Postdoctoral Appointee –CFD Modeling
Job posting number: #7103907 (Ref:413630)
Posted: June 23, 2022
Application Deadline: Open Until Filled
Leverage high-performance computing (HPC) to perform multi-physics and multi-scale computational fluid dynamics (CFD) simulations to improve understanding of multiphase mixing, combustion, and heat transfer in turbulent flows prevalent in industrial applications and develop novel numerical techniques and workflows for rapid simulation-driven design optimization.
Develop accurate and computationally-efficient physics-based models for near-wall turbulence, multiphase mixing, gas-phase and catalytic combustion, and conjugate heat transfer (CHT) for high-fidelity CFD of industrial applications using the open-source code OpenFOAM.
Create high-quality meshes for industrial mixers and combustors with complex geometries for use in OpenFOAM.
Perform high-fidelity CFD simulations and reduced order modeling of both reacting and non-reacting turbulent flows.
Perform mechanism reduction for complex fuels, enabling efficient incorporation of detailed chemistry in reacting flow CFD.
Develop and demonstrate integrated CFD-Machine Learning (ML) frameworks for accelerating model development and design optimization on HPC platforms.
Work as a part of a multidisciplinary team involving experimentalists, CFD experts, and computational scientists to use next-generation supercomputing architectures for scalable high-fidelity simulations.
Disseminate research outcomes in the form of technical reports, peer-reviewed journal articles, and conference papers and presentations.
Ph.D. in Mechanical engineering, or a related discipline.
0 to 3 years since Ph.D.
Knowledge of turbulent flows and turbulent combustion is required. Good understanding of multiphase flow physics and turbulent combustion modeling is desired.
Experience in development and application with OpenFOAM is required. Experience in turbulent reacting flow simulations, CHT and wall modeling in OpenFOAM is a plus. Experience with other CFD codes (e.g., Ansys Fluent, CONVERGE, etc.) is a plus.
Proficiency in CAD preparation and mesh generation for complex geometries is required. Automation and scripting skills for pre-processing and post-processing routines are desired.
Knowledge of optimization techniques is a required. Experience in the use of ML libraries (Scikit-learn, TensorFlow, PyTorch, Julia, etc.) for reduced-order modeling is a plus.
Collaborative skills, including the ability to work well with other divisions, laboratories, universities, and industry.
Skilled verbal and written communication skills at all levels of the organization.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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