ENGR 101 | University of Michigan

Project 2: Siting a Wind Farm

Progression of expected wind turbine evolution to deeper water. Photo source: National Renewable Energy Laboratory.

Project Checkpoint and Project Partnership Registration Deadline: Tuesday, February 20, 2024
Project Due: Tuesday, March 5, 2024
Late Submissions Due: Tuesday, March 12, 2024

Engineering intersections: Naval Architecture & Marine Engineering/Computer Science

This project provides an opportunity to work with data in files, automating an analysis, and producing a set of graphs to support your analysis.

The autograded portion of the final submission is worth 100 points and the style and comments grade is worth 10 points.

You may work alone or with a partner. Please see the syllabus for partnership rules.

Educational Objectives and Resources

This project has two aspects. First, this project is an educational tool within the context of ENGR 101, designed to help you learn certain programming skills. This project also serves as an example of a project that you might be given at an internship or job in the future; therefore, we have structured the project description in way that is similar to the kind of document you might be given in your future professional capacity.

Educational Objectives

The purpose of this project is to challenge you to perform a detailed analysis of a large data set and generate summary graphics to support your analysis. A strength of the MATLAB programming language is that it can support data analysis/display of complex data sets. Producing visual aids and interpreting visual data is a critical engineering skill.

This project also touches on two high level concepts that are common in engineering: design constraints and engineering graphics. A brief discussion these concepts is included below, and you can read more about them in the Appendix.

Design Constraints

A design constraint is, literally and simply, a limitation on some aspect of whatever it is you are trying to create. Design constraints should not be arbitrary; they should address a specific concern about how the product or service will work or be used. Some examples of design constraints are shown below:

A design constraint should always be phrased such that when you evaluate the design, you can say that the design meets or does not meet the constraint. It is a yes/no, or true/false, situation.

Engineering Graphics

An engineering graphic is any picture, figure, graph, or video that conveys your engineering information to someone else. They include, but are not limited to, engineering drawings, which are highly accurate drawings used to manufacture items such as circuit boards, gears, 3D printed parts, etc.

Engineering graphics are incredibly important. You can often convey something much faster and clearer if you use a picture or a video. All the project specifications for this class would be much harder to understand without any sort of graphics to explain what’s going on! Figure 1 shows an example of an engineering graphic that was created with MATLAB.

Figure 1. A single figure produced in MATLAB that contains multiple and different plots and a table of data. This figure ensures that related information (the individual graphs) are grouped together and viewed at the same time. Image Source: Laura Alford.

Project Roadmap

This is a big picture view of how to complete this project. Most of the pieces listed here also have a corresponding section later on in this document that goes into more detail.

• Read through the project specification (DO THIS FIRST)
  • Read the contents on the left so you understand the organization of these specs.
  • Understand the data sources/files and how to use them; go to office hours if you do not.
  • Understand the project tasks at a high level: what functions/scripts create what data/images? Go to office hours if you are not sure about anything.
  • Understand the algorithms provided for the different project tasks; go to office hours if you have any questions.
  • Sketch out a plan for how you want to write your program to implement the project tasks. Show your plan to course staff during office hours so we can help you more efficiently!
• Prepare your workspace
  • Download all data sources files/functions
  • Download all provided starter code
  • Download all provided test scripts
  • Put all of this stuff in the same folder/directory on your computer (otherwise your program won’t be able to run)
• Register your partnership on the Autograder (if you are going to work with a partner)
• PROJECT CHECKPOINT: Implement and test the helper function analyzeWindFarm, including:
  • Correctly evaluating Constraint 1
  • Correctly evaluating Constraint 2
  • Correctly evaluating Constraint 3
  • Correctly evaluating Constraint 4
  • Correctly evaluating Constraint 5
• Implement and test remaining function makePlots, including:
  • Correctly creating Plot 1
  • Correctly creating Plot 2
  • Correctly creating Plot 3
  • Correctly creating Plot 4
  • Correctly creating Plot 5
• Double check style and commenting. See the Submission and Grading section for information on style grading.
• Submit all files to the autograder.

