Physics Based Models for Power and Propulsion Innovations using High Performance Computing

ORAU; Aberdeen (MD); On-site; USD 70,000 - 90,000; Full time

Job summary

ORAU is seeking a research associate to develop advanced physical models for turbine power and propulsion systems. This role involves creating computational fluid dynamics (CFD) models, performing high-performance computing (HPC) tasks, and collaborating with academic and industry partners to validate models. Candidates should have a strong background in mechanical or aerospace engineering, as well as experience in big data processing and turbulence modelling. The position is open to those with Bachelor's, Master's, or Doctoral degrees in relevant fields.

Qualifications

• Deep knowledge of CFD model development, mesh generation techniques, turbulence models, and MPI communication protocols.
• Experience with HPC and scalability analysis for massively parallel computations.
• Familiarity with data-visualisation hardware and software resources and with big-data processing.

Responsibilities

• Develop new physics-based CFD models for turbine power and propulsion systems.
• Perform mesh generation and turbulence modelling for massively parallel computations.
• Collaborate with external partners to validate models against experimental data.
• Advance numerical methods for jet engine and hypersonic system optimisation.
• Develop machine-learning techniques to leverage large simulation datasets.

Skills

CFD model development

Mesh generation techniques

Turbulence models

MPI communication protocols

Big data processing

Education

Bachelor's, Master's or Doctoral degree

Job description

About the Research

The US Army Research Laboratory, Mechanical Sciences Division, conducts basic and applied research in Turbine Power and Propulsion to address far‑term science and technology challenges envisioned for future battlefield environments in Multi‑Domain Operations (MDO). Energy and Propulsion is one key thrust area, where exploratory fundamental research is conducted in propulsion sciences for Army gas turbine engines and hypersonic weapon systems to enhance durability, performance, self‑sustainability, and high power density under extreme battlefield operating conditions. The research associateship focuses on developing novel physics‑based models, high‑fidelity simulation, and machine‑learning capabilities to understand fundamental physics and explore the design space of next‑generation disruptive propulsion platforms. It involves collaborations with leading OEMs and academia and has direct links to experimental facilities at the ARL Weapons and Materials Research Directorate.

Research Focus and Responsibilities

• Develop new physics‑based computational fluid dynamics (CFD) models for turbine power and propulsion systems.
• Perform mesh generation, turbulence modelling, and MPI‑based HPC scalability analysis for massively parallel computations on DoD leadership High Performance Computing (HPC) facilities.
• Collaborate with ARL Weapons and Materials Research Directorate and external partners to validate models against experimental data.
• Advance numerical methods such as direct numerical simulation (DNS), large‑eddy simulation (LES), or Reynolds‑averaged Navier–Stokes (RANS) approaches, including interface‑tracking (VOF, LS), hybrid Eulerian–Lagrangian, and fluid–structure‑interaction (FSI) models for jet engine and hypersonic system optimisation.
• Develop and apply machine‑learning techniques to leverage large simulation datasets for insight discovery, optimisation, and data‑reduction, and support big‑data processing and in‑situ visualisation.

Qualifications

• Deep knowledge of CFD model development, mesh generation techniques, turbulence models, and MPI communication protocols.
• Experience with HPC and scalability analysis for massively parallel computations.
• Familiarity with data‑visualisation hardware and software resources and with big‑data processing.
• Background in computational fluid dynamics, mechanical or aerospace engineering, or related disciplines.

Eligibility

Eligible candidates must hold a Bachelor’s, Master’s, or Doctoral degree and be at least 18 years of age.

Contact

ARL Advisor: Luis Bravo
Contact Email: [e-mail]