Wondering when an ultrafast t-squared fire reaches 2,000 kW? 104 seconds! Or just want to quickly generate a slow t-squared curve to use in FDS? I’ve posted a new web calculator tool to generate t-squared fires, output plots, CSV files, and FDS syntax.

The t-squared fire ramp calculator is available here:

http://www.koverholt.com/t-squared-fire-ramp-calculator/

Please let me know if you find any bugs, would like to give feedback on this tool, or have a request for another web calculator!

The Consolidated Model of Fire and Smoke Transport (CFAST) model is a two-zone fire model developed by NIST. From the NIST website, you can download the Windows version of CFAST, which includes a graphical user interface frontend (CEdit) for the creation and execution of CFAST cases. However, there are no downloadable CFAST binaries or user interfaces for the Mac and Linux platforms; you must compile CFAST yourself.

This guide will help you compile the CFAST executable on Mac OS X and Linux. This is useful for running CFAST cases on Mac or Linux machines, which is especially useful for scripting CFAST runs for optimization problems or running CFAST on a large number of cases in batch mode.

You will need the Intel Fortran compilers (ifort) and a copy of the CFAST source code from the CFAST Google Code site. You can try to use other free compilers, but I find that the Intel compilers are the most compatible and produce the most optimized (fastest) binaries. Note: there are heavily discounted Intel compilers for students.

To download the CFAST source code, the easiest way is to install subversion on your Mac or Linux machine and issue the following command:

svn co http://cfast.googlecode.com/svn/trunk/ cfast

Once you have the CFAST source code on your machine, you can perform the following steps to compile the CFAST executable.

1. In the cfast/CFAST/ directory, edit the makefile_linux file and make the following changes:

• Replace ‘radation’ with ‘radiation’; this is a typo
• Add ‘cyl_conduct.o’, ‘datamodules.o’, and ‘ssHeaders.f’ to the obj_serial section
• Add the following lines to the Object Dependencies section:
• cyl_conduct.o : cyl_conduct.f
• datamodules.o : datamodules.f90
• ssHeaders.o : ssHeaders.f
• Add ‘cyl_conduct.f’, ‘datamodules.f90′, and ‘ssHeaders.f’ to the cfast.o line

Note: The corrected makefile can also be downloaded from here.

2. Copy datamodules.f90 from the cfast/CFAST/Include/ directory to the cfast/CFAST/Source/ directory

3. Change to the cfast/CFAST/Source/ directory

4. Run the command ‘ifort -c datamodules.f90′ to compile the datamodules and iofiles modules

5. Finally, run the command ‘make -f ../makefile_linux intel_osx_64’ to build CFAST

5b. If you are running Linux, the command is ‘make -f ../makefile_linux intel_linux’

Using these steps, I was able to build CFAST on an Intel Macbook Pro running 10.6 (Snow Leopard) as well as a Linux machine running CentOS release 5.6 with the 64-bit Intel Fortran Compiler version 12.0.4.184.

While a large number of wildland fires are burning through West Texas and threatening the safety of life and property of Texans, the fire research group at The University of Texas at Austin is actively working with physics-based computer models and laboratory-scale fire tests to characterize the fuel properties and flame spread characteristics of grassland fuels.

Computer model of grassland fire simulation

At UT Austin, we have been performing small-scale, controlled experiments in our burn structure to determine ignition times and burning rates for grassland fuels as well as intermediate-scale, controlled experiments to determine the fuel and combustion properties of grass fuels, the effects of external wind on ember production, and the heat release rates of grass bunches.

Controlled grass fire test in the UT Austin burn structure

We can utilize the results of the small- and intermediate-scale experiments in full-scale computer simulations of grassland fires using modeling tools such as Wildland-urban interface Fire Dynamics Simulator (WFDS).

Using the results and methods from these controlled experiments along with the help of the wildland fire community, we plan to develop a framework to determine fuel properties of wildland fuels, predict the physics and fire dynamics behavior of wildland fires, and achieve safer conditions for people and property faced with the threat of wildland fire situations.