery decreases after each time using it. So, the motors may behave differently per the power source. One possible solution to both sources of error might be that considering increase fault tolerance for the code. This means that the vehicle may perform similarly each time with small difference under different conditions. With the AEV project, students may learn how to work with Arduino codes and have a deep understanding of how the AEV works. In the end of the semester, students may be able to complete the scenario given by the manual with their own AEVs.

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Introduction

The rebel alliance needs to build a monorail transportation system on remote plant, where the power is limited. The Advanced Energy Vehicle is designed and it is focused on energy efficiency and operating consistency.

This report will go over the experimental methodology used to create the AEV and its code. The results of the test will be explored in depth and how the results influenced decision making for the AEV. The purpose of the lab was to determine which AEV design to continue moving forward with. The lab was also used for refining the two coding scenarios. The information and conclusions provided in this report are the basis for future labs and the ultimate goal of completing the Mission Concept Review.

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Experimental Methodology

Over the course of the first six labs, the group gained information that would help the group in designing the best Advanced Energy Vehicle. The group learned how the coding for the arduino worked, collected data about different types and configurations of propellers, and screened and scored the designs to create the final design. This past week, the group has been refining the final design for the AEV. Using the built AEV, the group tested the AEV’s design, and code. On the first run, the AEV was tested to see if there were any structural components that needed to be improved or fixed to increase efficiency. To do this the AEV was placed at the starting line and ran around the track. The group watched the AEV and made note of any potential structural issues. In the second run, the group tested two Arduino codes and collected data on both to determine which was the most energy efficient. To accomplish this, two codes were written using the commands:

● celerate(m,p1,p2,dt); ● motorSpeed(m,p); ● goFor(dt); ● brake(m); ● reverse(m); ● goToRelativePosition(m); ● goToAbsolutePosition(c);

The two codes were intended to move the AEV from the starting point to the gate, stop 7 seconds at the gate, and proceed to the pickup location to pick up the caboose. The data for the trials was then downloaded from the Arduino and evaluated to determine which code performed the task more efficiently.

Figure 1: Final AEV Design on Test Track

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Results After carefully analyzing and comparing the total of four designs, the best two of them are chosen to be tested on the rail. The analysis is based on the concept screening and scoring matrix provided below. According to concept screening sheet, the Design A and Design B have highest net score among the four designs. By referring to the scoring matrix, the total score of Design A and B are also

highest of the four designs.

Table 1: Concept Screening Scoresheet

Success Criteria Design A (Nathan)

Design B (Jordan)

Design C (Jack) Design D (Laurence)

Balance in Turns 0 + 0 0

Weight + 0 + -

Center of Gravity

+ 0 0 0

Maintenance + + + +

Durability 0 - - 0

Cost + 0 + 0

Efficiency 0 + - +

Sum +'s 4 3 3 2

Sum 0's 3 3 2 4

Sum -'s 0 1 2 1

Net Score 4 2 1 1

Continue? Yes Yes No No

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Table 2: Concept Scoring Scoresheet

Design A (Nathan) Design B (Jordan) Design C (Jack) Design D (Laurence)

Success Criteria

Weight

Rating Weighted

Score Rating

Weighted Score

Rating Weighted

Score Rating

Weighted Score

Balance in Turns

10% 2 0.2 4 0.4 3 0.3 3 0.3

Weight 20% 4 0.8 3 0.6 4 0.8 2 0.4

Center of Gravity

20% 3 0.6 2 0.4 3 0.6 3 0.6

Maintenance 5% 5 0.25 4 0.2 3 0.15 3 0.15

Durability 10% 4 0.4 1 0.1 2 0.2 2 0.2

Cost 10% 3 0.3 4 0.4 5 0.5 2 0.2

Efficiency 25% 3 0.75 4 1.0 2 0.2 3 0.75

Total score 3.3 3.1 2.8 2.9

Continue? Yes Yes No No

Design A has the best score in both decision-making matrices, which means the design is more likely to be focused on in the future. Design A is lightweight, easy to maintain, and durable. The design is meant to be extremely simple, which means there is no unnecessary weight and so that it can be easily maintained. Another main feature of the design is that it is perfectly balanced. The design is built symmetrically, so that the weight and components are distributed equally. The main drawback is the efficiency. This design utilizes the larger, more inefficient 3030 propellers for the extra thrust it provides compared to the 2510 propellers. Design B is another possible design that is tested in performance test 1. The advantages of this design are the aerodynamics and the balance in turns. The arrowhead shape allows for great aerodynamics going forward with slightly less efficiency going backwards. The balance in turns allows the vehicle to maintain a steady course along the path. However, the design is back heavy which would cause the nose to go up reducing the effectiveness of the aerodynamic design. The two designs behave similarly in the performance test. Both of them are able to start and travel a distance smoothly and they are balance on the rail or at the turn. Stopping distance after the ‘brake’ is different for the two designs, which is due to the various of mass and the propeller chosen. Both vehicles went around the track smoothly. By uploading the performance data of these designs, the efficiency of them can be evaluated. In the graph below, the blue line represents the performance test of Design A and the orange line represents the design B. From the graph, it’s clear that Design A starts with lower power than Design B and is more consistent. However, the energy consumed by Design A is larger than that of design B. The area under the curve represents the energy consumed and by comparing the two curves, Design A consumes more energy. Both designs could not stop before the red mark of the gate, which caused it could not finish the whole scenario.

