Welcome!


I'm a Computational Fluid Dynamics (CFD) Scientist.

I develop numerical codes, investigate the mathematical modelling of the physics of fluid flows and run simulations using High Performance Computing (HPC).















Curriculum Vitae

Research Experience

Princeton University Atmospheric and Oceanic Sciences

Research Software Engineer
April 2020 - Present

University of Waterloo Mechanical and Mechatronics Engineering

Postdoctoral Fellow
January 2019 - April 2020

Education

University of Sao Paulo
Mechanical Engineering

Ph.D.
Thermal and Fluids
October 2018

INSA Lyon
MEGA


M.Sc.
Fluid Mechanics September 2014

Lebanese University Mechanical Engineering


B.Sc.
Mechanical Engineering
July 2014

Research Projects

Sub-Grid Modeling of Filtered Two-Fluid Models of Fluidized Gas-Solid Flows

Filtered two-fluid models used to perform large scale simulations of gas-solid fluidized flows of industrial risers require closure models for filtered parameters such as filtered and residual stresses and interphase interaction forces mainly the effective drag. Closure models for those filtered parameters may be derived by averaging over results of highly resolved simulations with microscopic two-fluid modeling. Recent models for filtered parameters have been written as functions of filter size, filtered solid volume fraction, and filtered slip velocity. We show that macro-scale variables like domain average solid volume fraction and gas Reynolds number also significantly affect the filtered parameters. In addition to these filtered and macro-scale variables, the effects of other variables over the filtered parameters are investigated: filtered solid kinetic energy, sub-grid gas turbulence and interparticle friction. We show that the filtered solid kinetic energy should be accounted for in the concerning correlations, there by improving accuracy. Regarding gas turbulence, literature shows it has no significant effects on the motion of high Stokes particles. Extending on literature, we investigate the sub-grid gas turbulence effects on meso-scale structures formed of high Stokes particles. Results show that sub-grid gas turbulence has no significant effects on the meso-scale structures and corresponding filtered parameters. While the current results show the necessity of accounting for additional variables in the filtered parameter correlation, they also make it clear the necessity of further developments that are required in the search for better accuracy.

Induction heating of dispersed metallic particles in a turbulent flow

Inductively-heated solid particles dispersed within a decaying isotropic turbulent carrier gas are investigated via Direct Numerical Simulations (DNS). The multiphase simulations account for the compressibility and temperature-dependent viscosity effects of the carrier gas. We develop a semi-empirical model for solid particle heating through hysteresis and Joules mechanisms as these dispersed particles are inductively heated by an external high-frequency alternating magnetic field. The present study focuses on the characteristic time scales of the induction heating and thermal transport of the gas and their modulating effects on the turbulence. We show that the growth of the Kolmogorov length scale is due to a simultaneous increase in viscosity and decrease in the dissipation rate. The temperature-dependent viscosity of the gas leads to a faster decay of the gas turbulent kinetic energy, mainly due to a decrease of energy at intermediate wavenumbers. The evolution of the gas and particle thermal fluctuations are inversely correlated based on the relative thermodynamic timescales. By investigating the change in the temperature spectrum, two regimes could be identified. A first regime arises as the thermal fluctuations increase in time and is defined by a monotonic increase of thermal energy in the low wavenumber range; as the thermal fluctuations decrease in the second regime, the decay occurs over the entire spectrum. Furthermore, aggressive heating set by lower induction heating timescales results in a decrease in particle clustering whereas the particle thermal response time did not show any effect.

Targeted particle delivery via vortex ring reconnection

A conceptual model for targeted particle delivery is proposed using controlled vortex ring reconnection. Entrained particles can be efficiently transported within the core of the vortex ring which is propelled via self-induction. A pair of these particle-transporting vortices traveling in the streamwise direction along parallel trajectories will mutually interact resulting in vortex reconnection. The reconnection causes a topological change to the vortex rings which is accompanied bya rapid repulsion in a perpendicular plane to the direction of travel; effectively, transporting the particles to the desired location near the sidewalls of a ducted flow. In this work, we show the dominant physics of the process and the considerations for targeted delivery.

Multiple same level and telescoping nesting in GFDL's dyncamical core FV3

The GFDL Finite-Volume Cubed-Sphere Dynamical Core, FV3, is used by the National Weather Service (NWS) in the Unified Forecast System (UFS), including the operational Global Forecast System (GFS) and the forthcoming Hurricane Analysis and Forecast System (HAFS). FV3 delivers better numerical accuracy and efficiency, using less computational resources compared to other dynamical cores, and it is the main 'engine' of all weather and climate models developed at GFDL. Consequently, improving FV3 by adding new capabilities is necessary for enhanced forecasts, including short-term forecasts and long-term climate prediction. In this work, which is a collaborative effort of GFDL’s FV3 and FMS teams and part of the broader Hurricane Supplemental project, multiple same level and telescoping nests were implemented in FV3 using GFDL's Flexible Modeling System (FMS). A nest is an additional grid that zooms in over a region of interest to resolve small-scale structures necessary to get a better forecast of localized weather events such as severe storms and hurricanes. The nested grids run concurrently on different sets of processors and interact with their parent grids, thus providing more accurate results on both grids and reducing load imbalances between the different processors. A telescoping nest is a nest within a nest, meaning that “we are now able to 'zoom' in on several levels on multiple simultaneous weather events to very high resolutions”. Nests could be used in global and regional domains. Starting from the latest FV3 public release of 2021, multiple same level and telescoping nests are now fully functional and available for use by the broader scientific community. This will drastically improve the overall forecast performance, bringing unprecedented accuracy, and open the door to numerous research possibilities for scientists and meteorologists alike.

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