The Multiphysics Computation Section within the Transportation and Power Systems Division at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team involving engine modelers, CFD and AI/ML experts, and computational scientists to enhance the predictive capability and scalability of multi-scale and multi-physics simulation codes.

• Develop turbulent combustion modeling approaches augmented with machine learning (ML) for predictive computational fluid dynamics (CFD) simulations of extreme/rare events (such as, flame flashback, thermoacoustic instabilities) in gas turbine combustors operating on high-hydrogen content fuels with up to 100% hydrogen.

• Develop computational combustion diagnostic approaches to capture rare event precursors.

• Demonstrate the capability of computational modeling frameworks to accelerate full-scale simulations of advanced stationary power generation turbine engines. Import and accelerate CFD simulation workflows on leadership class supercomputing platforms.

Position Requirements

• Ph.D. in mechanical/aerospace engineering, chemical engineering, or a related discipline.

• Understanding of chemical kinetics, turbulent reacting flows, and turbulent combustion modeling.

• Background and expertise in CFD modeling of turbulent reacting flows within energy conversion systems (e.g., internal combustion engines, gas turbine combustors, detonation engines, etc.) using CFD solvers (e.g., CONVERGE, Nek5000/NekRS, OpenFOAM, Fluent, etc.) on large-scale HPC platforms.

• Collaborative skills, including the ability to work well with other divisions, laboratories, and universities.

• Skilled written and oral communication skills at levels of the organization.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. 

Preferred Qualifications:

• Experience in interdisciplinary collaborative research.

• Understanding of extreme combustion events in gas turbines.

• Development and application of machine learning tools in one or more of these areas: chemical kinetics, turbulent combustion, and extreme event prediction.

• Background and experience in the development of machine learning algorithms and software (in TensorFlow, PyTorch, Julia, etc.) for surrogate-assisted modeling, management and analysis of big data, and parallel scientific computing.

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