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Figure 2: Power vs. Time for Performance Test

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Table 3: Breakdown of Consumed Energy

// Current absolute position in inch float cur_pos = 0.0;  // Goto absolute position in inch at power% inline void goto_inch(float inch, int p) {   const float brake_pos = 0.58;    int p0 = getTotalMarks();    int marks = round_int(inch_to_marks(inch - cur_pos));   motorSpeed(4, p);   goToRelativePosition(marks * brake_pos);   brake(4);   goToRelativePosition(marks * (1.0 - brake_pos));    int p1 = getTotalMarks();   cur_pos += marks_to_inch((float)(p1 - p0)); }   void myCode() {   reverse(4);   cur_pos = CARGO; 

 

  goto_inch(TRACK - RED - 0.5*BLUERED, 20);    goFor(GATE_TIME); 

43 J 

  goto_inch(2.0*TRACK - CARGO, 20);    goFor(CARGO_TIME); 

(Estimate based on previous result) 

43 J 

  reverse(4);   cur_pos = 2.0*TRACK - cur_pos;   //cur_pos = 2.0 * AEV;    goto_inch(TRACK - RED - 0.5*BLUERED, 20);    goFor(GATE_TIME); 

(Estimate based on previous result) 

90 J 

  goto_inch(2.0*TRACK - DROPOFF, 20); } 

(Estimate based on previous result) 

90 J 

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Discussion

Currently, there are two solutions for the scenario. The first solution is relatively simple and easy to implement. It sets the motor speed to a selected value and stops all motors before the target position. Then, the AEV will slide to the target point. This solution has many cons. First, the AEV runs at different speed every time even if the motor speed was set to the same value, because of battery and some other factors. Second, we need to measure a lot of times on the track to know where it should stop all motors and what motor speed should it run. In addition, it cannot adapt to different operating environments, which means all works need to redo in the new environment. If the weight of cargo changes or the length of track changes, which may occur frequently, the operating parameters need to re-measure and change in the program. This solution depends on a specific track, a specific cargo, a specific power supply, so it has a very poor operational consistency. Another solution is controlling its speed automatically, which makes sure that the speed is consistent. The AEV monitors its speed by computing ds÷dt and adjust the motor output based on its speed. In the previous labs, the AEV did not stop precisely at the position where we wanted it to stop. We come up with two approaches. The first one is reversing the propellers to provide an reverse thrust, which can fully stop the AEV. But it will consume a lot of energy and it is very difficult to control. It may make the AEV to move back. Another approach is making a real brake using the servo, which only uses relatively fewer energy and can stop the AEV precisely, but the AEV’s weight will increase.

Conclusion and Recommendations

The results of the labs and the performance test have shown that a push system is the best for propulsion. They also showed that the code was going to need to have great precision at the gate to activate the first sensor, but not the second. The group will continue with the first prototype, shown in figure 4. The design is more rigid and aerodynamic than the second prototype design. The first prototype is a simpler design which has less weight and unused space. The propellers used in the first prototype are more efficient than the ones that would be used in the second prototype. In future labs the group will experiment creating a brake and the the effects it has on the code and the energy that would be used. The group may also chose to test the effect of a hood at the front of the design. Neither prototype or codes completed the course. An error was found in the code that created an error in unit conversion. This error has been fixed and the group will test the code in the next lab.

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Appendix

Figure 3: Group G Final AEV Design

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Figure 4: Parts List for Final AEV Design

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Figure 5: Group G Prototype 2

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Figure 6: Group G Prototype 2 Bill of Materials

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Table 4: Project Schedule

Task Start Due Jack Zhang Jordan Miller

Laurence Liu

Nathan Radomski

% Completed

Design AEV 17/02/03 17/02/17 25 25 25 25 100

Test AEV 17/03/09 17/04/14 25 25 25 25 60

Improve Code 17/03/15 17/04/01 20 20 40 20 60

Make a new brake 17/03/20 17/04/01 0 0 0 0 10

Project Portfolio 17/02/01 17/04/14 25 25 25 25 70

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