NeuralWing
Real-time neural simulation and design optimization of transonic aircraft aerodynamics
Transonic 3d Wing
Geometry and inflow Variations
Real-time Inference
Design-parameter Optimization
NeuralWing Demo
Modify wing geometry and test optimizations to maximize aerodynamic efficiency
The largest 3D transonic wing dataset with real-time neural simulation
We created a dataset of 30,000 steady-state CFD simulations of a 3D wing in the transonic regime, varying four geometry parameters and two inflow conditions. Utilizing our AB-UPT surrogate model trained on this data, the demo shows how design parameters can be optimized in seconds to maximize KPIs such as the lift-to-drag ratio.
large-scale 3D transonic CFD dataset
accurate neural simulation
Real-time Simulation & optimization
Dataset size
30,000 simulations
Dataset variability
4 geometry parameters, speed, angle of attack
Simulation accuracy
AB-UPT achieves 99.5% accuracy on pressure fields
Simulation & Optimization speed
Real-time prediction & design parameter optimization
1000x
Simulation speedup
99.8%
Drag & Lift Data Agreement
30 seconds
Design Parameter optimization
Architecture & Performance
Model Architecture
Type
AB-UPT
Input
Geometry mesh (STL), speed, angle of attack
Output
fields: pressure, friction, velocity
integral forces: lift, drag
integral forces: lift, drag
Training Data
26,000 CFD simulations
Geometry generation for inference
Geometry mesh (STL) is created in real-time from 4 design parameters in a differentiable manner
Performance Metrics
Numerical CFD Simulation (RANS)
4 CPU hours
(not optimized)
(not optimized)
AB-UPT
100 ms for surface fields,
1 sec for volume fields
1 sec for volume fields
Design-parameter Optimization
50 ms per gradient-free step,
150 ms per gradient step
150 ms per gradient step
Pressure coefficient comparison
GFD Ground Truth
AB-UPT
Prediction Error
Friction profile
Pressure profile
The NeuralWing AI model enables real-time simulation and design optimization
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