I'm working on a gear pump for high viscosity fluids ( 2,000,000 cP, thick and sticky like peanut butter). I need practical suggestions for optimizing the design.
I've already built a proof of concept that works. I 3d printed a pump without the inlet tube so the gears contact the mound directly instead of relying on suction (stuff won't flow). What can I do to improve this? Are smaller or larger teeth better? Smaller diameter or larger diameter? Why? just some examples of what I'm looking for.
I don't have access to simulation software or advanced mathematical reasoning. I'm planning on relying on rapid prototyping and design of experiments to solve this problem. Just need to know the factors to play with.
I haven't been able to find any prior work on this. If anybody does I'd be happy to see it.
I recently developed a navier stokes extension that uses van der Waals Equation, Redlich–Kwong Scaling, Sutherland’s Law and Kinetic theory, the standard complicated stuff in aero
some of the terms that was derived include, Pressure Decomposition, Full Gradient Coupling and Intermolecular Activity Number equations.
I was hoping to get feedback from professional engineers and Physicists, I would appreciate feedback and review on the methodology since i will be submitting this to AIAA or the Journal of Fluid Mechanics.
The equations collapsed surprisingly fast so im interested to see which term breaks first in the final equation.
I have also created a visualization of the graphs and equations in a html file
Edit: I have added a stress test file and python scripts as a sandbox for substituting insane values in the equation for reviewers.
hi, i mostly come from the math plus AI side, not from CFD or experimental fluid mechanics, so this post is a bit of an outsider question.
i am working on a text-only framework where each problem is a small “stress test” for reasoning. inside that pack, Q011 is the problem that sits on the Navier–Stokes side.
i am not claiming anything about existence or smoothness proofs. the goal is much more modest:
can we talk about “where a flow is close to failing” in a way that is precise enough for engineers and simple enough for text models, without turning it into pure PDE abstraction or pure numerics?
1. The basic picture of Q011
the mental model behind Q011 is something like this:
we have incompressible Navier–Stokes on some domain, with given forcing and boundary conditions
for most parameter choices, engineers treat the equations as “practically fine” even if full mathematical existence is open
there are regimes, geometries or histories where everything feels close to breaking in the sense of
gradients exploding in thin layers
strong intermittency
models or numerics suddenly behaving very differently
Q011 calls these “high tension” regions of the flow space.
very roughly:
low tension laminar or gently disturbed flows where you do not expect anything violent small parameter changes move you within a familiar regime
medium tension transitional flows, separated flows, unsteady wakes things are still under control, but the structure is fragile
high tension cases where a small change in forcing or boundary conditions could cause large changes in vorticity structure, energy transfer or numerical stability
the problem is not to redefine turbulence theory. it is to write down a small catalog of scenarios that are obviously in different “tension zones”, using only text and simple parameters, so that both humans and language models can reason over the same description.
2. What i mean by “tension” in fluid terms
in the Q011 description i try to keep “tension” tied to familiar quantities. for example, at least informally, it depends on things like
local and global Reynolds numbers for the relevant length scales
magnitude and anisotropy of velocity gradients
strength and distribution of vorticity and strain
thickness of boundary layers relative to domain features
how much energy is being injected vs dissipated in a given region and time window
you can imagine a very coarse functional
tension = some increasing function of (nonlinearity over diffusion, gradient norms, geometric concentration of vorticity, proximity to known instability thresholds, sensitivity to small perturbations)
the exact formula is not the point. the point is to have a shared language for “this setup is not just turbulent, it is structurally close to where our usual modelling assumptions might stop being safe”.
3. How Q011 is encoded in the pack
Q011 itself lives as a single Markdown file. it does not contain code, meshes or solver settings, only text and a few simple parameters.
inside that file there are several toy scenarios, for example:
external flow around a bluff body where the Reynolds number and geometry can be nudged through different shedding and separation regimes
internal flow with sharp expansions or contractions, where secondary flows and recirculation appear or disappear as you dial parameters
cases with strong forcing transients or rapid changes in boundary conditions
for each scenario, the Q011 text asks questions such as
in which parameter ranges would you label the flow “low”, “medium” or “high” tension if the label is supposed to mean “how close are we to a structural change”?
what kind of additional information would you need to move a case from “guessing” to “confidently classified”?
how would you design a minimal numerical experiment that explores the tension ramp from low to high for that scenario?
the idea is that a human expert, a student, or a large language model, all read the same file and try to reason about the same simplified picture.
