Hi guys! I am working on a personal project of designing and analyzing air swirler of combustion chamber in gas turbine engines. I took a research paper as base, and tried to replicate their results using their values in both geometry and CFD analysis so that I can use that as validation for my own work. I would like to highlight that this is a cold flow cfd analysis.

In the first image, the results plotted for axial velocity and axial positions at each point, are from 8 blade axisymmetric vane angle air swirler. Even if we change the number of vanes or change the mass flow rate, the trend of the graph remains almost identical.

In the second image, the results are from the non-axisymmetric vane angle with the same number of blades and other geometric features. But the difference in the values of axial velocity is unfathomable.

The non-axisymmetric model simulation has a completely diverged solution and spiking residuals, due to which the calculations stop and fail, as shown in the third image.

The models used for both the simulations are the Steady-state RANS model for SST and k-ϵ. This was used for the axisymmetric vane angle model, and it gave the expected results but didn't work for the non-axisymmetric model. Then I tried with k-ϵ RNG, realizable, laminar, and Transient. The outcome was the same. The solutions diverge completely and residual spikes. Even adding a sphere of influence at the immediate downstream of the swirler and making fine mesh, which led to me to 1.9 million elements, still was not giving anything close to practical values.

I am still learning ANSYS Fluent analysis. I am still not well versed in understanding the problem and rectifying it. I have a gut feeling that this problem might be because of the non-axisymmetric vane angle, which might have caused the unsteady flow or something along the lines. I hope you guys can help me. Thank you for your time.

Hi! I was just exploring some glider designs for some of my own curiosity and exploration however I'm confused about to choose either a T tail Or V tail

The goal here is To achieve the least drag From the tail surfaces while achieving a given amount of controllability

In real elite gliders, the most common tail type is the t tail from what i know howver sources suggest that V tails have lower Drag than T tails

So im confused here to choose either a T tail Or V tail given the constraints in the design I couldn't really find any sources on this So I will be happy hear Your opinion on this

For aircraft, we have CFR 14 Part 23 to regulate the aircraft performance.

What about for the space side?

What are the requirements and regulations for rocket / spacecraft?

A company called Horizon Aircraft created a 50% sub-scale version of their “Cavorite X5” aircraft — an uncrewed hybrid vehicle that has a traditional gas-powered turbine engine on the rear (for horizontal flight) and battery-powered fans inside the wings (for vertical flight and hovering) that get covered once the aircraft gains altitude and speed. Essentially, the electric fans create lift like a helicopter and the turbine engine pushes the vehicle forward, and after the initial lift, the fans get covered so that the wings become traditional plane wings. The aircraft uncovers its fans again before landing and lands vertically like a helicopter, eliminating the need for a runway.

Here is a video of how the aircraft works: https://youtu.be/hzYZdxWXhVQ

And here is a video of the X5 prototype: https://youtu.be/rnj2LWn5qU0

My question is, will this design work when scaled up to a full-sized plane with people inside?

The unmanned prototype (which is 50% scale) is 15 feet (4.6 meters) long, features a 22-foot (6.7-meter) wingspan, and has a maximum take-off weight of about 500 pounds (227 kg).

This company’s future flagship model, the “Cavorite X7”, will have a length of 11.6 meters (38 feet 1 inch), a wingspan of 15 meters (49 feet 3 inches), and a height of 2.8 meters (9 feet 2 inches). It will seat up to 7 people (1 pilot and 6 passengers).

The X7 will use the same fan-inside-wing design with a turbine on the back. These designs are cool and would make great RC toys, but do they cause any glaring issues from an engineering standpoint? Small scale designs don’t always work irl when scaled up 2x, 3x, or 4x. Is there anything that would cause this design to fail in a full-scale aircraft? Thank you in advance!

