CS382:Fire

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Contents

Fire

Overview

This short unit about the spreading of forest fires is intended to teach some of the basics of using a simple pre-made model/simulation. While there are many benefits to using this model, the ability to physically verify the results proves to be difficult. It turns out that the rudimentary simulation of a wild fire spreading through a forest of varying densities can be implemented in a wide range of tools including NetLogo, AgentSheets, Vensim, Excel, and possibly others. Thus, this single model can teach the basics of simulation techniques like agent modeling, cellular automata, and systems dynamics without requiring students to relearn or rediscover what results to expect and allows them to focus on the methods and the techniques.

Background Reading for Teachers and TAs

WildFire Modeling (wikipedia)

Agent-based modeling and simulation of wildland fire suppression

Reading Assignments for Students

Cellular Automata (wikipedia)

History of Cellular Automata

Reference Material

Lecture Notes

Lecture 1

Lecture 2

Lab

This lab will consist of learning how to use NetLogo's wildfire model to see how minor changes in parameters can, under certain circumstances, producing wildly different results.

The structure of this lab is loosely based on this.

Data should be recorded in a spreadsheet program (i.e. Excel or OpenOffice) for easy graphing.

Process

  1. Open up NetLogo. Go to the model library and under the Earth Science category, open "Fire".
  2. Experiment with different values for the density.
    1. Try 1%, then 99%. Record the results for each of these.
      1. This result is displayed in the "Percent Burned" box on the screen.
    2. Take a guess at what will happen at 60%, then try it. Record your result.
    3. How close was your guess? How close was your lab partner's?
  3. Start taking some more data:
    1. Record the percent burned at 50%, 60%, 70%.
  4. Graph your data.
  5. What does the graph look like?
    1. Possibilities: constant, linear, logarithmic, exponential (see the examples below)
  6. What might you be able to conclude from this?
  7. Run it 10 times at {50,60,70}% and record the average for each set
  8. Now graph the averages
  9. Does it look different?
    1. Will probably be closer to linear.
  10. What new conclusions can you make, if any?
  11. Record results for burning at {50,55,60,65,70}% and graph it when you have all of them.
    1. How did that change your graph?
  12. Again, do this 10 times for {50,55,60,65,70}%, average each, and graph them.
    1. How did that change your graph?

Write-up

Example graphs

Software

Bill of Materials

As long as the students don't try to actually burn down a forest to validate these models, there is no cost for this lab.

Evaluation

CRS Questions

  1. A technique called "systematic dynamical conflagration"
  2. Going out back campus and ....
  3. Coding all properties of trees into a computer program
  4. A technique called "cellular automata"
  1. Automated Telecomune
  2. Tessellation Automata
  3. Biological Automated Simulation
  4. Systems Dynamics
  1. Stephen Wolfram
  2. John von Neumann
  3. Alan Turing
  4. Stanislaw Ulam
  1. ground slope/shape
  2. wind & wind changes
  3. day/night cycle
  4. high air humidity
  1. 128
  2. 256
  3. 512
  4. 1024

Quiz Questions

Fire's Metadata

Scheduling

This should be very early in the semester as it is a fairly simple and short topic. Given its simplicity, it should only be a single week.

Concepts and Techniques

This unit should teach the basics of using someone else's model, as well the importance of conducting multiple runs with different parameters in order to gain an accurate understanding of the effect of the model. The WildFire model used in this unit provides a brief introduction to the concepts of VVA (Verification/Validation/Accreditation). Specifically, a rough notion of how a WildFire spreads can be obtained using this model, but it should be stressed that this model is nowhere near accurate when compared to one would actually happen.

General Education Alignment

Analytical Reasoning Requirement

Abstract Reasoning

From the [Catalog Description] Courses qualifying for credit in Abstract Reasoning typically share these characteristics:

Quantitative Reasoning

From the [Catalog Description] General Education courses in Quantitative Reasoning foster students' abilities to generate, interpret and evaluate quantitative information. In particular, Quantitative Reasoning courses help students develop abilities in such areas as:

Scientific Inquiry Requirement

From the [Catalog Description] Scientific inquiry:

Scaffolded Learning

The NetLogo model(s) in question are easy to use and easy to witness the effect of changing parameters to obtain different results. As well there are fairly clear extensions for the student inclined towards taking those steps.

Inquiry Based Learning

This unit should be able to pique the curiosity of the student, most likely in the form of the question: "Why is that that a single variable can have such a dramatic effect on the output of a system?"

Fire's Mechanics

To Do

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