The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidate’s research will involve synergistic collaborations with a multidisciplinary team comprised of fellow postdoctoral appointees and staff scientists with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes.
The prospective postdoctoral appointee will perform multi-physics and multi-scale CFD simulations of complex systems involving modeling of multi-phase flows, turbulent combustion, heat transfer, combustion, and emissions of low-carbon propulsion systems by further developing commercial/in-house codes and high-performance computing (HPC).
Develop accurate and computationally efficient CFD models to simulate the chain of physics and chemistry involved with fuel injection, fuel-air mixing, turbulent combustion, and emissions of propulsion systems.
Perform simulations of turbulent combustion in combustion engines involving fuel sprays, spray-wall interaction, fuel-air mixing, combustion, and emission modeling.
Perform high-fidelity reacting simulations that involve both conventional and low-carbon fuels, with emphasis on alcohol-based, bio-, and renewable fuels.
Improve the computational efficiency and accuracy of physics-based and data-driven models for liquid fuel injection and spray-wall interaction and integrate them in simulations of direct injection engines.
Work as a part of a multidisciplinary team involving experimentalists, CFD experts, and computational scientists to enable cutting-edge CFD modeling & simulations on the next generation supercomputing architectures.
Position Requirements
Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline earned no more than three years ago is required.
Experience in modeling and simulation of three-dimensional multiphase turbulent reacting flow applications using 3-D CFD codes (e.g., CONVERGE, OpenFOAM, Ansys Fluent, etc.).
Excellent understanding of sprays, spray-wall interaction, turbulence, chemical kinetics, reacting flow physics, and turbulent combustion modeling.
Extensive knowledge of fuel properties and their effects on fluid behavior in reacting and non-reacting flow applications.
Understanding of combustion engine theory and modeling.
The candidate must demonstrate good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.
Skilled in communication at all levels of the organization.
The successful candidate is expected to present and publish results in peer reviewed society technical reports and journal articles.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications:
Knowledge of and experience with modeling alcohol-based, bio-, and renewable fuels.
Experience in geometry manipulation with computer-aided design software.
Experience with coupling CFD and FEA codes.
Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, as well as parallel scientific computing.
Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and parallel scientific computing.
Experience in carrying out research tasks with industry partners.
Experience in interdisciplinary collaborative research.
Job Family
Postdoctoral Family
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
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