Suggested Project Timeline

Below is a suggest project timeline that you may use. You can adjust the dates around any commitments or classwork that you have going on in other courses.

Things to Know Before You Get Started

Tips and Tricks

Here are some tips to (hopefully) reduce your frustration on this project:

• Your functions must be general purpose – in other words, they should work correctly with any provided buoy data, regardless of the number of measurements and chosen constraint values. This makes it possible to process any given buoy location on the planet. Your functions only need to handle a single buoy location and a given set of parameters, though.

• Make sure to place all of your data sources (the .csv files) in the same directory as the functions and test scripts.

• Remember, that you can tell functions like csvread and readmatrix where to start reading data. You might need to skip some rows or columns to get the data that you need.

• Take care to organize your data properly in MATLAB after you read it! Naming your variables appropriately (i.e. giving the variable a name that represents exactly what data it has) helps with the organization of your program.

Reading Data from .csv Files

The data for this project is in .csv files. MATLAB has different built-in functions for reading data from .csv files. See the Data Sources/Files section for more details on the .csv files that are used in this project.

Error using dlmread (line ##)
Mismatch between file and format character vector.
Trouble reading 'Numeric' field from file (row number #, field number #) ==>

Error in csvread (line 48)
    m=dlmread(filename, ',', r, c);

Submission and Grading

This project’s deliverables are two MATLAB .m files. See the Deliverables section for more details.

After the due date, the Autograder portion of the project will be graded in two parts. First, the Autograder will be responsible for evaluating your submission. Second, one of our graders will evaluate your submission for style and commenting and will provide a maximum score of 10 points.

Submitting prior to the project deadline

Submit the .m files to the Autograder (autograder.io) for grading. You do not have wait until you have all of the files ready to submit before you submit to the Autograder for the first time. In fact, we recommend that as you complete tasks for this project, you should continually submit those files to the autograder for feedback as you work on the project. Just remember to eventually have at least one submission that includes all of your completed deliverables!

The autograder will run a set of public tests - these are the same as the test scripts provided in this project specification. It will give you your score on these tests, as well as feedback on their output.

The autograder also runs a set of hidden tests. These are additional tests of your code (e.g. special cases). You still see your score on these tests, and you will receive some feedback on any cases that your code does not pass.

You are limited to 5 submissions on the Autograder per day. After the 5th submission, you are still able to submit, but all feedback will be hidden other than confirmation that your code was submitted. The autograder will only report a subset of the tests it runs. It is up to you to develop tests to find scenarios where your code might not produce the correct results.

You will receive a score for the autograded portion equal to the score of your best submission.

Your latest submission with the best score will be the code that is style graded. For example, let’s assume that you turned in the following:

Then your grade for the autograded portion would be 100 points (the best score of all submissions) and Submission #3 would be style graded since it is the latest submission with the best score.

Please refer to the syllabus for more information regarding partner groups and general information about the Autograder.

Here is the breakdown of points for style and commenting (max score of 10 points):

• 2 pts - Each submitted file has Name, Partner Uniqname (or “none”), Lab Section Number, and Date Submitted included in a comment at the top

• 2 pts - Comments are used appropriately to describe your code (e.g. major steps are explained)

• 2 pts - Indenting and white space are appropriate (including functions are properly formatted)

• 2 pts - Variables are named descriptively

• 2 pts - Other factors (Variable names aren’t all caps, etc…)

Submitting after the project deadline

If you need to submit your project work after the deadline, you can submit to the “Late Submission” project assignment on the Autograder. The late submission assignment includes all of the same test cases as the original assignment, but the points have been adjusted down a small amount per the syllabus’ flexible deadline policy.

Your project score at the end of the semester will be whichever is the higher score between the original project assignment and the late submission assignment. You will never be penalized for submitting to the late submission version of the project.

Autograder Details

There are some specific details to be aware of when working with the autograder. Make sure you understand these so you will have successful submissions.

