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 high-fidelity simulations of ICEs that involve both conventional and low-carbon fuels.

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

• Perform simulations of turbulent combustion in combustion engines involving fuel sprays, fuel-air mixing, combustion, and emission modeling.

• Perform modeling and simulations of aftertreatment systems used in the combustion engines simulated above.

• 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.

• 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.).

• The candidate must have the ability to demonstrate 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 combustion engine theory and modeling, extensive knowledge of fuel properties and their behavior in internal combustion engine applications, good understanding of turbulence, spray, spray-wall interaction, chemical kinetics, reacting flow physics, and turbulent combustion modeling are all highly desirable.

• Knowledge of and experience with modeling of aftertreatment devices for propulsion systems is 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.

• Experience in interdisciplinary collaborative research is desired.

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