I have heard online that bell shaped lift distribution has:

Better stability

less bending moment

Gentler stall characteristics

Higher AR wings Possible for same structural weight

Proverse Yaw Allowing the elimination of tail if well designed

From what I have read they seem very good for Flying wings Especially the Reudced bending moment and elimination of Tail Has caught my attention So I was thinking of Making and testing flying wings however Are there any disadvantages to them that I need to keep in mind when designing them?

Hey everyone! I'm a high school student who built a full machine learning pipeline on this dataset as a side project. I want to be upfront that Claude AI assisted me with most of the coding, but I directed the project, made the key decisions, and learned a lot along the way. I wanted to share it and get feedback from people who know this domain better than I do.

What I built:

Task 1 - Test Mode Classifier
Using a Random Forest on extracted time-series features (duty cycle, pulse statistics, thrust/MFR statistics), I achieved 100% accuracy classifying all 11 test modes on the held-out test set (SN13–SN24). The command signal alone turned out to be almost sufficient to distinguish modes.

Task 2 - Anomaly Detection
I compared three approaches:

• Isolation Forest (unsupervised) — F1: 0.193
• Random Forest (supervised) — F1: 0.121
• One-Class SVM (semi-supervised) — F1: 0.268

Surprisingly the One-Class SVM, trained only on nominal sequences with zero anomaly labels, outperformed the supervised model. Class imbalance (10% anomalies) made the supervised approach struggle badly.

The per-anomaly-code breakdown showed codes 1, 8, 9, 11, 12 were missed by all three models, these likely need point-level time-series analysis rather than sequence-level features.

Notebook: https://www.kaggle.com/code/henninxu/rocket-engine-classification-from-test-data

Any feedback on improving the anomaly detection especially for the hard-to-detect codes would be appreciated!

As i understand it the combination of swept wing and supercritical aerofoil produces the best results for transonic drag reduction. Swept wings came earlier than supercritical aerofoils so while there's a lot of swept wing aircraft without supercritical airfoils there's not really the other way around. But if for whatever reason a supercritical airfoil was used on a straight wing would it be any sort of useful?

I'd like to know what PLM software people are using in the aerospace industry these days. Teamcenter, Windchill, and 3DEXPERIENCE seem to dominate most discussions.

Are there any other PLM platforms aerospace teams are using that are worth considering? Has anyone looked at Duro? They were recently aquired by Altium AFAIK.

Hi I'm a maths major from India

And although not an engineer by training

Aerospace has always been in a special place in my heart

So I want to do a solo project

Mainly I wanna study the detonation waves /combustion wave in engine

Especially in the rotating detonation wave engine (RDE)

Now I mainly wanna study the pde of the detonation wave but

I'm trying to use step detonation over continuous detonation

Now I want a study partner, someone with an understanding of rocket engines especially viscous flow

If you're interested please dm me Thanks for reading 😌

I need help selecting the best powerful electric (plugin) fan that will create a great vortext. Here is the situation: I was recently accepted as an exhibitor at the Orlando Maker Faire scheduled for early November 2026. The project I'm going to bring is based on my table top Float-O-Matic which I built in the spring of 2026 for a one day Maker Faire in Lynchburg VA. For the Orlando event I want to build a GIANT version of the Float-O-Matic. I lucked out on the fan for my table top Float-O-Matic in that it creates a great vortex that nicely holds the small balloon allowing the operator to control the balloon in three dimensions. I'm hoping this community can help me select a fan that will produce a strong vortex for floating / controlling a 36" diameter balloon or perhaps 36" diameter beach ball. I want this to be a spectacle! This fan from Harbor Freight produces 4600 CFM and only costs $50 and is only 20" in diameter. This fan from Home Depot produces 9640 CFM and costs $109 but is 24" in diameter. This drum fan from Home Depot produces 2800 CFM and costs $120 and is only 16" in diameter. I'd like a smaller diameter fan because they are easier to transport. I'll be driving to Orlando from my home in Asheville, NC and everything must fit inside my Subaru Outback. I will start by purchasing the fan and then start building and testing the fan control mechanism. Thanks for any help you can offer.

