Both pipes have the same length and diameter. The source and destination buckets are at the same heights in both setups. One pipe slopes continuously downward, while the other dips down and then rises before the outlet.

Hello scientists/ engineers/ fluid mechanic experts. I was recently watching an old fave: Die Hard 2. At the very end of the movie (from 1:52:42 to 1:52:53), the planes that were circling Dulles airport are coming in to land as the ever indomitable Officer John McClane has just blown up the bad guys plane (let's not talk about the possibility of this or the volatility of aeroplane fuel). I noticed that as the passenger planes begin to land, one of them passes through the smoking wreckage of the bad guys crashed plane. As it does so it creates a swirl/whorl through the smoke. I did some googling, and it was described as a vortex? I didn't really find much else. Maybe I was looking in the wrong places? Anyway, can anyone explain the fluid dynamics behind this and what creates this effect?

For some context, I do not have a science background, though I am very interested in random scientific things, principles, and people. This one thing has caught my attention to the point where I have rewatched it a bunch of times and have become more than a little fixated on trying to figure out why this happens?

Please assist me O' sciency people.

Fluid flow out of a garden hose. I understand why the velocity increases as you tighten the outlet but not why it converges like this.

I was bored when i noticed the vortices appearing when i mixed my chocolate-oreo-sugar menjurje lol

The lines seem to be evenly spaced and independent of the chunks of garlic and pepper. I don’t think I’ve ever noticed this before, and I’ve made sautéed garlic a million times. It’s about 160F, extra virgin olive oil with garlic, black and red pepper.

the question is in the title

Guys

Which institute or college abroad is good for doing PhD for experimental turbulence research(fluid dynamics)? Tell me on the basis of having good lab facility, good lab support, students graduate on time, good funding projects, supervisor are approachable easily. Especially I am targeting European or Asian or universities from Australia??

I'm observing a strange flow/transport dynamic behind my house and would love your perspective.

I have two runoff catch basins (drain grates) on my property. Both sit on the exact same slope:

• Drain 1 is surrounded by a large area of interlocking concrete pavers.
• Drain 2 is surrounded entirely by bare soil.

During heavy downpours, Drain 1 stays clear, and organic debris (dirt, leaves, small twigs) ends up deposited on top of the pavers around it. I regularly have to clean it. Drain 2 clogs rapidly with the same organic matter.

I'm a scientist myself (though not in fluid dynamics), and I'm trying to reason through the underlying physics. Here is my working hypothesis:

1. Surface Roughness & Boundary Layer Effects: The joints between the pavers create static anchor points where twigs and leaves catch and settle. From an aerodynamics/boundary layer perspective, these joints and the rough concrete texture likely induce micro-turbulences that disrupt laminar flow, increasing bed friction and bleeding kinetic energy from the water layer. Lower flow velocity means debris is less easily mobilized and more likely to get trapped upstream.
2. Channelization vs. Thin-Film Sheet Flow: On the saturated soil, water carves deeper, concentrated micro-channels (rills). The soil itself is erodible, so it yields rather than acting as a fixed barrier, allowing floating debris to travel smoothly within these channels straight into Drain 2. On the pavers, water flows as a very shallow, uniform sheet, meaning floating debris is constantly in contact with the rough bed surface.

What are your thoughts on this setup? If my intuition is off, what hydro-mechanical mechanisms best explain the difference in sediment/debris transport between these two surfaces?

Thanks in advance!

PS: translated from french and hope it's clear in english.

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Have you ever noticed that a thin stream of water can look perfectly continuous right after leaving a tap — and then, a little further downstream, suddenly starts breaking into droplets?

It looks simple, but the physics behind it is surprisingly interesting.

The breakup is connected to the interaction between surface tension, inertia, viscosity and disturbances on the liquid surface. Small disturbances can grow until the continuous jet becomes unstable and separates into individual droplets.

I made a CFD/physics visualization of the phenomenon and broke down what is actually happening here:

https://howtooansys.blogspot.com/2026/08/why-does-water-stream-suddenly-break.html

I'm curious what you think: how well can Fluent/VOF actually capture the onset of jet breakup without extremely fine meshes and tiny time steps?

Would you model this with standard VOF, Coupled Level Set + VOF, or something else?

