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

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 for the future battlefield environment in Multi-Domain Operations (MDO). Energy and Propulsion is one key thrust area conducting exploratory fundamental research in propulsion sciences for Army gas turbine engines and hypersonic weapon systems to enhance durability, performance for self-sustainability, and high power density under extreme battlefield operating conditions. An important component is the development of novel physics-based models, high-fidelity simulation, and machine learning capabilities to understand the fundamental physics and explore the design space of next-generation disruptive propulsion platforms.

This research associateship is a critical part of ongoing mission programs towards developing innovative propulsion technologies, including adaptive turbine propulsion, pressure gain combustion, and morphing hypersonic systems for current and future Army vehicles. This research will focus on the development of new physics-based computational fluid dynamic models to be deployed on DoD leadership High Performance Computing (HPC) facilities leveraging exceptional resources and teaming with internal and cross-directorate experts in mechanical and computational sciences. This position will also involve collaborations with leading OEMs and academia. The models developed will have direct links to experimental facilities at ARL Weapons and Materials Research Directorate as well as external facilities for validation and concept vetting efforts.

Technical Areas of Interest

• First-principle and engineering models for compressible fluid dynamics
• Particle-laden turbulence
• Fluid structure interactions
• Conjugate heat transfer, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), or RANS approaches
• New methods in machine learning to leverage large databases produced by simulations for extracting new insights and guiding optimization and design space exploration
• Data reduction and in-situ visualization techniques for big data processing (TBs)
• Interface-tracking methods (e.g., VOF, LS)
• Hybrid methods (Eulerian-Lagrangian)
• Chemical kinetic models based on finite rate chemistry, tabulated kinetic models, or novel methods that improve computational CPU cost
• Fluid-structure-interaction (FSI) models for jet engine and hypersonic system optimization under off-design conditions

Qualifications

The candidate will have a deep knowledge of computational fluid dynamics (CFD) model development, mesh generation techniques, turbulence models, MPI protocols, High Performance Computing, and scalability analysis for massively parallel computations. The team will work closely with ARL staff in the Weapons and Materials Research Directorate, as well as cross-directorate collaboration from Computational & Information Science Directorate to closely interact with the data analysis and scientific visualization team, and HPC scientists. Data visualization hardware and software resources mentioned in [4] will be available to the candidate. The nature of the research will also require establishing close collaborations with experimentalists from ARL, and relevant external partners from academia, industry, and national laboratories.

ARL Advisor

Luis Bravo
luis.g.bravorobles.civ@army.mil

Arl-RAP Overview

The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, combustion processes, propulsion and flight physics, communication and networking, and computational and information sciences.

Application Process

A complete application includes:

• Curriculum Vitae or Resume
• Three References Forms
• Transcripts

If selected by an advisor, the participant will also be required to write a research proposal to submit to the ARL-RAP review panel for:

• Research topic should relate to a specific opportunity at ARL
• The objective of the research topic should be clear and have a defined outcome
• Explain the direction you plan to pursue
• Include expected period for completing the study
• Include a brief background such as preparation and motivation for the research
• References of published efforts may be used to improve the proposal

Questions about this opportunity? Please email ARLFellowship@orau.org