Research Needs for Magnetic Fusion Energy Sciences - US Burning ...

With this information in hand, physical and computational models would be developed or refined.
We do not yet have all of the basic understanding sufficient to complete this Thrust, so the
program will need to first identify and address missing or inadequate physics through new theoretical
work focused on problems relevant to the case studies. basic theory will also be important
for interpretation of code results. brute computational force by itself will not be sufficient,
and advances in computer size and speed will continue to be needed. innovation in algorithms
and numerics for efficient solution of the computation models are critical for success, requiring
expanded collaborations between fusion scientists and applied mathematicians, similar to those
forged in the scientific discovery through advanced computing (scidac) program. a mixture of
large-scale computational projects with smaller scale, more agile and speculative activities will
also be essential. a particular challenge will be the integration across spatial and temporal scales
and across different regions of the plasma. While work on these problems has begun, much more
needs to be done to analyze the basic physics issues in play and to define appropriate methodologies
for model and software module integration. This problem will be a key element in the planned
Fusion simulation Program. Fusion energy research has a long history of using computation to
advance the science, so the overall challenge is to “scale up” from current efforts while building on
past models of success. Required elements will include the necessary level of software engineering
to make codes robust and usable beyond the core development group, and collaboration with
computer scientists to define practical software frameworks, workflow schemes and data management
systems.
Figure 1. The processes involved in computer modeling are summarized in this diagram by Schlesinger, Simulation
32, (1979) p. 103.
a much more systematic code verification regime is envisioned as part of this Thrust. verification
assesses the degree to which a code correctly implements the chosen physical model and
is essentially a mathematical problem. sources of error include algorithms, numerics, spatial
or temporal gridding, coding errors, language or compiler bugs, convergence difficulties and so
forth. methodologies for verification have been explored and documented extensively in related
fields. These fall into two basic categories — those that look for problems in the code and those
that look for problems in the solution. The first would be handled by traditional software quality
assurance methods. The second will require testing against known analytic solutions, convergence
studies and careful code-to-code comparisons. The latter underscores the importance of
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