A newer version of the Gradio SDK is available:
5.49.1
title: Transport Wing Selector
emoji: 🛩️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false
Transport Wing Selector — AI-Assisted Wing Design
An AI-driven design tool that automates 3D wing generation from a 2D airfoil and optional polar data.
The system evaluates 160 candidate wings using a trained multilayer perceptron (MLP) selector model and returns the best geometry based on your chosen aerodynamic objective:
- min_cd – minimize drag
- max_cl – maximize lift
- max_ld – maximize lift-to-drag ratio
It converts a traditionally slow, intuition-based process into a transparent, interpretable, and reproducible workflow—delivering optimal wing geometries in seconds.
What This App Does
- Generates candidate 3D wing geometries from a user-provided 2D airfoil (.dat/.txt)
- Scores and ranks each design across lift, drag, and efficiency objectives
- Selects the top candidate, visualizing it as:
- A static PNG rendering
- An interactive 3D model (Plotly viewer)
- A downloadable CAD-ready STL file
- A structured JSON summary of aerodynamic and geometric parameters
- Validates the best wing using strip-theory sweep plots (lift, drag, efficiency)
- Explains results through a grounded LLM summary comparing the top design with close alternatives
How It Works
- Input: Upload your airfoil geometry (and optional polar data).
- Objective: Choose your design target (min_cd, max_cl, or max_ld).
- Candidate Generation:
The MLP model evaluates 160 deterministic wing geometries parameterized by span, taper, twist, and chord vectors. - Selection & Visualization:
The top-scoring wing is rendered as an interactive 3D mesh and validated with strip-theory sweeps. - Explanation:
A lightweight language model summarizes why this candidate performs best, grounding the response in numerical features like span, taper, aspect ratio, and score.
Example:“The chosen wing achieves high efficiency by combining a long span, moderate taper, and strong negative washout, lowering induced drag while maintaining lift.”
Features
| Feature | Description |
|---|---|
| Objective Selection | min_cd / max_cl / max_ld |
| Deterministic Mode | Reproducible results with identical geometry and scoring |
| Top-k Comparison | View multiple top candidates ranked by score |
| Validation Sweep | Strip-theory verification for lift/drag trends |
| Exports | .png, .stl, .json artifacts |
| Grounded LLM Explanation | Physics-based summary of the chosen design |
How to Use
- Upload your airfoil file (
.dator.txt) and optionally a polar file. - Choose an optimization objective.
- Adjust parameters (Top-k slider, deterministic mode, AoA sweep).
- Click “Find Best Wing.”
- View results, export files, and read the AI-generated design explanation.
Top-K Candidate Interpretation
The output for the top-k candidates is presented in two parts to help with analysis:
Candidate Results Table This table shows the top-ranked wings predicted by the model. It lists their specific geometric features and the model's validation outputs. These are the real, absolute values for each wing design.
Normalized Comparison Plot The plot that follows the table visualizes how each candidate wing compares to the others across different features. To make them comparable, all values in this plot are normalized to a common scale.
Important: Do not use the plot to get the actual numbers for a wing's features. For the true geometric and performance data, please refer to the preceding table.
System Overview
- Selector Model: MLP trained on 500+ generated wings, each labeled with aerodynamic metrics (CL, CD, L/D)
- Validation: Deterministic scoring and strip-theory consistency checks
- LLM Wrapper: Qwen2.5-1.5B-Instruct, constrained via structured JSON prompt to prevent hallucination
- Deployment: Gradio app on Hugging Face Spaces with STL/JSON export pipeline
References & Credits
Developed by Emily Copus and Kevin Kyi
Carnegie Mellon University — 24-679 Designing and Deploying AI/ML Systems
Instructor: Dr. Chris McComb
- Transport Wing Selector Space
- ViewSTL – for external STL visualization
- 3D Viewer.net – quick in-browser CAD preview
- Introduction to Aerospace Flight Vehicles — ERAU
License & Notes
Educational demonstration.
Not validated for production or certification use.
Outputs and explanations are deterministic, data-grounded, and intended to accelerate conceptual design only.
Final app.py and deployment scripts created with the assistance of Generative AI.