A Strategy for Faster Aerodynamics Analysis using Stallion 3D

Solving Half-Geometry Models in Stallion 3D Using Symmetry

In many aerodynamic problems, the geometry is symmetric about a plane. When this is the case, the computational model can be reduced to half the physical geometry. This approach either:

• Increases solution resolution for the same computational cost, or
• Reduces runtime by approximately 50% while maintaining resolution.

This short example demonstrates how to apply symmetry in Stallion 3D for a full aircraft configuration.

Purpose of Half-Model Analysis

When symmetry exists:

• The flow physics are mirrored across a plane.
• Only half the domain must be solved.
• Grid density can be increased for the same memory footprint.
• Turnaround time is reduced without sacrificing accuracy.

This is especially useful for:

• Conceptual aircraft design
• Wing-body configurations
• Early trade studies
• Parametric geometry comparisons

Step-by-Step Workflow

Step 1 – Import the STL Geometry

1. Go to Design → Import STL
2. Load the aircraft geometry.
3. Confirm the orientation and scale.

If you intend to use symmetry, only half of the geometry needs to be present (aligned with the symmetry plane).

Step 2 – Define the Computational Boundaries

1. Select Size / Scale
2. Set the CFD boundaries relative to the STL geometry.
3. Leave default settings unless refinement is required.
4. Click OK

The boundaries should extend sufficiently far from the geometry to prevent artificial blockage.

Step 3 – Inspect the Geometry

Use either:

• Visualization → View Geometry Only, or
• The wireframe view in the Design window

Verify:

• No unintended gaps
• Proper orientation
• Symmetry plane alignment at Y = 0 (or your chosen plane)

Step 4 – Configure the CFD Solver

Navigate to CFD Solver → Setup CFD Solver.

Recommended starting settings:

• Approximately 1,000,000 cells
• Initial X, Y, Z divisions appropriate to domain size
• Enable near-body refinement splitting

Confirm:

• RANS (Reynolds-Averaged Navier-Stokes) model selected
• Turbulence model appropriate for your case

Click OK.

Step 5 – Apply the Mirror Boundary Condition

This is the key step.

1. Select Ground Effect → Mirror Image
2. Choose Mirror at Minimum Y
3. Click Apply
4. Go to the Dimensions tab
5. Set Minimum Y = 0

This aligns the lower boundary with the symmetry plane. Stallion 3D reflects the solution across that plane internally.

Step 6 – Verify the Mirror Setup

Return to the geometry view. You should observe:

• The physical half-geometry
• A mirrored computational image
• A symmetry plane replacing the removed half

Step 7 – Generate Grid and Solve

1. Go to CFD Solver
2. Select Generate Grid and Solve Flow

After the solver converges (for example, ~4,000 iterations in this demo), the pressure field will be available for post-processing.

The solution shows pressure distribution on the physical half, with the symmetry plane replacing the opposite side, providing a full aerodynamic solution at roughly half the computational expense.

Step 8 – Extract Pressure Coefficient (Cp) Slices

Use surface graphs and spanwise Cp slices to compare stations across the wing. Example span stations:

• y = 3 m
• y = 4 m
• y = 5 m

These results are commonly used for load integration, structural sizing input, performance analysis, and validation against reference data.

Why This Matters

Using symmetry correctly enables faster iteration during early design, higher resolution grids within memory limits, and efficient conceptual evaluation of aircraft configurations. For UAV designers and small engineering teams, this can significantly reduce turnaround time while preserving fidelity.

Summary

To solve half-geometry models in Stallion 3D:

1. Import STL
2. Define CFD boundaries
3. Set solver parameters
4. Apply mirror boundary at the symmetry plane
5. Generate grid and solve
6. Extract aerodynamic data

The same method applies to wings, fuselages, hydrofoils, and other symmetric configurations.

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For more information, visit: hanleyinnovations.com

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. 

Aerodynamics Tools for Rapid Aircraft Conceptual Design

Cessna 210 NLF Wing: Four Models, One Story

This figure shows a simple but useful comparison for a real, non-trivial configuration: a Cessna 210 modified with a NASA Natural Laminar Flow (NLF) wing. The goal here is not “pretty CFD,” but practical validation for conceptual design work.

1) Stallion 3D automatic gridding saves time on real geometries

The Cessna 210 is not an academic “wing-only” case. It has a fuselage, wing-body junctions, tail surfaces, and the usual geometric complexity that shows up immediately when you try to run a 3D analysis.

With Stallion 3D, the gridding step is not a week-long detour. Automatic Cartesian gridding makes it practical to iterate on complex shapes without turning the meshing workflow into the main project.

2) Accuracy matters: Stallion 3D validated against a NASA experiment

The comparison includes experimental results from NASA Technical Paper 2772 (full-scale general aviation airplane equipped with an advanced NLF wing). That dataset provides a grounded reference for lift and drag trends over angle of attack.

In the plots, Stallion 3D tracks the experimental behavior well over the usable range. For conceptual work, this is the point: you want predictions that are directionally correct, quantitatively reasonable, and stable enough to support decisions.

