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 heavy-duty, non-road 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 for off-road, rail, and marine engines.

• Perform high-fidelity simulations of heavy-duty ICEs that involve both conventional and low-carbon fuels, such as biodiesel, methanol, and hydrogen.

• Improve the accuracy of two-phase flow models for dynamic coupling of the internal nozzle flow with the ensuing spray in simulations of direct injection applications.

• Develop surrogate models and reduced chemical kinetic models for complex fuels, enabling efficient incorporation of detailed chemistry in engine CFD.

• 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.

• 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.).

• Knowledge of internal combustion engine combustion theory and modeling, extensive knowledge of liquid and gaseous fuels for engine applications, good understanding of turbulence, spray, chemical kinetics, reacting flow physics, and turbulent combustion modeling.

• Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing,

• Experience in geometry manipulation with computer-aided design software.

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

• Skilled communication skills at all levels of the organization.

• Experience presenting and publishing 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.

• 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.

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