Faiq Shahbaz

Hi, I’m Faiq Shahbaz, a CFD engineer and researcher working at the intersection of Computational Fluid Dynamics (CFD), High-Performance Computing (HPC), and data-driven modeling for physical systems. I’m currently a CFD Support Engineer at BetaCodes (formerly Forbmax). I hold a BSc in Aerospace Engineering from the Institute of Space Technology (IST) and an MS in Computational Science & Engineering from NUST, with a specialization in CFD and HPC.

My work focuses on building reliable simulation workflows, developing scalable computational tools, and exploring how modern machine learning, especially graph-based methods (GNNs), can complement traditional numerical solvers for faster prediction and analysis.

I’m currently investigating the GEKO turbulence model on the Ahmed body benchmark. This includes a comprehensive comparison across RANS, pseudo-time RANS, and URANS, as well as a large parameter-sweep study spanning ~100 GEKO parameter combinations across multiple inlet velocities. The study is designed to understand model sensitivity, robustness, and predictive behavior under controlled variations, and to produce a structured dataset suitable for downstream analysis and machine learning.

Previously, I led an end-to-end ducted wind turbine effort covering design, optimization, CFD evaluation, and experimental validation, supported by robust automation and post-processing workflows.

Interests & expertise

• CFD and turbulence modeling, including benchmark-driven evaluation and verification/validation workflows
• Parametric studies and uncertainty/sensitivity analysis for model robustness and repeatability
• High-performance simulation workflows, automation, and scalable post-processing pipelines
• Solver and tooling development using Python, C++, MPI, and PETSc
• Scientific machine learning for CFD, including GNN-based surrogate modeling and physics-aware learning approaches
• Reproducible computational engineering, with an emphasis on clean software practices and traceable results

Open to

• PhD opportunities aligned with CFD, HPC, and scientific machine learning
• Research/engineering collaborations in CFD, turbulence modeling, and data-driven physical modeling

Feel free to reach out using the social links below.