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.)

Stallion 3D for Fast Aerodynamics Design Tradeoffs (no cloud, no CFD team)

One of the realities of engineering design is that many decisions are made before a detailed CFD campaign ever makes sense.

Landing gear placement, strut geometry, fairings, brackets, pylons, and similar components all introduce aerodynamic penalties. The question is usually not “what is the final answer?” but rather:

• Is configuration A better than configuration B?
• How much drag or side force did this change introduce?
• Is this direction worth pursuing further?

This is where Stallion 3D fits into the workflow.

Design tradeoffs without a heavy CFD process

The examples shown compare two landing gear configurations using Stallion 3D. The goal is not high-end turbulence modeling or mesh tuning. The goal is fast, consistent comparison between design options.

With Stallion 3D, design engineers can:

• Evaluate component-level tradeoffs early
• Make informed decisions without waiting on CFD specialists
• Avoid cloud compute costs and pay-per-run models

The solver and grid generation are automatic and repeatable, so changes in forces and moments reflect geometry changes, not meshing differences.

What Stallion 3D provides in this workflow

• Consistent automatic grids across multiple design variants, enabling meaningful A/B comparisons.
• Subsonic, transonic, and supersonic capability for evaluating components across a wide flight envelope.
• Designer-friendly workflow with no need to consult CFD experts for every iteration.
• Direct, interpretable outputs, such as CD_gear_2 > CD_gear_1.
• No pay-per-run cost for quick conceptual analysis.

Where this fits in the bigger picture

Stallion 3D is not intended to replace detailed CFD at later stages. Instead, it helps narrow the design space early so higher-fidelity tools are applied only when they add value.

For many projects, this reduces iteration time, cost, and dependence on limited CFD resources, while keeping decisions grounded in physics.

If you have questions about using Stallion 3D in your design process, feel free to reach out.

Learn more ➡️ https://www.hanleyinnovations.com/stallion3d.html

Aerodynamics of the NASA QueSST X-59 Quiet Supersonic Transport

📽️ Watch the YouTube Video

X-59 Quiet Supersonic Transport Study Using Stallion 3D

I ran a new quiet-supersonic study at Mach 1.45 and 55,000 ft using the built-in atmosphere tables and Cartesian solver in Stallion 3D. The goal was to reproduce and understand the kind of pressure distribution seen in the NASA X-59 QueSST demonstrator, which recently completed its first flight. The idea is the same: manage the shock pattern so the ground hears a soft “thump” instead of a sonic boom.

Shock Management Along the Nose

The simulation shows a controlled series of small compressions marching down the forebody rather than one big, coalesced shock. That’s exactly what quiet-supersonic shaping is about—spreading the pressure rise (Δp/Δx) gradually so the far-field signature becomes a sequence of gentle steps instead of a single N-wave.

At these flight conditions, the distributed shock train is similar to what the X-59 team reported during their low-boom configuration tests. It’s encouraging to see Stallion 3D’s Navier–Stokes solver naturally produce the same kind of flow behavior on a simple Cartesian grid.

Canopy and Inlet Shoulder Interaction

Right behind the cockpit, a red-blue compression and expansion pattern forms where the fuselage grows into the wing root. This region is a classic challenge in supersonic design—where cross-section growth and lifting surfaces meet, shocks can thicken and contribute to secondary noise.

It’s good to see that Stallion 3D’s refinement zone resolves these local gradients clearly, without any hand-built body-fitted grid. The automatic cell concentration gives an accurate look at how geometry transitions affect both drag and acoustic signature.

Aft-Body and Tail Effects

The aft wing and tail surfaces are doing real aerodynamic work. The pressure remains mostly clean, but there are still distinct compression and expansion regions being shed downstream.

In low-boom design, the rear shaping is as important as the nose. The aft body determines how the pressure signature closes—the part that controls how the sonic waveform ends. That’s the part that often separates a “thump” from a “bang.”

Refinement Zone and Solver Performance

The local grid density around the aircraft shows that the refinement box is working exactly as intended. It captures oblique shocks and shear layers efficiently, even at Mach 1.45, without requiring a fitted mesh.

From a numerical standpoint, this confirms that Stallion 3D’s Cartesian method is practical for supersonic concept studies—especially for early X-59-style configurations or general quiet supersonic transport layouts.

Realistic Flight Condition

The run used true high-altitude conditions (55,000 ft, Mach 1.45) from the built-in atmosphere model. These are the same conditions typically quoted for quiet-supersonic cruise tests and community response research under NASA’s QueSST program.

That realism matters for both acoustics and aerodynamics. At these pressures and densities, thin, swept lifting surfaces behave differently than they do in low-altitude transonic tests.

Next Steps

• Extract the far-field pressure trace along the ground track (Δp vs. time) to evaluate perceived loudness.
• Quantify lift, drag, and moment coefficients (C_L, C_D, C_M) to separate wave drag from viscous effects.
• Run sensitivity tests by shortening the nose or modifying canopy cross-section to see how it reshapes the shock train.

Conclusion

This quiet-supersonic run demonstrates what Stallion 3D does best—showing real aerodynamic detail from first principles without external meshing or post-processors. The solver’s ability to capture distributed shocks, canopy interactions, and aft-body effects all in one pass makes it an effective tool for early design of low-boom aircraft like the X-59 QueSST.

