This document summarizes Jordan Rhoads' portfolio, including experience and specialties in mechanical engineering, CAD, programming, fluid dynamics, and machining. It describes a project to optimize refrigerated train cars for Union Pacific to improve fuel efficiency. As team leader, Jordan scheduled meetings, assigned tasks, tracked progress, programmed MATLAB codes, and made decisions throughout the project. Prototypes were tested, drag coefficients were calculated, and a report and presentation were produced.

1. Portfolio for Jordan Rhoads
Biography
I am a BYU mechanical engineering candidate. I have experience in
CAD software, computer programing, fluid dynamics, compliant
mechanisms and machining. I am currently involved in research for
creating super­hydrophobic surfaces.
Specialties:
Computer Programing (html, Matlab and C++)
CAD and analysis (NX, Inventor and ANSYS)
Quality Systems Analysis
Compliant Mechanisms
Wind tunnel experience
Machining Experience
Project Overview
Our project was to optimize refrigerated train cars to be more fuel efficient for Union Pacific. Any
modifications developed were required to provide a ROI (return on investment) of 2­3 years.
Project Planning and Scheduling
Our Team: ​ We exchanged emails and had a conference with to our sponsor, Wayne, from
Union Pacific to find out what results he expected from our team. From this information, we
created a requirement matrix, a list of milestones, a budget, and a project contract.
Me:​ I was the team leader for the semester. I gave assignments to team members and verified
their deliverables. I organized and led team meetings. I also created burndown charts tracking
our effort and tasks as a team. I reported the results to Jake, our team coach. These burndown
charts can be found in figure A.1 in appendix A. I also managed the team calendar and making
sure all team activities were scheduled and that all team members were informed of the
activities.
Idea Generation
Our Team:​ We generated 25­30 ideas to improve the fuel cost of the reefer. We down selected
and scheduled to test and develop five of those ideas.
Me:​ I scheduled, organized and led the brainstorming and down­selecting sessions. I led the

2. team to deciding on five designs to test. These designs were: a smooth roof, a smooth bottom,
sideskirts, wheel diverters, fairings, and a dome over the refrigerator.
Prototyping
Our Team:​ Physical models were produced for wind tunnel testing.
Me:​ As mentioned before, I was in charge of scheduling and planning. I helped keep the team on
schedule making and testing the prototypes.
Decision Matrices
Our Team:​ We produced a requirements matrix for our project contract. We narrowed our
concept ideas to five. We made decisions on how to make prototypes, how to calibrate the wind
tunnel equipment and how to calculate the drag coefficients and fuel savings.
Me:​ I assigned a person to be in charge of creating the matrix and I verified her work. Our drag
coefficients were not acceptable so I had to check all of the calculations and data for errors.
Upon discovery of these errors, decisions had to be made. Our coach and I made a decision to
calibrate all three load cells separately since the load cells varied with the data they produced. I
made the decision to use the width of the train for calculating the Reynolds number. I also
decided to use the frontal surface area for calculating the drag coefficient after studying previous
reports and researching the formula. As team leader, I've had to make many judgement calls
and had to prioritize the team's tasks on a day­to­day basis.

3. Engineering Modeling
Our Team:​ We ran the CAD prototypes in the CFD program on the computer to produce a drag
coefficient for each prototype. We also ran the physical prototypes in the wind tunnel and in
Labview and MATLAB we calculated the drag coefficients of each prototype. We compared the
drag coefficients to the baseline model and made an estimate on the fuel savings of each
prototype.
Me: ​I programmed the MATLAB code to calibrate the load cell and pressure transducer in the
wind tunnel. I also programmed the MATLAB code for calculating the drag coefficient, Reynolds
number, and uncertainties for each test. In programming these codes, I had to learn new
techniques in MATLAB and learn to debug my code. These MATLAB codes can be found in
Figure A.2 in the Appendix. I also made plans and rough estimates on how to calculate the ROI.
FMEA
Our Team: ​We created an FMEA chart analyzing the risks of us not fulfilling the grading criteria.
The chart is found in Appendix A.3.
Me:​ I helped lead the team meeting where we made the chart. I gave input on what values to put
for the FMEA.
Engineering Reporting and Presentation
Our Team:​ We put together a project report and presentation.
Me:​ I wrote the product status portion of the report. I also wrote my portion in the Performance
Verification and Validation section.

4. Appendix A:
Figure A.1​: Task and Effort Burndowns for the semester. This shows the distribution of the
completion of tasks and effort over time compared to an ideal distribution.

