Postdoctoral Appointee in Low Carbon Engine and Injection Modeling

Argonne National Laboratory Multiphysics Computation Section

United States

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 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 involving multi-phase flows, turbulent combustion, and heat transfer phenomena for low-carbon internal combustion engines (ICEs) by further developing commercial/in-house codes and leveraging 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 internal combustion engines.

Perform high-fidelity simulations of ICEs that involve both conventional and low-carbon fuels, such as hydrogen, e-fuels, biofuels, and alcohol-based fuels.

Improve the computational efficiency and accuracy of physics-based and data-driven models for gaseous injection and integrate them in simulations of direct injection engines.

Perform simulations of turbulent combustion in internal combustion engines involving multiphase flows and gaseous under-expanded fuel jets.

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 two-phase and/or multiphase turbulent reacting flow applications using CFD codes (e.g., CONVERGE, Ansys Fluent, OpenFOAM, etc.) is required.

Knowledge of internal combustion engine combustion theory and modeling, extensive knowledge of liquid and gaseous fuel properties and their behavior for internal combustion engine applications, good understanding of turbulence, spray, chemical kinetics, reacting flow physics, and turbulent combustion modeling are all highly desirable.

Experience in geometry manipulation with computer-aided design software is highly desirable. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing is desirable.

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 is desirable. Experience in carrying out research tasks with industry partners is desirable.

The candidate must demonstrate good collaborative skills, including the ability to work well with other divisions, laboratories, and universities.

Skilled communication skills at all levels of the organization. Ability 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:

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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