Postdoctoral Appointee Low-Carbon Fuel Sprays and Engine Combustion Modeling
1 month ago
The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee. The successful candidates 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 simulations of turbulent combustion in combustion engines involving fuel sprays, spray-wall interaction, fuel-air mixing, combustion, and emission modeling.
+ Perform high-fidelity reacting simulations that involve both conventional and low-carbon fuels, with emphasis on alcohol-based, bio-, and renewable fuels.
+ Improve the computational efficiency and accuracy of physics-based and data-driven models for liquid fuel injection and spray-wall interaction and integrate them in simulations of direct injection engines.
+ 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 multiphase turbulent reacting flow applications using 3-D CFD codes (e.g., CONVERGE, OpenFOAM, Ansys Fluent, etc.).
+ Excellent understanding of sprays, spray-wall interaction, turbulence, chemical kinetics, reacting flow physics, and turbulent combustion modeling.
+ Extensive knowledge of fuel properties and their effects on fluid behavior in reacting and non-reacting flow applications.
+ Understanding of combustion engine theory and modeling.
+ The candidate must demonstrate good 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 Argonnes Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications:
+ Knowledge of and experience with modeling alcohol-based, bio-, and renewable fuels.
+ Experience in geometry manipulation with computer-aided design software.
+ Experience with coupling CFD and FEA codes.
+ Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, as well as parallel scientific computing.
+ 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.
+ Experience in carrying out research tasks with industry partners.
+ 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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Lemont, United States Argonne National Laboratory Full timeThe 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...
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