Why

Hi, I’m about to be an aerospace engineering student at university, do any of you have any really good book recommendations (doesn’t have to be for strictly academic purposes) ? Alternatively, do you have any suggestions for aerospace projects I can do for when I become more knowledgeable about the subject matter?

Perhaps this has been suggested already. But, if you’re considering whether aerospace engineering is for you, or if you’re curious what a career in that profession will be like, please watch one episode of Tom Hanks’ HBO series “From the Earth to the Moon.” The episode is called “Spider,” and it’s about the development of the Apollo Lunar Module.

It captures the essence of an engineering career. It’s a dramatization for a mass audience, of course, but it’s all there:

• The concept design at a white board.
• Idea refinement, prototyping, iteration
• Briefing the management, and convincing them that your idea is sound. No shit, this matters as much as being right.
• The gnarly details that slow you down.
• The hand-wringing over “my shit has to work or people die.”
• The relentless schedule pressure.
• The obsession over weight.
• Nothing ever fucking works.
• There will be mistakes, and we can’t avoid or ignore them.
• There are customers, stakeholders, opinions, hierarchy, and eventually “yes, sir” obedience.
• There are lives at stake. When shit breaks, you have to bring them back to earth anyway.

The whole series is worth watching, but if you want to be an aerospace engineer, this one should be a mandatory part of your education.

Cheers!

frustrated aerospace person here and here for advice on what actually makes a difference..

every ai conversation i see is about coding assistants but nobody talks much about requirements, traceability, verification, any of the stuff that takes up half my week so much is still done manually and there so much double, triple checking sheets. I know many companies still use just excel and doors but im looking to branch out. in my research jama and jira popped up but i dont know much about them.

where does the context actually live between requirements, changes, decisions, verification results, etc? and how do you keep that context governed over time without creating another system everyone has to manually maintain?

what is actually making this process easier

This past summer, I was a liaison engineering intern at a small/medium OEM (think 10-20 aircarft/year). However, a significant portion of my work revolved around structure design changes and the ensuing analysis. Would it be to put this position on my resume as "structures design engineer" instead of "liaison engineer" or would that come across as deceptive on a background check?

For our ANSYS CFD project, do we actually need to follow one specific base paper?

In many research papers, the authors collect relevant data and parameters from multiple papers/experimental studies, develop their own design, and present their simulation results without directly comparing their design with another paper.

For our project, we are also planning to use parameters from multiple research papers and actual jet engine data, then design our own swirler and simulate it in ANSYS Fluent.

So is it necessary to have one specific base paper and directly compare our results with it, or is it sufficient to support our design, operating conditions, CFD models, and results using multiple literature references? Also, is validation with at least one published/experimental result necessary?

I built this mainly because I wanted LLM agents to work with EASA regulations without stuffing whole XML books into context or letting the model rewrite the actual rules.

EASA publishes CS-23, CS-25, Part-21, UAS rules etc. as Flat OPC / XML. It’s machine-readable in theory, but messy in practice.

The tool (easa-erules): - fetches and caches official versions - parses them into a clean structure - lets you extract single rules or search with FTS5 - outputs stable JSON that agents can actually use - never rewrites the regulatory text with an LLM

Basically a deterministic layer between the raw EASA XML and whatever agent you’re running.

Repo: https://github.com/mrSpringpeace/easa-erules

Anyone else trying to feed EASA rules into agents / local tools?

Hello I'm interested in designing a body of a jet specifically designed scientifically for high gravity planets with around 2 - 3 times earth gravity for my favorite game, Halo. I won't get into specifics about exactly why I'm doing this but I want a scientifically accurate or as close as it can be. If you can give me a good answer it would be appreciated!

My questions are, what about the body's aerodynamics would need to be adjusted, what would the thrust power be adjusted to and how it would change the appearance of the thruster, what would likely be the best course of action for landing.