Postdoctoral Appointee – Predictive Reacting Flow CFD Modeling of Extreme Events in Hydrogen-Fueled Gas Turbine Engines
- Argonne National Laboratory
- Location: Lemont, USA
- Job Number: 7173769 (Ref #: 416378)
- Posting Date: Aug 26, 2023
- Application Deadline: Open Until Filled
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.
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.
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.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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