It’s not about pretty colors; it’s about credible data at real flight conditions. The results show a clean, believable Mach 1.45 solution with controlled shock structure—the kind of solution that points the way toward practical, certifiable overland supersonic transport.

Learn more ➡️

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

Multi-element Airfoils analysis with arbitrary shapes: Learn more about the best airfoils tools

Do Fish Swim Like Multi-Element Airfoils?

In nature, a school of fish moves as a coordinated system. Each fish swims in the wake of another, taking advantage of pressure differences and induced flows that reduce drag and save energy. It’s a clean example of fluid mechanics at work — and not too different from how engineers design multi-element airfoils for high lift.

The image above shows a simulation created from fish-shaped outlines. The shapes were first traced as simple drawings and then captured using Airfoil Digitizer. Airfoil Digitizer lets you turn almost any outline — hand-drawn, scanned, or imported — into an analysis-ready shape. You are not limited to NACA airfoils or standard sections. If you can sketch it, you can analyze it.

After digitizing the shapes, I placed them together and ran a potential flow solution in MultiElement Airfoils. This solver computes the velocity and pressure field around multiple bodies at once, and shows how they interact. The colored contours represent pressure: blue for low (suction) regions and red for higher pressure. You can see how each “fish airfoil” changes the flow around its neighbors, very much like the interaction between a slat, a main wing, and a flap.

This is the interesting part: even with playful shapes, the physics is still there. You get wake shielding, suction peaks, and local acceleration in the gaps. That’s the same family of effects we care about in real applications — multi-element wings, hydrofoils, propeller/wing interference, and UAV control surfaces working close together.

The workflow here was:
1) Sketch or outline a shape
2) Capture it with Airfoil Digitizer
3) Arrange multiple elements and solve the flow in MultiElement Airfoils
4) Visualize pressure and interaction

It’s a fun demonstration, but also a serious one. Airfoil Digitizer gives you full control over the geometry. MultiElement Airfoils lets you study how multiple lifting surfaces behave together, not just one at a time. Together they make it easy to explore ideas, test concepts, and see the aerodynamics before you ever build a model.

Visit ➡️ https://www.hanleyinnovations.com

Best regards,
Patrick

CFD and Aerodynamics of a Blown Wing for eSTOL, STOL & eVTOL Aircraft Design and Analysis

This guide condenses the video transcript into a short, actionable tutorial. Follow the steps below to replicate the workflow shown in the video.  Applications include eSTOL, STOL and eVTOL aircraft.

Tutorial Steps (from Transcript)

1. Hello and welcome to Hanley Innovations. Today we will outline how to set up actuator discs in Stallion 3D to implement a blown wing concept.
2. First, we create the wing in Stallion 3D using the built-in geometry tools. This wing has a span dimension of 4 m and a cord of 1 meter.
3. It uses a NACA 4412 from the built-in library. Next, we enter the actuator discs parameters.
4. For all four, we set a force of 500 Newtons. We copy the first disc and set the Y center to minus 0.75.
5. Next, with the same copy, paste a discs with Y centers of 0.25 and minus0.25 respectively to complete the propulsion distribution. Next, set up the CFD using 1 million cubes with the initial X, Y, and Z settings of 2, 2 and two.
6. Use the default Navier Stokes solver. Then click the generate grid solve flow menu.
7. Stallion 3D will automatically generate the grid and solve the flow. The results show the effects of the disc's prop wash over the wing and in the wake.
8. We can now compare the lift force of the unpowered wing to that of the blown wing. The unpowered wing has a lift force of 24 lb in the 20 m/s flow field.
9. The blown wing has a lift of 70.8 lb. 
10. Until next time, thanks for watching.

 For more information, please visit https://www.hanleyinnovations.com

Quick and Accurate UAV Aerodynamics Analysis using Stallion 3D

Solved by Default 🛠️ — Import • Mesh • Solve in Stallion 3D A quick walkthrough by Dr. Patrick Hanley (Hanley Innovations)

Click the image to watch the short demo on YouTube.

In this quick demo, we import a drone STL, let Stallion 3D auto‑configure the CFD boundaries and domain sizing, pick a sensible default mesh, and run the solver—going from geometry to pressure contours in minutes.

What the video covers Import geometry via Design → Import STL (ASCII or binary). Set units (e.g., meters) and position/orientation. Automatic domain & boundaries: Stallion 3D sizes the CFD box and boundary conditions from the STL—so you don’t have to hand‑tune the grid extents. Optional geometry quality check: If your STL has gaps/holes, run the quick check to mitigate issues before grid generation. CFD setup with smart defaults: choose a mesh density (Quick, Medium, Large; example shown: ~1 M cells). The default solver and domain dimensions are good starting points. Generate & solve: Start meshing and the flow solution in one click. Visualize results: View the 3D geometry and pressure distribution; add legends/units (Pascals) with the graph options.

Why this workflow is fast No manual domain sizing—it’s solved by default. Defaults that “just work” for early design checks. Applies across subsonic, transonic, and supersonic regimes for rapid concept iteration.

Watch the short demo Prefer reading? Reply with questions—happy to help you try this on your geometry.

© Hanley Innovations • This email is informational. Video & demo: Dr. Patrick Hanley.