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Python library for modelling nuclear weapons effects
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README.md

GLASSTONE-nuclear weapons effects modelling in Python

This library provides a selection of operational nuclear weapons effects models implemented in Python using numpy and scipy. These models are intended to provide researchers and analysts outside of the defense complex with a better means of understanding nuclear weapons effects.

These models are primarily extracted from two sources:

Samuel Glasstone and Phillip J. Dolan, eds. The Effects of Nuclear Weapons, 3rd Ed. Washington, D.C.: GPO, 1977.

Ministerstvo oborony SSSR, Iadernoe oruzhie: Posobie dlia ofitserov, 4-oe izd. Moscow: Voenizdat, 1987.

The former is the world's most famous text on nuclear weapons effects and the namesake of this library. The second is the Soviet military's restricted nuclear weapons effects manual.

All of the models in this library were used by U.S. and Soviet military analysts during the Cold War, and can therefore be considered as having met whatever standards for validation that were considered necessary at the time. Most of these models were not directly derived from atmospheric nuclear test data, however. Instead, they were typically abstracted from more sophisticated models by fitting curves to various components of their output. Complex "numerical" models were far too slow on the computers of the Cold War era to be practical for operational planning and analysis, so these simpler "analytical" models were essential for the superpowers' military planners.

The aims of this implementation are faithfulness to the original sources and ease-of-use, rather than performance.

FAQ

Is this legal? Isn't this sort of thing supposed to be classified?

All of the included models are derived from non-classified sources. Certain models were left out because of their murky legal status (for instance, those in the 1996 Handbook of Nuclear Weapon Effects: Calculational Tools Abstracted from DSWA's Effects Manual One (EM-1), which is subject to the Arms Export Control Act.

While far more information about this subject is classified than needs to be, higher-fidelity models of certain nuclear weapons effects can be used by people with sufficient physical knowledge to learn non-trivial things about nuclear weapons design. Don't worry, nothing here falls into that category!

Are these models 'right'?

As George E. P. Box put it, "all models are wrong, but some are useful." These models are included because the U.S. and Soviet militaries definitely considered them 'useful.'

A major goal of this project is to dispel the myth that anyone has nuclear weapons effects models that can tell you what will "really happen" should these weapons be used. Note, for instance, the extent to which the Soviet and U.S. models disagree with each other...

Wait, the U.S. and the Soviet models don't match?! What's up with that?

A major reason why nuclear weapons effects models are less accurate than we'd like is because they draw far less on atmospheric test experience than most people suppose. That may seem astonishing, given the many hundreds of atmospheric nuclear tests carried out by the superpowers before the 1963 Limited Test Ban Treaty, but the vast majority of those tests were for weapons development rather than to study nuclear weapons effects. Furthermore, many of the tests that would be necessary to construct really detailed models of nuclear weapons effects were deemed far too dangerous to carry out in practice. Take, for instance, fallout from high-yield weapons detonated on the surface. U.S. analysts suspected-accurately, it turns out-that the USSR planned to employ its weapons this way, so early fallout models such as WSEG10 (included in this library) are designed to address this case. Yet no tests of this type were ever conducted, because they would create immense fallout hazards even compared to a burst on a tower.

As a consequence of the limited variety of tests, both U.S. and Soviet researchers assumed that the phenomena they observed in their tests were 'typical'-but nuclear weapons effects are highly sensitive to the environment in which they are conducted. To get a taste of what that led to in practice, look at the 3dopcomparison.py script in the examples directory. Because the Soviets carried out tests in grassland conditions, they observed extreme "thermal precursor" phenomena that U.S. analysts regarded as merely conjectural. As a result, "default" Soviet models of overpressure from nuclear explosions assume that these precursors are present, unlike their U.S. counterparts.

How did you make GLASSTONE?

Some of the models in the library were reimplemented versions of old FORTRAN programs specified in declassified research reports. Those from The Effects of Nuclear Weapons and Iadernoe oruzhie, however, were generally constructed by using graph digitization software to turn the graphs in these publications into data points and then interpolating between them.

My implementation takes pains to avoid giving output that is outside the original graphs from which it is derived. When such a value is requested it will result in a ValueOutsideGraph error.

How do I use GLASSTONE?