Test Case Descriptions and Error Messages

Each of the test cases on the Autograder is testing for specific things about your code. Often, it’s checking to see if your programs can handle “special cases” of data, like if a matrix would typically have lots of different numbers in it but your function should still work correctly if the matrix is all zeroes or all negative numbers.

Each of the test cases on the Autograder has a description of what it’s checking for. If your program fails a test case, the Autograder will sometimes be able to give you some advice on how to go about debugging your code. It’s like you have a friendly GSI or IA giving you immediate help!

Suppressing Output

The test cases scripts discussed in these project specifications display output in the command window, but your implementations for all of the tasks should not display anything in the command window when they run. Suppress all output within functions by using semicolons, as is best practice.

Project Overview

Background and Motivation

Now that our settlements on Proxima b have been established for several decades and there are sufficient mining and manufacturing resources, we can start thinking about producing and installing more environmentally friendly sources of power. An obvious choice is wind power. However, this planet is mostly water – most of the accessible land is already given over to farming, industry, and cities (Fig. 2). Consequently, the population decides to install an offshore wind farm (Fig. 3).

Figure 2. Distribution of land masses on the Proxima b planet (green = land, blue = water). The planet is only 29% land, and all of this is already in use for farms, industry, and cities.

Figure 3. An offshore wind farm off the coast of Germany. Offshore wind farms must withstand very harsh environmental conditions: winds, waves, and corrosion from salt water. (Photo source: Wikimedia)

Your engineering company is hired to design a tool to aid in locating the optimal location of this offshore wind farm. The wind turbine company you are partnering with tells you there are 5 design constraints on the location of an offshore wind turbine:

1. The global-model-based average wind speed at the proposed site must be within a specified minimum-maximum range
2. The global-model-based average wave height at the proposed site must be less than a specified maximum value
3. The buoy measurement of the wave height at the proposed site must be less than the specified maximum wave height in constraint #2 for a given percentage of the time (the higher the percentage, the more conservative is the constraint)
4. The deck height of the wind turbine’s base must be higher than any potential rogue waves, as estimated by the buoy-measured wave heights.
5. The standard deviation of the buoy wave height measurements must be less than 5% of the average global wave height.

To begin your investigation, your company deploys several buoys at locations you believe may be viable locations for a wind farm… but whether these locations are suitable for a wind farm depends on actual environmental conditions and the values that are assigned to the constraints. The buoys have gathered data for a year, and now you must process the buoy data to determine whether any of these locations are suitable for a wind farm.

Your Job

Your job is to evaluate a single potential wind farm location (i.e. a single buoy location). You will do this by calculating whether the location passes all of the assigned constraints. You will also produce a detailed and clear figure that summarizes each of the environmental conditions at the single potential wind farm location.

Data Sources/Files

These sections describe the different sets of data you have available for implementing and testing your programs for this project. Unfortunately, the data sources come from different contractors that your company works with, so you’ll want to spend some time understanding the different formats and layouts of the data in the data files before you start to work with the data itself.

Latitudes and Longitudes

Proxima b has latitudes and longitudes for navigation just like on Earth (Fig. 4).

Figure 4. A map of Proxima b showing latitude and longitude lines, similar to those on Earth.

A set of standardized latitude and longitude values is provided to you. Click the filenames to download the files:

1. lat.csv - latitude in degrees
2. lon.csv - longitude in degrees

Global Models of Wind Speed and Wave Height

The global-model-based average wind speed is the estimated average wind speed at a given latitude-longitude coordinate based on a mathematical model of the wind speed across the planet. These types of models (such as the Fleet Numerical Meteorology and Oceanography Center (FNMOC) Hybrid Coordinate Ocean Model) are incredibly useful in any design process as they can provide a prediction of wind speeds at locations where ground truth data does not exist (ground truth data is measured data we trust), so we can use them as a first guess for our design.

Similarly, the global-model-based average wave height is the estimated average wave height at a given latitude-longitude pair based on a mathematical model of the wave heights across the planet.