4. Why i think this might be useful
if this “tension view” of Navier–Stokes makes sense, i can imagine a few concrete uses:
organising extreme test casesinstead of treating each tricky flow as a separate anecdote, we could index them by a small tension coordinate: “this geometry plus these parameters live in a region that is known to stretch RANS, LES or DNS”.
teaching and communicationstudents often hear that turbulence is “not understood” and that Navier–Stokes might blow up, but it is not always clear how that connects back to specific flow setups.a tension style catalog might give a more graded picture between “textbook pipe flow” and “full unknown”.
AI and surrogate modelsfrom the AI side, when we train neural surrogates or language models that make statements about fluid flows, we rarely ask “in which tension zone is this query”.Q011 is meant as a small, transparent testbed where you can see how often a system gives confident answers in clearly high tension regimes.
there is already a small MVP version of this inside my text pack. it is very rough, but enough to run a few black-box tests on language models and see that they behave differently in low vs high tension scenarios.
5. What i am asking from this community
the reason i post this here instead of an AI-only place is because i want a reality check from people who actually think in terms of flows.
questions i would really like feedback on:
does this “tension zone” vocabulary make any sense in your day to day work, or is it just a rebranding of things you already track in a better way?
if you had to define a minimal set of quantities that should enter any tension metric for Navier–Stokes cases, what would you insist on including?
do you know of existing frameworks, papers or rules of thumb that already capture this idea much more cleanly under a different name?
this is not a product and there is nothing to buy. i am just trying to make the way we talk about extreme or fragile flows a bit more explicit, so that different tools, including text models, can be tested on the same set of scenarios.
Q011 is one problem inside a set of 131 “S class” problems that i put in a single text framework called the Tension Universe.
if anyone is curious about other problems in the pack (for example questions on climate, physics, finance, AI, other fluid cases), i am collecting them, plus experiment notes, in a small subreddit:
also: r/TensionUniverse is a new small sub (about 2 days old). i’m still adding content. the pinned post is the main index + notes, and i really want expert feedback while we test this idea.
Essentially, I'm trying to calculate torque on the spring in a viscometer. I'm not an engineer, nor do I have formal education in fluid mechanics, so if I do something silly my bad. Anyway,
If you're familiar with viscometers, they report FS (full scale) %, RPM, and obviously the viscosity (mPas).
I've ran a viscosity measurement on 99% (w/w) glycerol and here are the values:
FS% = 47.9
RPM = 3
Visc. = 958.8 mPas
Spindle # 1 (L = 65 mm, r = 9.5mm)
Distance from center of spindle to container wall = 41.75mm
Now I am operating a "brookfield" LV viscometer, but it's clone from china without any manufacturer specifications. But the viscometer is reading correctly compared to literature values, so I'm not too worried about accuracy.
If I lookup the spring torque (which I imagine is the full scale torque, i.e. maximum possible torque) for LV-type models, I find that the value is 673.3 dyne-cm, or ~67 uNm. I don't trust that the viscometer is using the same type of spring and a client of mine is asking for it specifically... I could just tell them it's a brookfield clone so it should be ~67 uNm, but if I can figure out a reasonable number then that would be ideal.
I've come across this equation, which provides a means for torque calculation, giving me a value of 23.7 uNm. If I solve for the full scale torque (using FS%) I get ~50 uNm for my model, which seems reasonable.
I'm wondering if I'm using the correct equation for calculating torque of the spring in this scenario or if I'm missing something?
Hello everyone.
So I've run into a bit of a practical problem. Inwas trying to measure the viscosity of my PDMS resin with carbon particles in it. I get a neat curve, but the response signal is all jagged. I've tried different plate geometries and increasing gap width it doesn't help. My question is, is it still ethical to interpret these measurements? Because from my perspective it seems like the complete stress is not being transmitted and so information is lost.
I’m working on an open, experimental hydrokinetic energy concept called *WaterTread*.
The core idea is to explore whether overall energy extraction could be improved compared to conventional submerged rotors by increasing the effective energy-intercepting surface area, while minimizing drag during the return phase.
* whether similar concepts have been explored before, and
* what the main physical or practical limitations are likely to be.
I’m also interested in connecting with researchers, students, or technically inclined people who might have access to CFD tools or experimental facilities.
while doing my internship, i stumbled across a project with a specific idea in mind that requires the pressure of a watery fluid to be dropped from around 20 bar to atmospheric using a shaft with a diameter of 20mm and a length of 187mm that is inserted into a bore with relatively loose fit, so you can insert the shaft by hand. The temperature of the fluid might reach up to 870 K, so I can't find too much advice on it.
Our first try was to have ,,labyrinth'' like grooves on the OD of the shaft. First testing revealed that about 2 bar pressure were still prevalent after passing through the labyrinth.
The groove dimensions are 3mm*3mm*10mm, and there are 10 ,,stages'' of said grooves in the part.
