Vortic CFD
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Studies that answer real design questions

A look at how we work: the AI capability, and the full-vehicle aero study behind it.

Sensor locations selected across the F1 car surface
Case Study 01 · Capability

AI & Flow Control

Performance prediction, sensor placement, automated optimization, and real-time control, built on top of the same CFD work.

Full F1 car surface pressure visualization
Case Study 02

Vehicle Aerodynamics Analysis

A full-car external aero study covering downforce sources, wake structures, and pressure and velocity fields, on a Formula 1 car at race speed.

Optimized fuel injector mixing field and design search plot converging on the target
Case Study 03 · AI Surrogate

AI-Optimized Fuel Injector Design

A Fourier Neural Operator trained on an 80-case CFD sweep, used to inverse-design a hydrogen injector geometry and checked against real CFD.

Reconstruction error dropping as sensor count increases
Case Study 04 · AI Capability

Sparse Sensor Placement

18 sensor locations picked from a CFD-generated surface field, reconstructing the full pressure and shear picture from a handful of readings.

Optimizer converged on the flap angle that hits a target downforce coefficient
Case Study 05 · AI Surrogate Modeling

Dynamic Wing Angle Control

A trained neural network inverse-designs the flap angle of an active aero wing, run in reverse to hit a target downforce value in milliseconds.

Vortic CFD

Custom AI and automotive airflow consulting

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