Advanced Physics Based Models for Next Generation
at ORAU Workforce Solutions

Date Posted: 8/23/2019

Opportunity Description

Title of Research Opportunity: Advanced Physics Based Models for Next Generation Power and Propulsion Innovations using High Performance Computing The US Army Research Laboratory, Science-for-Maneuver Campaign, conducts basic and applied research which will enable key technologies that can address far-term science and technology challenges that are envisioned for the future battlefield. Energy and Propulsion, is one thrust area conducting exploratory research in adaptive propulsion platforms for existing and novel internal and gas-turbine engines, to expand its performance for self-sustainability and high power density under extreme battlefield operating conditions. An important component is the development of novel physics based models and detailed simulation capabilities not only to understand the fundamental physics, but also to explore the design space for next generation vehicle propulsion platforms. This research associateship is a critical part of on-going mission programs towards developing adaptive propulsion technologies, including fuel-flexible operations, for current and future Army vehicles. This research will focus on the development of accurate physics based models to be deployed on DoD leadership High Performance Computing (HPC) facilities leveraging exceptional computational resources and teaming with internal and cross-directorate experts in computational sciences. The models developed will have direct links to experimental facilities at ARL Vehicle Research Laboratory and external collaborators for validation and concept vetting efforts. Research proposals are invited to develop first principle and engineering models for multiphase turbulent flows, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), or RANS models, as well as, as One-Dimensional-Turbulence (ODT) stochastic models, or Linear Eddy Mixing (LEM), computational frameworks, such as LEMLES. Data reduction and in-situ visualization techniques are also desired for big data processing (TBs) and interpretation/presentation of scientific data. The areas of technical interest include, but are not limited to, 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 the computational CPU cost; and fluid-structure-interaction (FSI) models for jet engine optimization under off-design conditions. Some possible research topics include: a. Develop improved physics based interface tracking models based on VOF or LS methods to simulate spray breakup under turbulent flow environments using HPC resources for massively parallel computations. Development of sub-grid scale (SGS) models for complex multiphase flows in LES framework. Develop a deep understanding of the fuel injection physics including internal nozzle flow instabilities (e.g., cavitation), primary breakup, and transcritical mixing and thermodynamics based on the state-of-the-art theoretical formulations. b. Develop computational tools and combustion models that are mode or regime independent based on the Linear Eddy Mixing (LEM) methodologies to enable more accurate simulations of unsteady non-premixed turbulent combustion, extinction/re-ignition physics with comparison to DNS or laboratory measurements. c. Advance Lagrangian particle transport and deposition models in turbulent flow environments to simulate complex multi-physics sand ingestion processes in jet engines, phase-change, and solidification in E/TBC. Develop concepts and strategies to create sand phobic engineered materials to improve the performance of coatings under high temperature conditions. The candidate will have a deep knowledge of computational fluid dynamics (CFD) model development, mesh generation techniques, turbulence models, MPI/OpenMPI protocols, High Performance Computing and scalability analysis for massively parallel computations. The team will work closely with ARL staff in the Vehicle Technology 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 (Su et al, 2018) will be available to the candidate. The nature of the work will also require establishing close collaborations with experimentalists from Vehicle Research Laboratory, and relevant external partners from academia, industry and national laboratories. References: 1. Bravo, L., et al, “Comp. Study of Atomization and Fuel Drop Size Dist. in High Speed Primary Breakup”, J. Atom. Sprays, Vol 4 (2018). 2. Bravo, L, et al. “Effects of Fuel Viscosity on the Primary Breakup Dynamics of a High-Speed Liquid Jet with Comparison to X-ray Radiography”, Proc. Comb. Inst., Vol 37 (2018). 3. Ma. P., Bravo, L., Ihme, M., et al, “LES of transcritical injection and auto-ignition using diffuse-interface method and finite rate chemistry”, Proc. Comb. Inst., Vol 37 (2018). 4. Bravo, L, et al, “Breakthroughs in Engine Propulsion Research with High Performance Computing”, DSIAC Journal, Vol 4, No 4 (2017). 5. Murugan, M., Ghoshal, A., Xue, F., Hsu, M.C., Bazilev, Y., Bravo, L., Kerner, K., “Analytical Study of Articulating Turbine Rotor Blade Concept for Improved Off-Design Performance of Gas Turbine Engines”, J. of Eng. Gas Turb. & Power, Vol. 139, No. 10, 102601, 2017. 6. Su, S., et al., “Reconfigurable visual computing architecture for extreme-scale visual analytics”, Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520M, 2018. For additional information, or questions regarding curriculum vitae please contact Dr. Luis Bravo: Dr. Luis Bravo 410-278-9719 [Click Here To Join] Keywords: fuel spray, near-limit combustion, particle laden flow, sub-grid-scale (SGS) models Click here for more information

Opportunity Snapshot

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