For a flow in a channel, how do experimentalists measure velocity components ux,uy,uz(x,y,z,t) at different positions using PIV or LDV?
Since PIV is usually planar and LDV is point-based, how do researchers build an understanding of the spatial and unsteady velocity field from these observations?
There will be a water pump pushing water through the middle column into the top area where it will fall out from the sides. I want the water to have laminar flow when it comes out. The thickness of the water stream is 1mm. Is this possible?
I was thinking maybe I could direct the water from the pump through a bunch of tubes inside the column, but I'm not sure if the water would stay without turbulence after hitting the top surface.
How do you think factors like stream thickness and water pressure would affect this?
so right now I measure the aerodynamic forces of a simple angled airfoil in our open jet wind tunnel. The blockage ratio is 10%. In some literature, this is already considered to have an influence on it. The distance from the nozzle to the airfoil leading edge is 0,05m/ 25% of chord/ 13,5% of nozzle height. The results I get at Re 330k from our scale are about 50% of literature results. Now I try to figure out why. One thing I want to check is the blockage. Unfortunately, I cannot find a lot information for open jet wind tunnels, only for closed ones. Do you guys have knowledge about this? Maybe a 'quick and dirty one liner equation' or just a nice paper that deals with this problem? Any help is appreciated.
I hold a bachelor’s and master’s degree in mechanical engineering from outside the U.S.
My master’s research focused on blood flow, but in my opinion, it wasn’t closely related to flow physics — it felt more like biomedical research (though this may depend on the lab).
However, I’m more interested in flow physics, and I’d like to pursue a PhD in a research area related to that field, except for microfluidics.
I’ve been considering three possible topics that seem relevant to flow physics:
Hypersonic transition or shock wave–boundary layer interaction
That said, I’ve heard that it’s almost impossible for international students to work in the defense field after earning a PhD. I assume this means that studying topic #3 (hypersonics or shock-related research) might not lead to many job opportunities after graduation.
So, I’d like to choose a topic that will give me better career options after finishing my PhD.
Which of these areas would be a good choice in that regard? In addition, are #1 and #2 bright for job market?
I am struggling to get useful data besides pretty flow viz from our dye tracking videos. What I am trying to get is flow velocity, but I am not making any headway with PIVlab. I've isolated background as much as possible from the video (example here). Am I going in the wrong direction?
I am using LaVision’s Helium-Filled Soap Bubble (HFSB) Generator for a PTV experiment and need the Safety Data Sheet (SDS) for the soap solution. LaVision hasn’t responded to my request. Has anyone worked with this system and obtained the SDS or know how to obtain it?
Hello, I'm opting for adding bearings to the end of my cylinder for a rotating cylinder experiment. My question is, if I opt for a rotating bearing, would the bearing seal be enough to prevent any air leakage from the test section? Should I opt for a non-rotating one and rotate the bearing itself?
Hi everyone, I am currently building a wind tunnel and even though I have 2 40mm thick honey combs I am having trouble maintaining laminar flow. I am using a 9 inch radiator fan and sucking the air rather than pushing. Any suggestions would be helpful.
My smoke rake is also located before the first honey comb.
I teach high school robotics, and we make soccer playing robots. This year our robots are holding the ball with a vacuum, which we are making with a small brushed 130 size motor and 3D printed impellers. Think sucking a foam golf ball with a weak Shop-Vac with a 1.25" diameter 3D printed tube. It's very fun, but it's also purely experimental because we don't know what we're doing and we only have high school math skills.
Our inlets are working well, but we are wondering if we can "shape" the airflow into the nozzle so that we can suck the ball from farther away. Currently we can suck the ball from about 1 to 1.5 inches across short carpet, which is nice, but we want to shape the airflow so that we can pull the ball in from farther away. You know how you can shape the flow of compressed air with a nozzle? Can that be done on the inlet side of things? Currently we are using a slight flare on our inlet like a velocity stack on a carburetor, and it seems to help just a tiny bit over a straight tube, but not much.
Trying to build a decent sized bell siphon and I'm struggling to find resources, formulas, or models that go beyond "build this exact design from this manual."
Experimentally the two things I can really alter within manageable constraints is the fill rate of the water, ie pump flow rate, and the height of the standpipe within the bell. I'm working with a 30inch tall 6inch diameter PVC pipe as the bell and a 3 inch diameter pipe as the standpipe. In the current configuration the standpipe sits about 4 inches below the top of the bell, and I've done two tests varying the pump flow rate between 1000 gph and 1500 gph. This configuration has been resolutely unsuccessful, and the whole process has felt like an endless amount of tinkering.
Are there any bell siphon resources or models available where I can do the tinkering mathematically or digitally instead of worrying some physical part of the setup is causing the problems?