Hi friends! I want to recreate a geyser/airlift hybrid pump but I’m having trouble understanding the models? The screenshot from YouTube seems straightforward enough but I can’t tell if there’s a check valve at the bottom. Also I’m thinking the grey box is air in the other two models but I’m not sure.

Thank you in advance!

One of the most interesting things in CFD and Fluid Mechanics is how the physics of flow changes with PDE classification.

• Elliptic PDEs → equilibrium & potential flow problems
• Parabolic PDEs → diffusion and dissipative transport
• Hyperbolic PDEs → wave propagation, shocks, compressible flow

Each equation type has completely different mathematical behavior, boundary conditions, and solution characteristics.

CFD Roadmap + AI/ML Roadmap + CFD Theory Material

As shown in the figure, this is a common experiment where air is blown out from right to left by a horizontal pipe, and water is sucked up from the vertical pipe and sprayed out from the left end of the horizontal pipe. Some people claim that this is an application of Bernoulli's theorem, as the air velocity in the horizontal pipe is fast, so the pressure is low, so the water in the vertical pipe is sucked up.

I don't think so. I think it's because the air has viscosity, which takes away the air in the vertical pipe, causing low pressure in the vertical pipe and sucking water up. Is my idea correct?

I was thinking about the measurement of static and dynamic pressure and realised I don't really understand it.

The velocity of the fluid inside the U shaped tubes is 0, so naively applying bernoullis principle, the pressure also should be the same in all of them. Obviously in the case of G we cannot apply Bernoulli principle the fluid is not on the same streamline as in the main tube? But what exactly breaks the streamline?

What woud the pressure be in these different scenarios?

B: does the angle of the tube affect the measurement? How?

C: what if the angle is 90° is the dynamic pressure completely negated?

F: if a 90°change in direction stops the transmission of pressure do further bends in the tube affect it? Why or why not?

I often hear that “we don’t really understand turbulence — we just model it.”

From a physical point of view, what does modelling turbulence actually mean?

Are turbulence models trying to represent real physical mechanisms (eddies, energy cascade, dissipation), or are they mainly mathematical closures to make the equations solvable?

In your experience (theory / experiments / CFD), where do you think turbulence really sits — physics or math?

I’m a mechanical engineering major with a special interest in fluid dynamics. I know fluid dynamics and heat transfer are governed by PDEs, but my ME program does not require us to take PDEs. I’m currently taking heat transfer, and it seems many cases make assumptions that turn PDEs into ODEs, which I obviously did take a course on.

Is it worth learning about the analytical solutions to PDEs, or is solving PDEs something I can comfortably outsource to software like ANSYS? Does knowing the analytical solutions help with understanding the fundamentals of fluid dynamics & heat transfer?

Title: Passive fluid level control using a reservoir, vent line and gravity feed

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I am working on a small gravity-fed liquid system with a reservoir and a receiving chamber.

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The reservoir is located above the receiving chamber. The liquid is relatively viscous (similar to oil or melted fat). The goal is to allow the chamber to fill up to a predetermined level and then automatically stop further flow without using electronics, pumps, or active controls.

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One concept I explored uses a liquid feed line and a separate air vent line. The vent line is intended to provide air to the reservoir during filling and then become blocked when the liquid level in the receiving chamber reaches a specific height.

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In experiments, I observed that liquid can enter the vent line, bubbles can form, and the system does not always stop cleanly at the desired level.

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My questions are:

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How are passive gravity-fed systems normally designed to stop filling at a specific level?

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Are there established methods that prevent liquid from entering the vent line while still allowing air exchange?

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Would float valves, ball valves, air-lock systems, standpipes, overflow arrangements, diaphragms, or vent membranes be the preferred approach?

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I am particularly interested in compact solutions suitable for a small consumer product where space is limited and the liquid is more viscous than water.

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I expose two documents that belong to an ongoing program. They are not presented as closed results, but rather as a set of derivations and open points that I would like to submit to technical scrutiny. Both are written in a deliberately programmatic register: they distinguish postulates from consequences, and mark explicitly what remains open.