These global models can be shown graphically as a function of latitude and longitude, as shown in Fig. 5.

Figure 5. Examples of the mathematical models of average (mean) wind speed [left image] and average (mean) wave height [right image] across Proxima b. The white areas in the wave height plot correspond to land, hence there is no wave height data for those locations.

You have been given access to a set of test data for the global wind and wave data to use in testing your program. Click the filenames to download the files:

• windSpeedTestCase.csv - Global-model-based average wind speed data; rows correspond to the latitudes in the lat.csv file, columns correspond to the longitudes in the lon.csv file

• waveHeightTestCase.csv - Global-model-based average wave height data; rows correspond to the latitudes in the lat.csv file, columns correspond to the longitudes in the lon.csv file; NaN (Not-a-Number) corresponds to a lat-lon combination that is on land

Buoy Data

Buoys are used to collect different kinds of data related to water: temperature, wave height, wind direction, salinity, oxygen levels, etc. Figure 6 shows a buoy being deployed in the Great Lakes to measure data.

Figure 6. The National Oceanic and Atmospheric Administration (NOAA) includes the Great Lakes Environmental Research Laboratory (GLERL). GLERL has an Observing Systems and Advanced Technology (OSAT) branch which develops and operates technology that supports GLERL scientific research, meets emerging infrastructure needs, and provides environmental awareness to stakeholders. Here, you can see an OSAT buoy that is being deployed in Lake Michigan to gather data. (Photo source: NOAA GLERL OSAT)

The buoys each record their location and a set of time series data, all saved in a single .csv file. Here is an example of a file created by the buoy data sensing system:

The buoys have recorded their location as an index value for the latitude and longitute vectors in the lat.csv and lon.csv files. For example, this buoy recorded its location as:

Therefore, this buoy’s latitude is the value of the 18th element of the latitudes vector, and this buoy’s longitude is the value of the 75th element of the longitudes vector.

The buoy’s measured data includes:

• wave height (in meters)
• mean wind direction (in degrees from true north)
• mean water temperature (in Celsius)

You have been given access to a set of test data from one of the buoys to use in testing your program. Click the filename to download the file:

• buoyTestCase.csv - buoy data; includes buoy location (lat-lon corresponds to the INDEX of the lat-lon vectors, not the lat-lon in degrees) and time series data for wave height, mean wind direction, and mean water temperature

Deliverables

This project has two deliverables:

Starter Code and Test Scripts

The starter code files contain some code that is already written for you, and you will write the rest of the code needed to implement the tasks described in the Project Task Description section. The starter code files all have _starter appended to the file’s name so that if you want to download a fresh copy of the starter code, you won’t accidentally overwrite an existing version of the file that may have some code you have written in it. Remember to remove the _starter part of the filename so that your programs will run correctly!

Click the filenames to download the starter code:

• analyzeWindFarm_starter.m
• makePlots_starter.m

Here are some test scripts that you can use as a base for testing your functions. Each test script includes at least one test case as an example. You should add more test cases, using the example test case(s) as a template, to verify that your functions are working correctly.

Click the filenames to download the test scripts:

• UnitTest_analyzeWindFarm.m
• UnitTest_makePlots.m
• RunComparisonsForPlots.p

The RunComparisonsForPlots script provides detailed feedback on each aspect of the plots that you will be graded on. The script also produces a file named comparison_plots.png that shows a side-by-side comparison of your plots vs. the correct plot that the specs are asking for.

Project Task Description

There are 2 primary tasks for your program:

1. Evaluate the constraints on the proposed wind farm location
2. Produce a figure that summarizes the environmental conditions at the proposed location.

Although this project is only going to look at a single potential location for a wind farm, in general you would be evaluating a lot of different locations. Therefore, it makes sense to write a function for each task that will load the data sources and complete the task, with the eventual end goal being that the functions could be used by someone else to evaluate a lot of potential wind farm locations.

Task 1: Evaluate Design Constraints

Your company has a location that they think will be a good place to build an offshore wind farm. You need to check this location against the five design constraints that the wind turbine company says must be met.