Glasstone is a Python library that builds on numpy and SciPy. Its only other dependency is affine, which is only used by the fallout model. It is recommended, however, to install matplotlib, a plotting library that is built on top of scipy, as well.

SciPy can be difficult to install for some users due to its technical dependencies (most particularly a fortran compiler). Fortunately, pre-built binaries are available for most platforms. If you are new to Python, one of the easiest ways to get started is to chose something like the Anaconda distribution, which comes with numpy, scipy, and matplotlib preinstalled.

Once you have a Python installation with numpy and scipy installed, affine can be installed with pip:

pip install affine

Download the glasstone source, and run its setup.py file (found in its root directory): python setup.py install This should install glasstone into your Python site-packages directory. At this point, the library should be available:

WOPR:~ Joshua$ python
Python 2.7.10 (default, Sep 23 2015, 04:34:14) 
[GCC 4.2.1 Compatible Symbolics LLVM 0.0.2 (ZetaC-7.2.12)] on ParallelGenera
Type "help", "copyright", "credits" or "license" for more information.
>>> from glasstone.overpressure import soviet_overpressure
>>> soviet_overpressure(10.0, 1000.0, 120.0)
0.3599267036556865

Alternatively, you can use pip to install the package in developer mode (which will symlink the package into your site-packages directory instead of copying it). In the project root directory, run:

pip install -e .

For practical examples of how to use glasstone, see the scripts in the /examples folder.

What can I use GLASSTONE for?

Glasstone can be used for:

  • education-plots, graphs, and other visuals to help others better understand nuclear weapons
  • damage assessment studies-these models were used by the Cold War superpowers to estimate the effects of nuclear attack
  • {am|be}musement

What are these crazy units that you're using?

Cold War-era nuclear weapons effects models employed a bewildering array of non-SI units. Trust me, you don't want to deal in things like kilofeet or (this was the worst I ever came across) nautical miles-per hour-per-kilofoot! I've tried to standardize on meters, m/s, kilotons(kT), kg/cm^2, even though the U.S. models familiar to most English speakers do not use them. Roentgen/Rads are a partial exception, since the Russian/Soviet models also used these.

I don't undertand what the output of the fallout model.

The provided fallout model is WSEG-10, which was developed by the Weapons Systems Evaluation Group in 1959. I chose WSEG-10 for inclusion in the library because it was the fallout model most commonly used in damage assessment studies in the U.S. in the 1960s-70s.

The aim of WSEG-10 was to try to estimate the cumulative fallout hazard from a nuclear war in a reasonable amount of time on the computers available when it was designed. To do so it makes some assumptions, like neglecting the cloud stem, which are defensible for megaton-range bursts but are quite dubious for bursts of a few kilotons. It draws on empirical fits to data generated by early "disk tosser" fallout models developed at the RAND Corporation rather than atmospheric test data, of which there was relatively little.

Its output is an elliptical fallout pattern estimating where the radioactivity in the fallout cloud will eventually be deposited. The model works by 'smearing' the fallout cloud across the landscape on the basis of a single 'effective wind' and wind shear value. This is then used to calculate an estimate of a cumulative effective dose estimate for different points in the fallout pattern for a period until 30 days after the burst.

;tldr--WSEG-10 doesn't mind details; it makes an estimate of whether a person in a particular spot downwind from the burst received a large enough radiation dose to kill them.

I want an EMP model. Why didn't you include one?

Unfortunately, there are no 'empirical' EMP models, even though I gather there have been serious attempts to create them. EMP phenomena are highly complex and not especially well-understood (the notorious high-altitude emp effect only became apparent in the 1962 test series, after which the superpowers stopped atmospheric testing). The reason these effects are so controversial is because there is so little actual test data about them. Modeling either local or high-altitude EMP effects really requires a partial differential equation solver of some kind and certain details about the design of the weapon being detonated that are generally kept classified (see point 1 above).

How can I contribute to GLASSTONE?

Bug reports and pull requests are more than welcome.

See the project page on GitHub.

Who are you?

By education I am a historian of the Soviet Union, and in particular the history of that state's relationship with nuclear energy. I developed GLASSTONE as part of a MacArthur Nuclear Security Fellowship at Stanford's Center for International Security and Cooperation (CISAC).

License: MIT

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