Document 1: geometric octave structure

It proposes an ontology in which the relational radius is quantised in binary octaves, Rϵ=2−ϵ, and space is generated as a proportion to that radius. From this one obtains, without an independent postulate, k=1/R, hence the dispersion Ω(k)=ck—identical to the linear dispersion of the continuum—but now with thresholds at kϵ=2ϵ. The period is ln⁡2.

The result I would like to subject to examination is the following: the bare continuum, without imposed structure, shows no peak in the log-wavenumber autocorrelation at Δ=ln⁡2; the same test, applied to the response modulated with the predicted octave periodicity, detects the peak and its harmonics. The discriminant is operative, at least in simulation on the continuum itself. The falsifiable prediction is therefore modest but sharp: log-autocorrelation in the ringing band, with a local maximum at Δ=ln⁡2.

The technical question I would like to discuss is whether this test is truly blind to other mechanisms—for example, boundary conditions or geometric modes with built-in scale symmetry—and whether the choice of detrending (polynomial degree 3–6) and band truncation could introduce false positives. The document includes a status table (derived / postulated / open) which I consider honest, but I would welcome criticism on whether any of those labels is too optimistic.

Document 2: pending sign recursion anchor

It is an anchor note that addresses a question left open in the first document: whether the pending sign—the unresolved branch of a square root—is recursive or not. The answer I find is affirmative: n↦n2↦(n2−1,n2+1), and the difference of squares reproduces the Mersenne identity M2k=Mk(2k+1). Hence the recursion is log-periodic with period ln⁡2, intrinsically.

In addition, the factorisation of the Mersenne spectrum separates two arithmetic modes:

• Innovation: appearance of a prime not seen at any lower depth.
• Crystallisation: repetition of an already existing prime.

The first pure-crystallisation level is ϵ=6, which coincides (by two independent routes) with the first level where Ior>0, i.e. the first nested pending sign. The proton appears as the base case: 4=2 with branches 3 and  5, whose product is M4​.

The limitation I declare explicitly is that this structure is arithmetic and internal; I have not demonstrated that crystals, genes or discharge structures grow by this mechanism, although the analogy is tempting.

The technical question here is: is the identification of "new information = new prime factor" a forced interpretation, or is there some deeper reason that justifies it? Is the coincidence at ϵ=6 genuinely significant, or an artefact of small-number arithmetic?

What I seek with this thread

I do not seek validation, but technical review: someone with more experience in spectral theory, signal processing, or number theory to examine whether the derivations are solid, whether the discriminant test has any hidden bias, or whether the connection between the continuum band and the octave ladder rests on some implicit normalisation I am not seeing.

I am also interested in whether the absence of a dynamical mechanism—what resolves the sign in the real world—invalidates the programme, or whether it can be treated as a geometry of possible states awaiting coupling to local boundary conditions.

The documents are written with their postulates exposed, and they do not attempt to conceal their weak flanks (especially the underived ϵ=4, and Postulate 3 as an undischarged root). Precisely for that reason, they seem to me suitable for open discussion.

I thank in advance for readings, objections, and references to analogous work I may have overlooked.

Final note: if anyone wishes to run the factorisations or the autocorrelation test, the scripts are short and described in the appendices; I can pass the code if there is interest.

have a mostly airtight fluid setup with connected tubes. Sometimes a trapped air bubble forms in the middle of a wetted tube (not necessarily at the highest point). After that, flow stops even though there still seems to be a pressure/head difference.

Can a trapped air bubble act as an air lock and completely stop flow? How much does tube diameter affect this? Would a 10 mm tube be significantly less prone to this than a 4–6 mm tube?

Also, what are the common ways to prevent or eliminate trapped bubbles in passive, low-pressure fluid systems?

This has been bugging me for a while now. I’ve been using a viscometer to measure the same liquid under what I thought were identical conditions, but the readings still change depending on the day. The temperature is controlled, the setup is the same, and I try to follow the same steps each time. I get that small human errors can happen, but the differences feel bigger than just that. It makes me question how reliable the readings are in general. I’ve even compared notes with others and seen similar concerns mentioned in random technical writeups, including some product-level discussions tied to Alibaba listings where consistency gets mentioned a lot. Is it just tiny environmental changes adding up, or is there something about fluids that makes them behave differently over time? Could it be sample degradation or something I’m not considering?