The five design constraints are evaluated using specific parameters along with the data sources:

Before we start trying to determine an algorithm for this task, we should decide on the function definition so that we can talk about how to pass in all of the necessary data to the function.

Function definition and description: analyzeWindFarm

The analyzeWindFarm function will evaluate the design constraints for a given location. The function header and comments are provided for you in the file called analyzeWindFarm_starter.m. Don’t forget to remove _starter from the filename before you try to call this function.

function [ c1, c2, c3, c4, c5 ] = analyzeWindFarm( filenameWind, ...
         filenameWave, filenameBuoy, windSpeedMin, windSpeedMax, ...
         waveHeightMax, waveHeightRisk, deckHeight )

%   Function to complete Task 1. Evaluates the 5 constraints on the location of a
%   wind farm.
%
%   parameters: 
%          filenameWind: a string that names the file containing the 
%                        global-model-based average wind speed 
%                        (i.e. 'windSpeedTestCase.csv')
%          filenameWave: a string that names the file containing the 
%                        global-model-based average global wave heights 
%                        (i.e. 'waveHeightTestCase.csv')
%          filenameBuoy: a string that names the file containing the time 
%                        series of wave heights measured by the buoy          
%                        (i.e. 'buoyTestCase.csv')
%          windSpeedMin: for constraint 1 -- minimum wind speed (m/s)
%          windSpeedMax: for constraint 1 -- maximum wind speed (m/s)
%         waveHeightMax: for constraints 2 & 3 -- maximum wave height (m)
%        waveHeightRisk: for constraint 3 -- maximum wave height risk (%)
%            deckHeight: for constraint 4 -- height of the deck that supports 
%                        the turbine base (m)
%
%   return values:
%                    c1: boolean values corresponding to whether the wind 
%                        farm location passes constraint #1
%                    c2: boolean values corresponding to whether the wind 
%                        farm location passes constraint #2
%                    c3: boolean values corresponding to whether the wind 
%                        farm location passes constraint #3
%                    c4: boolean values corresponding to whether the wind 
%                        farm location passes constraint #4
%                    c5: boolean values corresponding to whether the wind 
%                        farm location passes constraint #5

Algorithm for the analyzeWindFarm function

The analyzeWindFarm function will use MATLAB’s “compound return” capability to return 5 true or false logical values corresponding to whether each of the 5 design constraints is met. The function must return the values in the same order that they are listed below.

1. Constraint 1: Is the global-model-based average wind speed at the proposed location within the min-max range (parameters 4 & 5)?
2. Constraint 2: Is the global-model-based average wave height at the proposed location < max value (parameter 6)?
3. Constraint 3: Is the local buoy measurement of wave height < max value (parameter 6) at least XX% (parameter 7) of the time?
4. Constraint 4: Are all potential rogue wave heights < deck height (parameter 8)?
5. Constraint 5: Is the standard deviation of the buoy wave height measurements < 5% of the global-model-based average wave height?

The algorithm for evaluating these constraints will make reference to the parameters of the analyzeWindFarm function:

Import Data

This function should be able to evaluate the design constraints for different sets of data (wind/wave data, buoy location/data), so the first step is to load data from:

• the file containing the global-model-based average wind speed (parameter #1)
• the file containing the global-model-based average wave heights (parameter #2)
• the file containing the buoy information (parameter #3); don’t forget that you will have to access this file twice: once to get the location, and once to get the time series data.

See the Data Sources/Files section for more information on these files, including how to download sample files.

Constraint 1: Global-Model-Based Average Wind Speed

To evaluate this constraint, first find the global-model-based average wind speed corresponding to the buoy location (lat/lon index pair), and then evaluate:

Return this answer as the first value in your compound return of the function.

Constraint 2: Global-Model-Based Average Wave Height

To evaluate this constraint, first find the global-model-based average wave height corresponding to the buoy location (lat/lon index pair), and then evaluate:

Return this answer as the second value in your compound return of the function.

