FUTURE RESEARCH VISION: HASHED LAB
I am a Ph.D. candidate in Mechanical Engineering specializing in Computational Heat Transfer and Fluid Mechanics, with a strong focus on aircraft and wind turbine icing. My research involves high-fidelity numerical simulations, multiphase flow modeling, and heat transfer analysis to improve our understanding of icing mechanisms and their impact on aerodynamic performance.
As I work toward my career, I aim to establish HASHED Lab (High-fidelity Aero-Thermodynamic Simulation and Hydrometeor Evolution & Deposition)—a research group dedicated to advancing icing physics, computational modeling, and thermal-fluid interactions in extreme environments.
CURRENT RESEARCH FOCUS
Multi-phase Flows & Ice AccretionI love figuring out how liquid droplets and ice behave with environmental flows, especially when they mix or freeze. I use computational methodologies to understand these complex movements better. I use computational codes based on the applied forces on each particle/droplet to analyze the droplet breakup mechanisms, splashing effects, collision, etc.
Wikipedia Links: Environmental flows, Multi-phase flow
I am passionate about studying the movement of fluids, and I employ computational methods to gain a deeper understanding of these flows. My focus lies in exploring diverse approaches to represent the data associated with fluid movements. To achieve this, I utilize various computational methods, including the finite difference method, finite volume method, finite element method, and spectral element method. The following are the coding languages and tools I use to demonstrate and investigate the CFD problems:
Tools: Nek5000, NekRS, Gmsh, Ansys Fluent, COMSOL, Solidworks, ParaView
Wikipedia Links: Computational fluid dynamics, Fluid mechanics
Exploring Patterns in Fluid Mechanics with Machine Learning
I am passionate about leveraging machine learning (ML) to uncover hidden patterns in fluid mechanics. My research focuses on applying ML techniques to Computational Fluid Dynamics (CFD), aerodynamics, and multiphase flows, enabling more efficient simulations and predictive modeling. I use Python, TensorFlow, and advanced ML algorithms to analyze complex fluid dynamics data, enhance turbulence modeling, and improve aerodynamic and icing simulations. By integrating data-driven approaches with physics-based models, I aim to push the boundaries of fluid mechanics and high-fidelity simulations.
Wikipedia Links: Machine Learning, Deep Learning
Heat transfer enhancement is the process of increasing the heat transferability of the heat processing equipment. Elementary research objectives are:
- to explore what and how the computational results are approximated in cases of turbulent nanofluid flow for forced and mixed convective heat transfer through a medium.
- to observe and compare the computational values obtained under the presence of a magnetic field with previous literature.
Tools: Ansys Fluent, COMSOL, Solidworks
Wikipedia Links: Heat transfer enhancements, Heat transfer
Stochastic optimization is the study of applying optimization techniques in uncertain parameters. The world is uncertain with a lot of uncertain resources around us. My goal is to optimize for better outcomes using optimization techniques.
Wikipedia Links: Stochastic optimization, Linear programming