Description
Nuclear fusion comes along with great promises of almost limitless
energy with little risks and waste. But it also comes with significant
scientific and technological complexities. Decades of efforts may find
an echo in ITER, an international tokamak being built to address this
challenge. A tokamak is a particular kind of advanced experimental
nuclear fusion reactor. It is a torus-shaped vacuum vessel in which a
hydrogen plasma of very low density is heated up to temperatures
(10-100 millions of degrees Celsius) allowing nuclear fusion reactions
to occur. The torus-shaped plasma radiates light, which is measured in
various wavelength domains by dedicated sets of detectors (called
diagnostics), like 2D cameras observing visible light, 1D arrangements
of diodes sensitive to X-rays, ultra-violet spectrometers... Due to
the torus shape, the plasma is axisymmetric, and like in medical
imaging, tomography methods can be used to diagnose the light radiated
in a plasma cross-section.
For all diagnostics, one can seek to solve the direct or the inverse
problem. The direct problem consists in computing the measurements
from a known plasma light emissivity, provided by a plasma simulation
for example.
The inverse problem consists in computing the plasma light emissivity
from experimental measurements. The algorithms involved in solving
both the direct and inverse problem are very similar, no matter the
wavelength domain.
Like many, the fusion community tends to suffer from a lack of
reproducibility of the results it publishes. This problem is
particularly acute in the case of tomography diagnostics since the
inverse problem is ill-posed and the solution unicity is not guaranteed.
There are also many possible simplifying hypotheses that may, or may
not, be relevant for each diagnostic. In this regard, the historical
uses of the community display a large variety of single-user black- box
codes, each typically designed by a student, and often forgotten or left
as is until a new student is hired and starts all over again.
In this context, a machine-independent, open-source and documented
python library, ToFu, was started to provide the fusion community with
a common and free reference tool.
We thus aim at improving reproducibility by providing a known and
transparent tool, able to efficiently solve both the direct and
inverse problem for tomography diagnostics. It can use very simple
hypothesis or very complete diagnostics descriptions alike, one of the
ideas being that it should allow users to perform accurate
calculations easily, sparing them the need to simplify hypotheses that
are not always valid.
A zero version of tofu, fully operational but not user-friendly
enough, was first developed between 2014 and 2016 when it was used for
published results. Strong with this first proof of principle, a
significant effort was initiated in 2017 to completely re-write the
code with a stronger emphasis on python community standards (PEP8),
version control (Github), performance (cython), packaging (pip and
conda), continuous integration (nosetests and travis), modularity
(architecture refurbishing), user-friendliness (renamings, utility
tools) and object-oriented coding (class inheritance).
This effort is still ongoing to this day and is scheduled to go on for
the next 2.5 years. However, the first milestones have been reached,
and we would like to present the first re-written modules to the
python community, for publicity, advice, feedback, mutually enriching
exchanges and more generally because we feel tofu is part of the large
open-source python scientific community.
The code is composed of several modules: a geometry module, a data
visualization module, a meshing module, and an inversion module. We will
present the geometry module (containing ray-tracing tools, spatial
integration algorithms...) and the data module (making use of matplotlib
for pre-defined interactive figures). Using profiling tools, the
numerical core of the geometry module was optimized and parallelized
recently in Cython making the code more than ten thousand times
faster than the previous version on some test cases. Memory usage has
also been reduced by half on the largest test cases.
see ToFu
We present an open-source parallelized and cythonized python library,
ToFu, for modeling tomography diagnostics on nuclear fusion reactors.
Its functionalities (with realistic examples), its architecture and its
design will be shown.
