Postdoctoral Appointee CFD Modeling of Ammonia Gas Turbine Engines
2 weeks ago
The Multiphysics Computation Section within the Transportation and Power Systems Division at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidates 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 for predictive computational fluid dynamics (CFD) simulations of combustion dynamics and emissions (such as, N2O, NOx) in gas turbine engines operating on ammonia and its blends with hydrogen and natural gas.
+ Develop accurate reduced chemical kinetic models for combustion of ammonia and ammonia/hydrogen/methane blends.
+ Develop computational combustion diagnostic approaches to capture rare event precursors.
+ Augment chemical kinetic and turbulent combustion models with machine learning (ML) approaches.
+ Demonstrate the capability of computational modeling frameworks to accelerate full-scale simulations of advanced stationary power generation gas 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 Argonnes Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications:
+ Experience in interdisciplinary collaborative research.
+ Understanding of combustion dynamics and emissions in gas turbines.
+ Development and application of machine learning tools in chemical kinetics and turbulent combustion.
+ 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 Family**
Postdoctoral Family
**Job Profile**
Postdoctoral Appointee
**Worker Type**
Long-Term (Fixed Term)
**Time Type**
Full time
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