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, computational fluid dynamics (CFD) experts, and computational scientists to enhance the predictive capability and scalability of multi-scale and multi-physics simulation codes.

• Develop turbulent combustion models for predictive CFD simulations of combustion dynamics in rotating detonation engines (RDEs) operating on high-hydrogen content fuels.

• Develop turbulent combustion modeling approaches for predictive CFD simulations of extreme events (such as, flame flashback) in gas turbine combustors operating on high-hydrogen content fuels with up to 100% hydrogen. Leverage simulation datasets to develop an understanding of extreme 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

• Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical/aerospace engineering, chemical engineering, or a related discipline.

• Onsite presence is required.

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

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

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

• Ability to demonstrate strong written and oral communication skills.

• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

• Ability to make our laboratory a safe, welcoming, inclusive, and accessible environment where all can thrive.

Preferred Qualifications:

• Experience in interdisciplinary collaborative research.

• Understanding of extreme combustion events in gas turbines.

• Background and expertise in reduced-order manifold-based (flamelet) combustion models for turbulent reacting flows.

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

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