AI-Based Real-Time Aerodynamic Prediction for 2-Channel Electric Air Vents

Physics-constrained predictive AI that replaces CFD simulation for real-time airflow evaluation of automotive air vents

AI-Based Real-Time Aerodynamic Prediction for 2-Channel Electric Air Vents — Narnia Labs case study (originally: AI 예측을 활용한 2채널 전동 에어벤트 최적 설계)

Project Overview

Developed a predictive AI framework that replaces computational fluid dynamics (CFD) simulation for evaluating 2-channel electric air vents used in vehicle cabin air-conditioning. The system returns airflow speed and direction distributions in real time from a candidate geometry, enabling designers to evaluate and iterate concepts without going through a full CFD analysis cycle.

My Role

Project Manager, Narnia Labs

  • Led problem definition and scoping in collaboration with the client team
  • Owned client-facing communication and delivery milestones throughout the engagement
  • Drove hands-on technical work on the physics-constrained predictive AI framework for real-time airflow and direction estimation

The Challenge

Verifying the performance of a new air-vent geometry traditionally requires running heavy CFD simulations for every candidate design:

  • Expert-dependent long turnaround: CFD requires significant expert time and compute, inflating design cycle time and cost.
  • Bottleneck for design exploration: Because each variant must go through a full simulation, designers cannot rapidly compare a large number of alternatives.

Technical Approach

Built a physics-aware predictive AI that supplies airflow and direction estimates in place of CFD:

  • Physics-constrained learning: Beyond fitting reference data, the training loss enforces physical constraints such as the incompressible continuity equation, so the model is aware of the governing physics — not just the numbers.
  • Aerodynamic reasoning: The model learns aerodynamic principles from data, producing high-fidelity, physically consistent predictions rather than purely data-fit outputs.

Impact

  • Real-time airflow prediction: Airflow speed and direction distributions are estimated instantly for any vent geometry, without invoking CFD.
  • Designer self-evaluation: Engineers can judge whether their concept meets requirements on their own, without submitting an analysis request to a CFD expert.
  • Rapid data-driven exploration: The most performant designs can be filtered from a large candidate pool in a fraction of the time previously required.

Client: Automotive parts manufacturer (undisclosed) Organization: Narnia Labs Category: Automobile

Source: Narnia Labs Case Study #65