6.
Figure A.2​: MATLAB code for pressure transducer and load cell calibrations and the full analysis of
drag coefficient and reynolds number.
Pressure Calibration:
%Master script for calibrating Pressure
%Jordan Rhoads 2013‐12‐04
clc
clear all
%static directory input
dir_to_import = 'matlab';
[Voltage,Pressure,Vstd] = PCal_ProcessDir(dir_to_import);
%plot Pressure vs Voltages
jfiles = dir('*.csv');
jsize = size(jfiles,1);
p = polyfit(Voltage,Pressure,1); % p returns 2 coefficients fitting r = m_1 * x + b_2
r = p(1) .* Voltage + p(2);

7. m1 = p(1)
b1 = p(2)
R=corrcoef(Voltage,Pressure);
R2 = (R(1,2))^2
%plot both the points in y and the curve fit in r
plot(Voltage, Pressure, 'x')
hold on
plot(Voltage, r, '‐')
hold off
xlabel('Pressure')
ylabel('Voltages')
title('Pressure vs Voltages')
%Calculate Uncertainty
Vacc = .0025; %accuracy of pressure transducer
Uncertainty_4 = Vacc*Voltage + Vstd %Uncertainty of Pressure Transducer
%PCal_ProcessDir.m
%Inputs directory name and calls process file on each file that matches the
%pattern
function [Voltage,Pressure,Vstd] = PCal_ProcessDir(dirname)
%folder_name = uigetdir %lets you choose file for directory
jfiles = dir('*.csv');
jsize = size(jfiles,1);
Voltage = zeros(jsize,1);
Pressure = zeros(jsize,1);
Vstd = zeros(jsize,1);
for i=1:jsize
[Voltage(i),Pressure(i),Vstd(i)] = PCal_ProcessFile(jfiles(i).name);
end
end
%PCal_ProcessFile.m
%Inputs file name and calucates Cd and Re with uncertainties
function [Voltage,Pressure,Vstd] = PCal_ProcessFile(filename)
%read txt file
myfile = importdata(filename);
%Import Voltage data
V = myfile(:,1); %Voltage for test
Pressure = myfile(1,3)*249.174; %Pressure Converted to Pa
%Calculate mean and std of voltages
Voltage = mean(V(1:10000,1));
Vstd = std(V(1:10000,1));
end

8. Load Cell Calibration:
%Master script for calibrating Load Cell
%Jordan Rhoads 2013‐12‐04
clc
clear all
%static directory input
dir_to_import = 'matlab';
[Voltage1,Voltage2,Voltage3,Mass,Vstd1,Vstd2,Vstd3] = LCal_ProcessDir(dir_to_import);
%plot Pressure vs Voltages
jfiles = dir('*.csv');
jsize = size(jfiles,1);
%m and b for Voltage 1
p1 = polyfit(Voltage1,Mass,1); % p returns 2 coefficients fitting r = m_1 * x + b_2
r1 = p1(1) .* Voltage1 + p1(2);
m1 = p1(1)
b1 = p1(2)
R1=corrcoef(Mass,Voltage1);
R2_1 = (R1(1,2))^2
%m and b for Voltage 2
p2 = polyfit(Voltage2,Mass,1); % p2 returns 2 coefficients fitting r = m_1 * x + b_2
r2 = p2(1) .* Voltage2 + p2(2);
m2 = p2(1)
b2 = p2(2)
R2=corrcoef(Mass,Voltage2);
R2_2 = (R2(1,2))^2
%m and b for Voltage 3
p3 = polyfit(Voltage3,Mass,1); % p3 returns 2 coefficients fitting r = m_1 * x + b_2
r3 = p3(1) .* Voltage3 + p3(2);
m3 = p3(1)
b3 = p3(2)
R3=corrcoef(Mass,Voltage3);
R2_3 = (R3(1,2))^2
%Calculate Uncertainty
Vacc = 4.0801; %accuracy of Load Cell
Uncertainty_1 = Vacc + Vstd1
Uncertainty_2 = Vacc + Vstd2
Uncertainty_3 = Vacc + Vstd3
%LCal_ProcessDir.m
%Inputs directory name and calls process file on each file that matches the pattern
function [Voltage1,Voltage2,Voltage3,Mass,Vstd1,Vstd2,Vstd3] = LCal_ProcessDir(dirname)

