Portfolio
MSc Computational Fluid Dynamics student at Cranfield University. Dual-degree engineer (Physics + Mechanical) from BITS Pilani. I build simulation pipelines, surrogate models, and data-driven tools for aerodynamics and beyond.
Selected builds and experiments from my personal workspace.
Generated a 24.6M-cell hybrid-unstructured mesh for the NASA Trapezoidal Wing (HiLiftPW-1) using Pointwise T-Rex. Targeted y+ ~ 1 at Re = 7.5M with 30 prism layers and structured wake refinement.
Implemented and analysed FTCS, Upwind, Lax-Friedrichs and Lax-Wendroff schemes for 1D advection. Performed parallel scaling and carbon-footprint assessment of UCNS3D on Cranfield HPC clusters.
Implemented a 1D Euler solver with Minmod slope limiting and HLL/HLLC Riemann solvers. Analysed 2D UCNS3D results for Riemann problems, Mach 3 forward-facing step, and double Mach reflection.
Group design project: convertible fixed-wing / quad-rotor UAV concept. CFD hover validation, grid-convergence study (~2M / 5M / 10M cells), RPM parametric study, and turbulence model comparison.
Built a 2D incompressible Navier-Stokes solver using SIMPLE + TDMA on a staggered grid. Validated against Ghia et al. (1982) at Re = 100–1000, with grid convergence studies and central-difference comparison.
Built a full project tracking pipeline: personal-db with status/sub-items/icons, local kanban dashboard with drag-and-drop, portfolio integration, one-click Vercel deploy, and Graphify setup.
CLI tool that turns natural language into executable CAD models using a fine-tuned vision-language model.
Weylus + KDE Connect tablet input configuration.
Personal Hyprland desktop configuration docs and wallpaper scripts.
GBA ROM hack dev framework based on pret's pokeemerald decompilation.
Browser-based RPG map builder inspired by Pokémon Emerald. Flask backend + HTML5 Canvas tile renderer.
Now
MSc Researcher — CFD & Machine Learning
At Cranfield University, executing high-fidelity CFD simulations (ANSYS Fluent, OpenFOAM) and developing Neural Network-based surrogate models to accelerate aerodynamic design cycles.
Recent
Thesis Researcher — AI-Enhanced Microfluidics
At BITS Pilani, designed a microfluidic sensor and developed AI-based detection algorithms using machine learning for real-time fluid analysis, bridging CFD with sensor technology.
Education
MSc CFD @ Cranfield | Dual Degree @ BITS Pilani
MSc in Computational Fluid Dynamics (Cranfield, 2025–2026) and a 5-year dual degree in Physics + Mechanical Engineering (BITS Pilani, 2020–2025). Core focus: fluid mechanics, aerodynamics, HPC, and numerical methods.