The Quality of Science and Engineering at the NNSA National Security Laboratories (2013) / Chapter Skim
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5 Modeling and Simulation
5 Modeling and Simulation
Pages 37-44
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From page 37... ...
In aggregate, the scope of the M&S program is very broad, from focused science codes that address a particular physical phenomenon, to integrated modeling codes (IMCs) that provide physical models involving the interactions between multiple physical processes.
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From page 38... ...
While the codes have had an important impact on laboratory science and technology decisions, the code developers acknowledge that they do not yet have a robust predictive capability for a number of key phenomena, such as energy flows and boost, except through the use of problem-dependent calibration from experiments. The predictive capability of a numerical simulation depends on several factors, including the following: • The validity of the physical models encoded in the simulation; • The fidelity of the numerical methods used, including whether the discretization accurately captures the essential features; • The quality of the algorithms -- for example, the choice of preconditioner used in solving linear systems and the numerical accuracy of the algorithms in the face of roundoff errors; and • The quality of the software implementation of the algorithms.
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From page 39... ...
There is a rigorous verification and validation process for MCNP revisions, and the MCNP team is internationally respected for its expertise and achievements in Monte Carlo development. The development of MCNP and the support provided to the large user community is an indirect peer review of the Monte Carlo methods development at the laboratories, and the community's acclaim for these codes couples with the MCNP team's strong record of journal publications and conference proceedings to illustrate the high quality of MCNP development and applications.
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From page 40... ...
For example, production codes routinely run on parallel clusters using 5,000-10,000 processors and some occasionally run with up to 100,000 processors. However, significant challenges loom ahead, starting with underlying computing technology.
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From page 41... ...
There are some positives in the software practices at LLNL, including systematic regression testing, revision control, release processes, and some documentation, such as user manuals and, in at least one case, a developer's manual. However, the codes at both LLNL and LANL lack systematic documentation of the physics, algorithms, and software design and, rely instead on developers themselves to be the repositories of such information.
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From page 42... ...
This does not seem to be an issue for other specialists who are key to the laboratories' M&S, such as physical scientists, applied mathematicians, computational modeling experts, or even computer scientists in selected areas like scientific visualization -- probably because the laboratories offer unusual intellectual challenges for these specialties. The committee also has a general concern about the dilution of resources devoted to at least some aspects of M&S.
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From page 43... ...
for multiple key physical phenomena. The staffing levels of the M&S effort are inadequate to meet the needs of retooling the IMC codes to meet the simultaneous challenges of developing higher-fidelity simulation capabilities, meeting expanded mission requirements, and changing the algorithms and software architecture of the codes to respond to the disruptive changes in computer architecture expected to occur over the next decade.
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From page 44... ...
Recommendation 5.2. Given the increasingly important role that the IMCs will play in certification of the stockpile in the absence of testing, the NNSA should undertake a detailed assessment of the needs for simulation and modeling over the next decade and implement an adequately funded execution plan to meet the challenges outlined in Finding 5.5.
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