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description: An experiment by YouTuber Neuronautics compared a conventional, highly-optimized propeller against a CFD-designed toroidal propeller. The conventional...
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# Pitting A CFD-Optimized Toroidal Propeller Against A Conventional One

**[Hackaday](https://daily.dev/sources/hackaday)** · 3 min read · 1 upvotes · 0 comments

## Summary

An experiment by YouTuber Neuronautics compared a conventional, highly-optimized propeller against a CFD-designed toroidal propeller. The conventional propeller scanned and simulated at 73% efficiency, while the best toroidal design found through a two-month CFD process using multiple reference frame (MRF) simulation and a neural network to filter results only reached a simulated 62.9% efficiency, and 62% in real-world testing after 3D printing and manual redesign for printability. The comparison wasn't perfectly apples-to-apples since one propeller was commercial and the other DIY resin-printed, but no dramatic efficiency gain was found for the toroidal shape.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://hackaday.com/2026/09/28/pitting-a-cfd-optimized-toroidal-propeller-against-a-conventional-one>

## Questions this post answers

### How does a toroidal propeller's efficiency compare to a conventional propeller in CFD simulation?

A conventional, highly optimized propeller scanned and simulated in CFD achieved about 73% efficiency, while the best toroidal propeller design found after a two-month search using multiple reference frame simulation and neural network filtering only reached a simulated 62.9% efficiency, later measured at around 62% in physical testing. The toroidal design showed no clear efficiency advantage over the conventional shape.

_Anyone weighing propeller designs for a drone or aircraft project can track experiments like this on daily.dev._

### What technique speeds up CFD analysis when optimizing many propeller shape parameters?

Multiple reference frame (MRF) simulation drastically speeds up evaluating many propeller shape variants compared to brute-forcing every combination in full CFD. Even with MRF, testing seventeen shape parameters for a toroidal propeller design still took about two months, and a neural network was trained afterward to filter the resulting data for the most promising candidates.

_Engineers exploring faster CFD workflows can follow techniques like this on daily.dev._

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning)

[View this post on daily.dev](https://daily.dev/posts/pitting-a-cfd-optimized-toroidal-propeller-against-a-conventional-one-ltxdemfz4)

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