3) Vortex lattice (3DFoil) wing-tail results bracket the trends

A vortex-lattice model (via 3DFoil) is also included for the wing-tail configuration. As expected for an inviscid lifting model, it provides a fast, low-friction reference that helps “triangulate” the physics.

When the VLM curve brackets or parallels the experimental/CFD trends, it increases confidence that the configuration-level aerodynamics are being captured consistently (especially in the pre-stall regime where conceptual sizing happens).

4) Why this matters: validation for conceptual design on complex shapes

Taken together, the four views in the figure (experiment + multiple computational models) provide a practical validation set:

• Experiment: the anchor point — what actually happened in the tunnel.
• Stallion 3D: a high-utility conceptual CFD tool that can handle real geometry and produce forces and moments.
• Vortex Lattice (3DFoil): fast wing-tail estimates that add context and help bound expectations.
• Cross-comparison: agreement across models is often more useful than any single curve by itself.

This is the workflow I care about: reducing blind spots early. When multiple models (plus experimental data) tell a consistent story, you can move forward faster and spend your time on design choices instead of debating whether the analysis is “real.”

Where this approach is useful

This same validation logic applies beyond the Cessna 210 example. Once the workflow is in place, it scales naturally to:

• UAVs (wing-body-tail interactions, payload pods, booms, blended shapes)
• Light aircraft (junction flows, downwash effects, tail sizing, drag budgeting)
• Sails / marine foils (lift/drag trends, induced effects, configuration comparisons)
• General projects where geometry complexity is unavoidable and iteration speed matters

Summary

• Automatic gridding in Stallion 3D keeps complex geometry analysis practical.
• Results can be validated against experiment (here, a NASA NLF wing dataset).
• 3DFoil vortex-lattice wing-tail predictions provide a fast bracketing model.
• Multiple models + experiment = better confidence for conceptual design decisions.

If you’re doing early-stage design and want “good physics quickly” on real geometries, this is the kind of comparison that matters.

Please visit Hanley Innovations for more information ➡️ https://www.hanleyinnovations.com

First try at AI for Design & CAD with CFD Simulation for Aerodynamics

#AI + Simulation: a 1-hour aerodynamics workflow (Gemini → Copilot3D → Stallion 3D)

Sometimes the fastest way to learn is to let a messy workflow happen… then measure what physics says.

Quick summary

1) I asked Gemini for the “best airfoil shape” → it generated something that looks like a supercritical airfoil.
2) Microsoft Copilot 3D turned that into an STL → surprisingly it became a biplane-ish configuration.
3) Stallion 3D solved the CAD at M = 0.825 within the hour.

Figure: (1) Gemini “best airfoil shape” prompt result, (2) Copilot3D STL interpretation, (3) Stallion 3D pressure visualization + Cp plot.

Step 1 — Asking AI for “the best airfoil shape”

I used a deliberately vague prompt: “draw a picture of the best airfoil shape”. AI doesn’t know your mission requirements (Re, Mach, thickness constraints, lift target, stall margin, structure, manufacturing, etc.), so the output is always going to be a guess—but it’s still interesting what it “reaches for” when asked.

In this case, Gemini returned an airfoil that looks supercritical-ish: thicker mid-chord, flatter upper surface, and a sharper-ish trailing region. Is it “best”? No. But it’s a recognizable design intent: manage transonic pressure gradients and reduce wave drag.

Step 2 — Converting the concept into geometry (and getting a surprise)

Next, Copilot3D generated an STL from the concept image. Here’s the fun part: it didn’t produce a clean monoplane wing. It produced something closer to a biplane / joined-surface interpretation.

This is a good reminder that “image → CAD” isn’t a deterministic pipeline yet. The tool is inferring 3D structure from ambiguous cues—so you can get creative geometry even if you didn’t ask for it. That’s not a failure. It’s a feature (as long as you validate the aerodynamics).

Step 3 — Let physics vote (Stallion 3D at M = 0.825)

Once the STL exists, you can stop debating what the shape “means” and just run it. I brought the CAD into Stallion 3D and solved at Mach 0.825. From there, the workflow becomes familiar: surfaces, pressure/Cp trends, and whatever integrated outputs you care about (lift, drag, moments).

The point isn’t that the AI created a production-ready aircraft. The point is that you can now move from an AI sketch to a solvable geometry to CFD-based insight fast enough to iterate.

What this workflow is (and isn’t)

• It is: a rapid way to generate “candidate geometry” when you’re brainstorming.
• It is: a quick filter—physics can reject bad ideas early, before they waste days.
• It isn’t: an optimizer, a certification path, or a substitute for requirements-driven design.
• It isn’t: proof that AI “understands aerodynamics.” It’s proof that AI can accelerate the setup—and CFD can validate the result.

Try your own workflow

If you want to run this experiment yourself, keep it simple:

1. Ask an AI for a concept (airfoil, wing, fairing, inlet—anything).
2. Convert to STL (expect surprises).
3. Run a quick CFD sweep (one condition is enough to learn something).
4. Decide what to keep, what to change, and repeat.

Try your workflow ➡️ https://www.hanleyinnovations.com/stallion3d.html 

(If you’d like, send me your STL + flight condition and I’ll tell you what I’d look at first: Cp trends, shocks, separation risk, and the integrated forces/moments.)