PHYSICS-AI FLUID SIMULATION · PROJECT 01

NACA0012 Aerodynamics

A layered engineering portfolio: start from accepted OpenFOAM CFD, then build parameter studies, compare reduced-order and data-driven representations, validate what works, and keep failed or inconclusive experiments visible.

CFD-grounded96,000-cell fieldsMethod comparisonsLive ROM deployment
NACA0012 → CFD data → parameter sweep → method tests → deployment
01GROUND TRUTH
OPENFOAM CFD DATA

One controlled source before model comparison

The current work starts from the same accepted 2-D transient URANS campaign and canonical HDF5 preprocessing. Model tests should differ in representation or dynamics—not quietly in their source data.

DATA LAYER

OpenFOAM → canonical HDF5

Geometry, volume fields, wall fields, time/phase metadata and exact physical weights used by the downstream ROM experiments.

Open CFD data overview →
02PARAMETER STUDY
U SWEEP · CURRENT ACTIVE STUDY

Seven speeds, four representation / dynamics experiments

AoA is fixed at 12°. Seven CFD anchors cover 50–200 km/h. The methods below are shown in parallel, but their outcomes are deliberately not presented as equally successful.

Open the complete U-Sweep case study →
03NEXT PARAMETER STUDIES
EXPAND THE OPERATING SPACE

Move from one-dimensional speed variation to harder physics regimes

04BEYOND NACA0012
FUTURE PROJECTS

The workflow is broader than one airfoil

Potential next directions include ground-vehicle aerodynamics, morphing geometry, and low-Reynolds-number flapping-wing aerodynamics—each stressing a different combination of geometry, unsteady physics and model generalization.

Open future project map →
ENGINEER BEHIND THE PROJECT

CFD depth, broader engineering ownership, and a Physics-AI direction

My background combines engineering physics, fluid mechanics and more than a decade of CFD / thermal-fluid work with increasingly cross-functional engineering and project responsibility. This portfolio is part of a longer-term direction: connect trusted simulation, reduced-order modeling and AI with deployment, communication and technical leadership.

Engineering background & career vision →
Engineering Physicsphysics / mathematics foundation
MSc Fluid Mechanicsnumerical and fluid-dynamics specialization
10+ years CFDautomotive · energy · industrial applications
Current directionPhysics-AI · technical leadership · productization