Postdoctoral Appointee High-fidelity Scale-resolving CFD Simulations and Reduced Order Modeling of Propulsion and Power Generation Systems jobs in United States
cer-icon
Apply on Employer Site
company-logo

Argonne National Laboratory · 1 month ago

Postdoctoral Appointee High-fidelity Scale-resolving CFD Simulations and Reduced Order Modeling of Propulsion and Power Generation Systems

Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems. The successful candidate will collaborate with a multi-disciplinary team to enhance the predictive capability for next-generation engine modeling code.

EnergySecuritySocial Impact
check
Culture & Values
badNo H1BnoteU.S. Citizen Onlynote

Responsibilities

Perform high-fidelity CFD simulations of turbulent and reacting flows pertaining to gas turbines and detonation engines using spectral element method (SEM)
Perform scalability studies and port these simulations to leadership class supercomputing platforms, identify and improve the bottlenecks in scaling the simulations
Analyze large simulation datasets to gain new scientific insights and develop reduced order models (ROMs) for boundary layer flows and turbulent combustion
Integrate ROMs with CFD solvers and demonstrate predictive accuracy compared to traditional modeling approaches

Qualification

CFD simulationsSpectral element methodSupercomputing platformsReduced-order modelsTurbulenceBoundary layer flowsMesh generationCollaborative skillsCommunication skills

Required

Recent or soon-to-be-completed (typically completed within the last 0-5 years) Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a related discipline
Expertise in Nek5000/NekRS or other comparable spectral element method codes
Experience in running high-fidelity simulations on leadership class supercomputers
Knowledge of performing scalability studies to identify and improve bottlenecks in large codes
Experience in development of data-driven reduced-order models in one or more of these areas: turbulence, boundary layer flows, combustion
Understanding of high-order methods for fluid flows
Understanding of turbulence, boundary layer flows, multi-phase flows, chemical kinetics, combustion, and detonations
Experience in mesh generation with computer-aided design software
Collaborative skills, including the ability to work well with other divisions, laboratories, and universities
Ability to demonstrate strong written and oral 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
This position requires an on-site presence at the Argonne campus in Lemont, Illinois

Preferred

Experience in interdisciplinary collaborative research
Knowledge of large scientific code management and optimization

Benefits

Comprehensive benefits are part of the total rewards package

Company

Argonne National Laboratory

company-logo
Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management.

Funding

Current Stage
Late Stage
Total Funding
$41.4M
Key Investors
Advanced Research Projects Agency for HealthUS Department of EnergyU.S. Department of Homeland Security
2024-11-14Grant· $21.7M
2023-09-27Grant
2023-01-17Grant

Leadership Team

leader-logo
Raeanna Sharp- Geiger
COO
linkedin
leader-logo
Paul Kearns
Laboratory Director
linkedin

Recent News

Inside HPC & AI News | High-Performance Computing & Artificial Intelligence
Inside HPC & AI News | High-Performance Computing & Artificial Intelligence
Company data provided by crunchbase