# Dirty Bomb Simulation

Stevens Institute of Technology Research Summer 2016 with Alex Wellerstein.

#### ‘What’s a dirty bomb,’ you ask?

Terrorism is a popular topic to evoke instant fear. So is talk of nuclear bombs. Dirty bombs or (Radioactive Dispersion Devices/RDD in super villain terms) are sort of a Catdog of the two. Though purely theoretical, these bombs are a small amount of nuclear material wrapped in conventional explosives (TNT, C4, …) and have the potential to disperse nuclear fallout. A terrorist doesn’t have to be a nuclear scientist nor even have the amount of nuclear material necessary to make a true nuclear bomb.

• AKA Radioactive Dispersion Device (RDD)
• Nuclear material wrapped in conventional explosives
• Easy to make, easy to transport, easy to conceal
• Need much less nuclear material than to make a traditional nuclear bomb
• Purely theoretical

#### The goal was to approximate.

Talking about the possible outcomes and fallout of RDDs is fun and all, visualizing would be so totally wicked! The main problem: there are no current existing mathematical models to understand the radioactive dispersion of a RDD explosion. Ooph.

We wanted to be able to see the effects of the fallout over a geographic region but without the need for intense calculations that other dispersion models require. My task was to create a Javascript library that could be used to create visualizations like this. We took existing models for both dispersion and nuclear bomb and combined them to approximate how a dirty bomb could possibly spread its nuclear material.

The models for the explosion were taken from models describing nuclear explosions. Problems here: most bombs detonated by terrorist are thousands of times less powerful than modern nuclear bombs. Advantage: extensive research has been done to describe metrics like cloud radius and cloud height, both helpful in modeling dispersion.

For the dispersion, we explored many commonly used models; the CISAC, CALPUFF, and Gaussian models were all well researched. We ultimately chose the Gaussian model as it is versatile and the formulas well documented. There are also variations that include radioactive decay factors. Win win win.

Scan scan scan:

• Goal: Javascript library to be used for dirty bomb visualization
• No current models describing dirty bomb explosion
• Combined current models for plume dispersion and bomb explosions
• Modeled the radioactive dispersion with a Gaussian Plume variation.

#### The outcome was small, fast, and of course, an approximation.

The Javascript library is a success! While the data needs fine tuning and research to determine the accuracy, but the models stack up against basic numerical testing for dispersion. Of course, this is all an approximation. We still do not know if the combination of nuclear bomb model is an OK method. Compared to typical modeling programs, like QUIC that can take minutes to run a simulation, our library runs incredibly fast and in a browser, though doesn’t come close in accuracy or features.

Visualizations are ongoing, but the basic version using a Google Maps ‘Heatmap’ appears Gaussian in nature and responds to changing data. Phewf.

The library contains models for dynamically changing winds, urban and rural settings, the most common nuclear materials, nuclear decay dispersion plumes, and much more. Ok, maybe not much.

Ok, last scan:

• Still needs testing against real data or other sources (hopefully we will never have real data)
• Visualizations are in basic stages, but reflect initial goals
• Library contains: dynamic atmospheric conditions, urban/rural settings, common nuclear materials, decay plumes, …

#### What’s next?

Test it out! Contribute! Hopefully this project will be taken on and expanded by future students, or someone in the world will inform me that this assumption is not an assumption that is feasible. Either way, boom!

The github repository!

The npm package!

Resources and sources!

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