This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Position Research Title:
Improved Soot and Radiation Modeling in the Fire Dynamics Simulator
In this phase of the project, the work will focus on developing and validating engineering level closures for applications requiring detailed chemistry, including soot formation and oxidation and flame ignition and extinction. The work will involve implementation of a stiff chemistry time integrator and methods for coarse-grained solution of the reacting flow equations, including implementation of an equilibrium solution method for detailed mechanisms, providing flame temperatures for ignition and extinction models. The work will involve formulating an analytical Jacobian of the reacting flow system, including mixing and heat loss terms. The composition state will be used as the basis of a soot model. The local soot field is needed then for improved prediction of radiant emission and absorption, as described next.
A photon Monte-Carlo (PMC) line-by-line (LBL) radiation solver (the state of the art in high-resolution radiation modeling) to assess the accuracy of the FDS radiation solver for spectral band absorption and emission by carbon dioxide, water vapor, gaseous fuel vapor, and soot. Detailed chemistry and state of the art soot formation models will be implemented in FDS. Improvements to the effective absorption coefficient for the gray or wide-band models will be developed. Improvements to the radiation transport equation (RTE) to address the issues of angular resolution and time resolution will be made. The effects of turbulence-radiation-interaction (TRI) will be assessed and improvements to the FDS solver to allow predictive radiative emissions to fuel surfaces and distant targets will be developed. This work will also entail development of high-performance computing capabilities in FDS, utilizing large-scale, leadership class supercomputers and GPU acceleration.
Key responsibilities will include but are not limited to:
Implementation of complex chemistry,
GPU acceleration and load balancing of complex chemistry,
Development of coarse-grained (LES) closure for the mean chemical source term,
Implementation of soot formation and oxidation submodels,
Verification and validation of detailed mixing and reaction models,
Developing radiation benchmark tests,
GPU acceleration of the radiation solver,
FDS user support,
Publication of results,
Maintenance of relevant FDS project codes and documentation.
Qualifications
PhD in mechanical engineering.
10 years of relevant experience.
Strong background in turbulent reacting flow simulations, including a detailed understanding of:
o chemical kinetics
o turbulence-chemistry interactions
o turbulence-radiation interactions
o thermal radiation solvers (discrete ordinance method, photon-Monte Carlo line-by-line)
o low-Mach, variable density flows
Strong background in computer-science and programming, including a detailed understanding of:
o distributed memory parallel computing (MPI)
o shared memory parallel computing (OpenMP)
o GPU computing (e.g., OpenACC, CUDA, etc.)
Familiarity with common scripting languages (Bash, Python, Matlab)
Familiarity with LaTeX.
Strong oral and written communication skills.
The university is an Equal Employment Opportunity/Affirmative Action employer that does not unlawfully discriminate in any of its programs or activities on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or expression, or on any other basis prohibited by applicable law.