Constraint 3: Buoy Measured Wave Height Exceedance

Constraint 2 checked only if the average wave height was less than a given maximum. We now need to verify how often the actual wave heights are less than that given maximum. The actual wave heights must be less than the given maximum for at least a given percentage of time (a measure of risk). For example, the waves measured by the buoy must be less than the maximum wave height for, say 80% of the time.

To determine whether this location meets Constraint 3, evaluate:

Return this answer as the third value in your compound return of the function.

Constraint 4: Risk of a Rogue Wave

Another danger to the wind farm is a rogue wave at the proposed location. Rogue waves are very large waves that appear suddenly and without warning; they can cause extreme damage to structures and put human lives at risk. A rogue wave is defined as a wave that is twice as high as the significant wave height (significant wave height is what is recorded by the buoy). If the deck that supports the wind turbine is higher than the rogue wave, then the risk of damage and potential loss of life is minimized. Therefore, we need to check whether the deck height is greater than the height of any potential rogue waves. To evaluate this constraint, multiply all the buoy-measured wave heights by 2 to estimate the height of “rogue waves”:

then evaluate:

Return this answer as the fourth value in your compound return of the function.

Constraint 5: Standard Deviation of Buoy Wave Height

The proposed location needs to have wave heights that are fairly consistent. The standard deviation of the buoy measured wave heights can be used to quantify consistency. To evaluate this constraint, first calculate the standard deviation of the buoy measured wave heights. Then, evaluate:

Return this answer as the fifth value in your compound return of the function.

Testing your analyzeWindFarm function

To test your analyzeWindFarm function, run the test script:

>> UnitTest_analyzeWindFarm

If your function is working correctly, you should have the following values for the five return variables:

Task 2: Summarize Environmental Conditions

You also need to produce a figure visually summarizing the conditions at the proposed site of the wind farm. This visual summary must include five plots; see Fig. 7 for an example.

Figure 7. A single figure created by MATLAB that visually summarizes the conditions at the proposed site of the wind farm. Note that it starts at the global level and then works its way down to the local level.

The five plots in the visual summary are:

1. Map of global-model-based average wind speed across the planet
2. Map of global-model-based average wave height across the planet
3. Map of all potential wind farm locations (i.e. all lat-lon combinations) based only on constraints 1 & 2 with the proposed location (i.e. the buoy’s lat-lon location) marked with a red square
4. Histogram of buoy measured wave heights
5. Time series of buoy measured wave heights compared to average wave height at that location

Plots 1 & 2 are general, high-level maps that would be the same for all potential buoy locations. You include them here to give context to your audience. Plot 3 shows your audience where the potential wind farm is located (it corresponds with the buoy location, remember). Plots 4 & 5 summarize the local wave conditions at that wind farm/buoy location.

Function definition and description: makePlots

The makePlots function will create the visual summary and save it as a file named environmentalSummary.png. The function header and comments are provided for you in the file called makePlots_starter.m. Don’t forget to remove _starter from the filename before you try to call this function.

function [  ] = makePlots( filenameWind, filenameWave, filenameBuoy, ...
    windSpeedMin, windSpeedMax, waveHeightMax )

%   Function to complete Task 2. Creates a figure with multiple plots that 
%   summarizes the environmental conditions for a wind farm.  Saves figure as 
%   a .png file.
%
%   parameters: 
%          filenameWind: a string that names the file containing the 
%                        global-model-based average wind speed 
%                        (i.e. 'windSpeedTestCase.csv')
%          filenameWave: a string that names the file containing the 
%                        global-model-based average global wave heights 
%                        (i.e. 'waveHeightTestCase.csv')
%          filenameBuoy: a string that names the file containing the time 
%                        series of wave heights measured by the buoy          
%                        (i.e. 'buoyTestCase.csv')
%          windSpeedMin: for constraint 1 -- minimum wind speed (m/s)
%          windSpeedMax: for constraint 1 -- maximum wind speed (m/s)
%         waveHeightMax: for constraint 2 -- maximum wave height (m)
%
%   return values: none
% 

Algorithm for the makePlots function

To produce the summary figure in Fig. 7, use the subplot function to put multiple plots in one figure. Arrange your plots as shown in Fig. 8 (plot numbers correspond to those listed at the beginning of the Task 2 section.

