Postdoctoral Appointee Multi-phase Gas Turbine Modeling
1 month ago
The Multiphysics Computation Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing multi-physics and multi-scale CFD simulations of aviation gas turbine engines. The candidate will be a part of the Transportation and Power Systems Division within the Advanced Energy Technologies Directorate at Argonne. The successful candidates research will involve synergistic collaborations with a multidisciplinary team involving experimentalists, CFD and AI/ML experts, and computational scientists to enhance the predictive capability and scalability of multi-scale and multi-physics simulation code for propulsion applications.
The candidate will perform multi-scale computational fluid dynamics (CFD) simulations involving two-phase flows and combustion/reaction dynamics, applied to combustors for aerospace propulsion systems. The work will take advantage of both commercial and in-house codes and leverage high-performance computing (HPC).
+ Perform high-fidelity nozzle-flow simulations of realistic atomizers to capture liquid jet breakup characteristics.
+ Perform high-fidelity simulations of gas turbine combustors with aviation fuels by developing accurate and computationally efficient CFD models to simulate the fuel injection, fuel-air mixing and combustion dynamics for aerospace combustors.
+ Develop robust libraries to accurately model non-ideal thermophysical properties of real fluids and multi-component vaporization dynamics applied to sustainable aviation fuels (SAF).
+ 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.).
+ 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.
+ Ability to model Argonnes core values of impact, safety, respect, integrity, and teamwork.
+ Ability to make our laboratory a safe, welcoming, inclusive, and accessible environment where all can thrive.
Preferred Qualifications:
+ Knowledge of 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.
+ 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 interdisciplinary collaborative research.
**Job Family**
Postdoctoral Family
**Job Profile**
Postdoctoral Appointee
**Worker Type**
Long-Term (Fixed Term)
**Time Type**
Full time
_As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law._
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