The Multi-Physics 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 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.

The candidate will perform multi-physics and multi-scale computational fluid dynamics (CFD) simulations for novel internal combustion engines (ICEs) fueled with low/zero-carbon fuels and used in combined heat and power systems and chemical reforming systems by leveraging high-performance computing (HPC) resources.

• Perform high-fidelity CFD simulations of ICEs’ in-cylinder processes involving gaseous fuel injection, turbulent jet ignition, turbulent combustion, pollutant formation, and heat transfer phenomena.

• Perform optimization of combustion chamber geometry and operational parameters of ICEs to achieve higher efficiency, desired throughput, and lower emissions.

• Improve and develop CFD submodels in commercial CFD codes such as CONVERGE to improve the predictive capability of simulation.

Position Requirements

• Ph.D. in mechanical/aerospace engineering, chemical engineering, or a related discipline earned no more than three years ago is required.

• Expertise in CFD modeling of turbulent reacting flows in advanced propulsion and energy conversion systems using CFD codes (e.g., CONVERGE, STAR-CD, OpenFOAM, etc.) is required.

• Knowledge of ICEs’ combustion theory and modeling, extensive knowledge of advanced ignition and combustion concepts for ICEs, as well as good understanding of turbulence, chemical kinetics are required.

• Experience in multi-dimensional code development (in Fortran/C/C++) and parallel scientific computing on large-scale HPC platforms is highly desirable.

• Experience in carrying out research tasks with industry partners.

• Ability to demonstrate strong written and oral communication skills.

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

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

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