Here are the details that are required for each plot:

1. Map of average wind speed across the planet (measured globally)
   1. Use the contourf function to create a map of the average wind speed vs. latitude and longitude (tip: use meshgrid())
   2. Do NOT plot the mesh lines (e.g. set 'LineStyle','none')
   3. Use the parula colormap
   4. Make sure all labels, color bars, legends, etc. match Fig. 7; however, use default settings for ticks and axis limits
   5. Use default MATLAB font sizes
2. Map of average wave height across the planet (measured globally)
   1. Use the contourf function to create a map of the average wave height vs. latitude and longitude (tip: use meshgrid())
   2. Do NOT plot the mesh lines (e.g. set 'LineStyle','none')
   3. Use the parula colormap
   4. Make sure all labels, color bars, legends, etc. match Fig. 7; however, use default settings for ticks and axis limits
   5. Use default MATLAB font sizes
3. Map of all potential wind farm locations based on constraints 1 & 2 with potential location of wind farm (i.e. the buoy location) marked with a red square
   1. Use the contourf function to create a map of all potential wind farm locations based on constraints 1 & 2 vs. latitude and longitude
      1. Latitude-longitude combinations that meet BOTH constraints should be given a value of 1
      2. Latitude-longitude combinations that do NOT meet both constraints should be given a value of 0
   2. Do NOT plot the mesh lines (e.g. set 'LineStyle','none')
   3. Use the scatter function to plot the buoy location with a red square with a marker size of 200 and a linewidth of 3
   4. Use the gray colormap in reverse
   5. Make sure all labels, color bars, legends, etc. match Fig. 7; however, use default settings for ticks and axis limits
   6. Use default MATLAB font sizes
4. Histogram of buoy measured wave heights
   1. Show the grid
   2. Make sure all labels, color bars, legends, etc. match Fig. 7; however, use default settings for ticks and axis limits
   3. Use default MATLAB font sizes
5. Time series of buoy measured wave heights
   1. Plot the buoy measured wave heights vs. time
   2. Plot the average wave height (from the global measurements for the buoy’s lat-lon) across all time
   3. Show the grid
   4. Make sure all labels, color bars, legends, etc. match Fig. 7; however, use default settings for ticks and axis limits
   5. Use default MATLAB font sizes

You may use the default MATLAB settings for anything that is not explicitly specified above. After the figure has been created, with the five plots, your program should save the figure as environmentalSummary.png.

Testing your makePlots function

To test your makePlots function, run the test script:

>> UnitTest_makePlots

Compare the figure that is created to Fig. 7. When you compare your environmentalSummary.png to the one shown in Fig. 7, it’s okay if there are small differences, e.g. tick mark spacing is slightly different, aspect ratio of the graphs is slightly different, overall size is slightly different. MATLAB renders things a little differently depending on the computer. We will grade only those details explicitly specified in the algorithm for Task 2.

You can also run the RunComparisonsForPlots script to generate a side-by-side comparison of each individual item your plots are graded on. The RunComparisonsForPlots script creates a file named comparison_plots.png, which you can open in any program that opens images. Here is an example of running the RunComparisonsForPlots script and part of the comparison_plots.png image that shows a plot that is not quite correct:

>> RunComparisonsForPlots

Figure 9. Part of the comparison_plots.png. This shows that Test Case 12 has failed, in this case because the red square does not have the correct line width.

FAQs

Here are frequently asked questions about this project. If you are stuck on something, start here!

General FAQs

What is meshgrid? How does it work?