9. jfiles = dir('*.csv');
jsize = size(jfiles,1);
Voltage1 = zeros(jsize,1);
Voltage2 = zeros(jsize,1);
Voltage3 = zeros(jsize,1);
Mass = zeros(jsize,1);
Vstd1 = zeros(jsize,1);
Vstd2 = zeros(jsize,1);
Vstd3 = zeros(jsize,1);
for i=1:jsize
[Voltage1(i),Voltage2(i),Voltage3(i),Mass(i),Vstd1(i),Vstd2(i),Vstd3(i)] =
LCal_ProcessFile(jfiles(i).name);
end
end
%LCal_ProcessFile.m
%Inputs file name and calucates Cd and Re with uncertainties
function [Voltage1,Voltage2,Voltage3,Mass,Vstd1,Vstd2,Vstd3] = LCal_ProcessFile(filename)
%read file
myfile = importdata(filename);
%Import Voltage data
V1 = myfile(:,1); %Voltage for test
V2 = myfile(:,2);
V3 = myfile(:,3);
Mass = myfile(1,4)*9.80665/1000; %Mass converted from g to kg
%Calculate absolute value of mean and Standard Deviation of voltages
Voltage1 = mean(V1(1:10000,1));
Voltage2 = mean(V2(1:10000,1));
Voltage3 = mean(V3(1:10000,1));
Vstd1 = std(V1(1:10000,1));
Vstd2 = std(V2(1:10000,1));
Vstd3 = std(V3(1:10000,1));
end
Full Analysis:
%Master script for running Aerodynasty code
%Jordan Rhoads 2013‐12‐04
clc
clear all
%static directory input
dir_to_import = 'matlab';
[Vdc1,Vdc2,Vdc3,Vdc4,T,speed] = ProcessDir(dir_to_import);
%conversions

10. ftm = 0.3048; %ft to meters
%from Pressure Calibration
m4 =62121; %characteristic slope for pressure
b4 =47.439; %characteristic bias for pressure
%from Load Cell Calibration
m1 = 4.4403e+03; %characteristic slope for 1st Load Cell
b1 = 38.7560; %characteristic bias for 1st Load Cell
m2 = 2.9372e+03; %characteristic slope for 2nd Load Cell
b2 = ‐33.0182; %characteristic bias for 2nd Load Cell
m3 = 4.4071e+03; %characteristic slope for 3rd Load Cell
b3 = 21.0659; %characteristic bias for 3rd Load Cell
%constants
rair = 286.9; %constant for air (J/kg*K)
mu = 1.836e‐5; %viscosity @ 750m above sea level (kg/m*s)
Vt = 3.2; %ave v of typical train (m/s)
Pb = (1814/144)*6894.75729; %Absolute Pressure of Wind Tunnel (Pa)
Pt = 92633.6; %Absolute Pressure of Train (Pa)
l = (4.08875/12)*ftm; %Width of Model Train (m)
A = .01444463; %surface area of front of train (m^2)
%calculate
Dm = m1.*Vdc1+b1+m2.*Vdc2+b2+m3.*Vdc3+b3; %Drag of model
dp = (m4.*Vdc4+b4); %pressure difference (Pa)
rho = rair.*T/Pb; %density of air (kg/m^3)
V = sqrt(2.*dp./rho); %calculated velocity (m/s)
Re = V*l.*rho/mu %Reynolds Number
Cd = Dm./(.5*rho.*(V.^2)*A) %Drag Coeff
%plot Cd vs Velocity
jfiles = dir('*.csv');
jsize = size(jfiles,1);
scatter(V,Cd)
xlabel('Velocity (m/s)')
ylabel('Cd')
title('Cd vs Velocity')
%ProcessDir.m
%Inputs directory name and calls process file on each file that matches the pattern
function [Vdc1,Vdc2,Vdc3,Vdc4,T,speed] = ProcessDir(dirname)
%dirname = uigetdir(dirname);
%dir(fullfile(dirname,'filename*.x*'))
%create empty matrices
jfiles = dir('*.csv');
jsize = size(jfiles,1);
Vdc1 = zeros(jsize,1);

11. Vdc2 = zeros(jsize,1);
Vdc3 = zeros(jsize,1);
Vdc4 = zeros(jsize,1);
T = zeros(jsize,1);
speed = zeros(jsize,1);
%for loop to fill empty matrices
for i=1:jsize
[Vdc1(i),Vdc2(i),Vdc3(i),Vdc4(i),T(i),speed(i)] = ProcessFile(jfiles(i).name);
end
end
%ProcessFile.m
%Inputs file name and calucates the mean voltages and temperature
function [Vdc1,Vdc2,Vdc3,Vdc4,T,speed] = ProcessFile(filename)
%read csv file
myfile = importdata(filename);
%Import Voltage data
V1 = myfile(:,2); %Voltage for Drag 1
V2 = myfile(:,3); %Voltage for Drag 2
V3 = myfile(:,4); %Voltage for Drag 3
V4 = myfile(:,5); %Voltage Pressure
temp = myfile(:,1); %Temperature
speed = myfile(1,7); %speed setting
%Calculate mean of voltages and Temperature
Vdc1 = mean(V1);
Vdc2 = mean(V2);
Vdc3 = mean(V3);
Vdc4 = mean(V4);
T = mean(temp);
end
Figure A.3​: Team FMEA