Pre-Conceptual Design Process using Stallion 3D CFD for Fast Aerodynamics & AI for Geometry Iterations

Vibe-Coding a Bell X-1 Concept with AI CAD + Fast CFD

This note summarizes a simple workflow: use AI to generate “good enough” concept geometry quickly, then use fast CFD to compare design directions before investing time in detailed CAD. The example is a Bell X-1 inspired aircraft concept, created using AI-generated FreeCAD Python, then assessed with Stallion 3D.

Why this matters

Early design decisions are usually made with incomplete information. The goal is not perfection; the goal is to narrow the field of choices quickly. AI-based geometry generation helps you create a plausible 3D model from a written description. Fast CFD then helps you identify the obvious wins and losses (trim tendencies, pressure loading trends, interference hot spots, tail authority risk, etc.) before committing to a “real” CAD model.

What was built

The target look and approximate dimensions were based on the Bell X-1. The aerodynamic surfaces were assigned common airfoils to make the concept testable:

• Wing: NACA 2412
• Horizontal tail: NACA 0012 at -5 degrees incidence
• Vertical tail: NACA 0006

The Gemini prompt used (and why it worked)

The prompt used in Gemini was:

“can you write the python for freeCAD for an aircraft that has the looks and dimensions of the Bell X-1 but the wings has a NACA 2412 airfoil. The horizontal tail has the 0012 at -5 deg insizence. The vertical tail has naca 0006.”

This prompt contains three useful elements:

1. A recognizable reference: “Bell X-1” is a strong anchor for proportions and overall arrangement.
2. Explicit aerodynamic definitions: specifying airfoils and tail incidence prevents the geometry from being “just a shape” and makes it a legitimate candidate for early aerodynamic checks.
3. A clear output format: “python for FreeCAD” strongly constrains the response to something executable.

If you want even more consistent results, add a few practical constraints to the prompt:

• State span, chord, tail spans, and approximate fuselage length (numbers reduce ambiguity).
• Ask for a single script that builds solid bodies (not only surfaces) when possible.
• Ask the script to group parts into named objects (Wing, Tail, Fuselage) for easy editing.
• Request parameters at the top of the script so you can “tune” dimensions without rewriting code.

Suggested workflow: AI CAD → quick cleanup → early CFD

1) Generate concept geometry quickly

Use AI to produce a FreeCAD Python script that creates the fuselage and lifting surfaces. Do not overfit details. At this stage, you are trying to capture the overall layout (wing position, tail volume, fuselage shape, and incidence angles) well enough to learn something from analysis.

2) Sanity-check geometry (do not “CAD-polish”)

Typical quick checks:

• Are the wings/tails located where you intended (relative to fuselage length and CG guess)?
• Do incidence angles match your prompt (e.g., horizontal tail at -5 degrees)?
• Are the surfaces oriented correctly (no flipped normals / inverted sections)?
• Is symmetry sensible (if using a symmetry plane in CFD)?

3) Run early CFD to compare design directions

Once the shape is plausible, run CFD to identify major pressure trends and interference regions. The attached CFD visualization (surface pressure in Pa) is a good example of what “early” analysis should reveal: loading patterns on the wing and tail, fuselage pressure distribution, and areas where geometry interactions are likely to matter.

What you can learn from early CFD (without pretending it is final)

Early CFD is not a replacement for detailed design CFD, wind tunnel testing, or flight test. It is a way to avoid obvious mistakes early and to reduce the number of designs you carry forward.

Practical early questions to answer:

• Does the wing loading look reasonable? (spanwise loading trends, tip behavior, large gradients)
• Is the tail doing what you expect? (incidence effects, tail pressure response, potential authority concerns)
• Any strong interference zones? (wing-fuselage junction, tail-fuselage junction, etc.)
• Are there “hot spots” that suggest geometry changes? (local pressure concentrations, unexpected gradients)
• Does the concept look stable-ish? (not a full derivatives study—just obvious stability/trim red flags)

Where Stallion 3D fits

Stallion 3D is well-suited to this stage because it is designed for fast setup and frequent iteration. In early concept work, you do not want a workflow where every run feels expensive or slow. You want to test more ideas, not fewer.

A practical advantage is licensing: Stallion 3D is cost-effective and does not use a pay-per-run model. That matters because early design is inherently iterative. If you are comparing multiple geometry variants, multiple angles of attack, or small configuration changes, “run metering” becomes friction. Removing that friction is part of moving faster.

Recommended mindset: “cheap learning” before “perfect geometry”

The best use of AI CAD is to accelerate learning. Generate geometry fast, run CFD fast, and only then decide which directions deserve detailed CAD and higher-fidelity analysis. In other words:

• AI helps you get from idea → 3D model quickly.
• Stallion 3D helps you get from 3D model → aerodynamic insight quickly.
• Detailed CAD comes after you have reduced uncertainty and narrowed the design space.

Summary

AI-generated FreeCAD scripting can produce useful concept geometry in minutes. That geometry is not the final answer, but it can be good enough to run early CFD and compare design directions. Stallion 3D is a practical tool for this stage: fast setup, high-accuracy workflow, and cost-effective licensing without pay-per-run friction.

If you are doing early aircraft concepts and want to iterate quickly from rough geometry to meaningful aerodynamic feedback, Stallion 3D is designed for exactly this type of work.

Learn more ➡️ https://www.hanleyinnovations.com

Thanks for reading.