If you want to plot a heatmap or surface (something which has a z value for each x & y combination), MATLAB needs us to specify every x & y value for which we want to plot z. Consider the (x,y) combinations shown below:



In this figure, there are discrete values for x and y. To represent all of the combinations of x & y, we need two matrices, X & Y, where X is a matrix where each row is a copy of the x-values , and Y is a matrix where each column is a copy of the y-values . We can create these matrices by using meshgrid:

x = -1:3
y = 1:4
[X,Y] = meshgrid(x,y)

[ X , Y ] = meshgrid( x , y ) returns 2-D grid coordinates based on the coordinates contained in vectors x and y . X is a matrix where each row is a copy of x , and Y is a matrix where each column is a copy of y . The grid represented by the coordinates X and Y has length(y) rows and length(x) columns.

The X and Y matrices now allow us to create 3D graphs such as contourf due to the new sizes of the X and Y variables.

See the Beam Deflection Lecture for more information and an example.

Task 1 FAQs

Task 2 FAQs

Appendix: Engineering Concepts

Here is some more in-depth background information on the two high level concepts used in this project: design constraints and engineering graphics.

Design Constraints

Design and constraints go hand-in-hand (Fig. 10), although constraints are not always evil things that stifle creativity. A design constraint is, literally and simply, a limitation on some aspect of whatever it is you are trying to create. Design constraints should not be arbitrary; they should address a specific concern about how the product or service will work or be used.

Figure 10. A description of the engineering design process. Note that the top of the cycle (where you would start) includes the constraints of the process. (Photo Source)

When you evaluate a design, especially a prototype of your design, you must state whether your design meets each of these constraints. Some constraints can be met right away (e.g., choice of programming language), but you won’t know if you meet other constraints (e.g., maximum false positive rate for medical test) until after you have a prototype and start running test cases. Continually checking your design against its constraints is crucial to the design process. If you forget to check your design constraints often, you might invent something cool, spend weeks building it, more weeks checking it, more weeks documenting it, and then find out it’s not what you need because it fails all the constraints. Since you have to check the constraints so many times, it makes sense to (semi) automate the process by writing a computer program to analyze your constraints for you. This way, every time you change something, you can just re-run your computer program to check the constraints.

Engineering Graphics

An engineering graphic is any picture, figure, graph, or video that conveys your engineering information to someone else. They include, but are not limited to, engineering drawings, which are highly accurate drawings used to manufacture items such as circuit boards, gears, 3D printed parts, etc.

Engineering graphics are incredibly important. You can often convey something much faster and clearer if you use a picture or a video. All the project specifications for this class would be much harder to understand without any sort of graphics to explain what’s going on!

This video summary of James Cameron’s dive to the Mariana Trench is an excellent example of how to describe a highly scientific expedition in just 2 minutes using small words. In this article on the potential of offshore renewable energy production, figures are used liberally to explain where wind turbines could be effective (shown here as Fig. 11), different types of wind turbines (shown here as Fig. 12), and concepts of future wind turbines (shown here as Fig. 13).

Figure 11. United States offshore wind resource by region and depth. Photo source: Wind resource estimates developed by AWS Truepower LLC. Web: http://www.awstruepower.com. Map developed by National Renewable Energy Laboratory (NREL). Spatial resolution of wind resource data: 2.0 km.

Figure 12. Different types of offshore wind turbines. Photo source: Bureau of Ocean Energy Management (BOEM).

Figure 13. Concept of an underwater wind turbine that generates energy from ocean currents. Photo source: Bureau of Ocean Energy Management (BOEM).

If you have several graphs that are summarizing a complicated analysis, you can plot them together in one figure (Fig. 14) to improve the final product and to keep the data together.

Figure 14. A single figure produced in MATLAB that contains multiple and different plots and a table of data. This figure ensures that related information (the individual graphs) are grouped together and viewed at the same time. Photo Source: Laura Alford.

Note the differences in all of the visuals. Some are drawings/cartoon-like (Fig. 11), some are renderings from 3D models (Figs. 12 & 13), and some are traditional graphs (Fig. 14). You can use any or all of these types of graphics (and others!) to help communicate your ideas to someone else.