My research endeavors encompass a multifaceted approach to advancing computational fluid dynamics (CFD) techniques in a diverse array of engineering domains. Leveraging my expertise in building in-house finite element method (FEM) and finite volume method (FVM) codes in Fortran and C++, I have developed robust tools for simulating both steady and unsteady incompressible and compressible flows, spanning from basic Euler equations to more complex Navier-Stokes formulations.

Embracing the principles of Object-Oriented Programming (OOP), I have contributed to the development of shape optimization code using Fortran. In addition, I gained the skills of dealing with different types of dynamic meshes, such as morphing and overset meshes, as well as implementing fixed meshes such as Volume-of-Fluid (VOF) and Level Set Method (LSM) in both Fortran and OpenFOAM (C++) environments. 

My proficiency in parallelization techniques for High-Performance Computing (HPC) environments has been instrumental in optimizing computational efficiency. Notably, my contributions include developing libraries for mooring coupling, refining overset methods to accommodate simultaneous rigid body motion and deformation, and implementing PANS in turbulent modeling.

Leveraging advanced turbulence modeling techniques, including Reynolds-Averaged Navier-Stokes (RANS), Partially-Averaged Navier-Stokes (PANS), and Implicit Large Eddy Simulation (ILES), I have delved into understanding the intricate interplay of turbulent structures and their impact on fluid behavior. Recognizing the limitations of traditional modeling approaches, I will embark on integrating machine learning (ML) and deep learning (DL) techniques into turbulent simulations to augment predictive accuracy and efficiency. By harnessing the power of ML algorithms, such as neural networks and deep learning models, I aim to uncover hidden patterns within turbulent flow data and develop novel methodologies for turbulence modeling and prediction. Through projects like ML-based lift coefficient prediction for transonic airfoils (Ayman et al., 2023) and participation in OpenFOAM-Machine Learning Hackathons, I have enriched my knowledge in AI technologies and their potential for revolutionizing turbulent flow simulations. Moving forward, I am committed to further exploring the synergies between turbulent simulations and machine learning, with the ultimate goal of advancing our understanding of turbulent flows and driving innovation in engineering design and optimization.

The VIV phenomenon also appears over streamlined bodies, such as foils, at high angles of attack. The flapping foil generates power by performing two main motions: heave and pitch. The main indication of the power extraction capability for the flapping-foil is the strength of the Leading-Edge Vortex (LEV) (Hamada & Fürth, 2022 & 2023). Hence, the gained power starts to fade out when the stall phenomenon appears (Karbasian et al., 2016). High-lift devices will delay the stall phenomenon of the LEV over the flapping foils. However, which type of high lift device, (flap, slat, cuff, and air slots) is the best and the time of its operation during the flapping cycle is still unclear. In addition, expanding from 2D to 3D, biomimicry from the fish fin will be used to improve the performance of the flapping foil. The motion of a fish’s fin during swimming is very similar to the flapping foil motion, simplifying the fish’s fin motion to be a rigid body, Qiang Zhong et al. (2021) showed that the induced vortices, generated during the flapping motion of the fin, are highly affected by the shape of the fish’s fin. Thus, the bio-inspired shapes from near-ground swimming fishes will increase the efficiency of energy harvesting using the flapping foil operating near the ground. However, the determination of the geometric parameters of the flapping foil, such as the aspect ratio, sweep angle, taper ratio, and twist angle is still an open area of research.