Turbulence - Science topic

I am exploring leadership that navigates VUCA turbulence at the intersection of leadership development and organizational performance.

Hi everyone,

I am working on a simulation involving restricted canal with ship using DFBI. I am facing reversed flow in my outlet boundaries as the DFBI is released (In 1.25s). Is there any method to avoid this type of wave reflection in outlet?

software using STARCCM+

VOF, implicit unsteady, K - epsilon turbulence

Thanks in advance

Hello all,

I've got a 2D simulation case in which the flow separates from the sharp leading edge of rectangular bluff body and reattaches to the wall some distance downstream. The main goal is a accurate prediction of pressure distribution along the body's face parallel to the flow.

I'm doing a transient simulation using SST model in conjunction with gamma-Re transition model. The time- cord-averaged y+ is less than 2~3 and the inflation layer around the face of interest contains 10 prism layers. The Re number based on the body's width (perpendicular to the flow) is 1.7e+4.

The problem is that my model overpredicts the reattachment length, which in turn leads to delayed pressure recovery.

I have a suspicion that longitudinal decay of turbulence values specified at the inlet might be to blame. Consulting the Ansys CFX-solver Modeling Guide, I learnt that one solution is to prescribe appropriate turbulence values at the inlet based on the desired values at the body. An alternative approach also suggests some additional source terms for k and w transport equations in order to preserve the inlet values up to some distance upstream the body, from where decay is allowed.

Here are my questions:

1- Is my suspicion valid in the case of my problem?

2- Is the decay of turbulence of physical basis or a numerical artifact?

3- which of the two methods works better? Are there any attempts in the literature?

I appreciate your comments.

Hi everyone,

I try to do a simulation about heat transfer of supercritical hydrogen in a cooling channel. Properties of H2 is obtained from NIST and displayed by an UDF function. I used the k-w model. If i set inlet temperature at 300K, everything is fine and correct. But when i set it to 34.6K, i ran into the problem " turbulent viscosity limited to viscosity ratio of 1.000000e+05 in xxx cells". My mesh is very fine as displayed below. CAn anyone help me to solve the problem

The simulation is for turbulent and incompressible flow and the inlet velocity condition is selected for the inlet

Airflow

Turbulence

Air Dynamics

Signal effect

frequency response

I am running a coarse DNS case for pipe flow with 2.1 Million cells. My residuals are quite fluctuating as its a fully turbulent annular pipe flow case but its getting statistically converged to a mean value.

My doubt is, the residual values are quite high where its mean is getting converged for instant close to 0.1 or 0.01(refer attached .png), despite of giving tolerance of 1e-06. Due to this I think I have results of velocity profiles and shear stresses quite under predicted.

what can be the possible ways to reduce these residual values?? and what is the reason of having such high residuals??

NOTE: I am already using higher order schemes for solving Fluid flow equations in OpenFOAM

"How do we understand special relativity?"

The Quantum FFF Model differences: What are the main differences of Q-FFFTheory with the standard model? 1, A Fermion repelling- and producing electric dark matter black hole. 2, An electric dark matter black hole splitting Big Bang with a 12x distant symmetric instant entangled raspberry multiverse result, each with copy Lyman Alpha forests. 3, Fermions are real propeller shaped rigid convertible strings with dual spin and also instant multiverse entanglement ( Charge Parity symmetric) . 4, The vacuum is a dense tetrahedral shaped lattice with dual oscillating massless Higgs particles ( dark energy). 5, All particles have consciousness by their instant entanglement relation between 12 copy universes, however, humans have about 500 m.sec retardation to veto an act. ( Benjamin Libet) It was Abdus Salam who proposed that quarks and leptons should have a sub-quantum level structure, and that they are compound hardrock particles with a specific non-zero sized form. Jean Paul Vigier postulated that quarks and leptons are "pushed around" by an energetic sea of vacuum particles. 6 David Bohm suggested in contrast with The "Copenhagen interpretation", that reality is not created by the eye of the human observer, and second: elementary particles should be "guided by a pilot wave". John Bell argued that the motion of mass related to the surrounding vacuum reference frame, should originate real "Lorentz-transformations", and also real relativistic measurable contraction. Richard Feynman postulated the idea of an all pervading energetic quantum vacuum. He rejected it, because it should originate resistance for every mass in motion, relative to the reference frame of the quantum vacuum. However, I postulate the strange and counter intuitive possibility, that this resistance for mass in motion, can be compensated, if we combine the ideas of Vigier, Bell, Bohm and Salam, and a new dual universal Bohmian "pilot wave", which is interpreted as the EPR correlation (or Big Bang entanglement) between individual elementary anti-mirror particles, living in dual universes.

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Wolfgang Konle added a reply

5 days ago

Fred-Rick Schermer "It does not explain how all got started up, but then again I also have Energy as a given. I recognize your model as complete on its own, leaving some aspects unexplained."

Good question, an answer and some additional explanations can be given in short words.

There was no startup. The model is eternal.

The background of dark energy distorts space into an S³ structure with a space curvature of 1/R² and a volume of 2π²R³. The volume of space oscillates. It shrinks as the dark energy is charged and expands during a recycling event.

The transfer of the upload energy takes place via a gravitational interaction. With its gravitational field, each particle, including photons, creates a tiny dent in the dark energy density. If this dent moves, the dark energy has to bypass the dent. The bypass motion requires some energy, which must be provided by the moving gravitating object.

A recycling event lasts a few million years. The energy charging phase lasts about twenty to thirty billion years. We are currently in this phase.

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Fred-Rick Schermer added a reply

5 days ago

Wolfgang Konle

Thank you, Wolfgang, I understand better now what you are working with.

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Cosmin Visan added a reply

2 days ago

Fred-Rick Schermer Universe doesn't exist. "Universe" is just an idea in consciousness.

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Cosmin Visan added a reply

2 days ago

Wolfgang Konle Energy doesn't exist. "Energy" is just an idea in consciousness.

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Sergey Shevchenko added a reply

1 day ago

It looks as that rather strange series of posts in the thread is too long already, and to point here that the thread question rather in detail is scientifically answered in SS 5 posts series on page 1, and on page 2..

Cheers

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Fred-Rick Schermer added a reply

1 day ago

Wolfgang Konle

Wolfgang, will you please read this article in which I propose the inverse for explaining a Black Hole.

All data is the same, but the perspective is distinct.

It's like Rubin's Vase, where one can see a Vase, but another can see the Two Faces. All data is the same, but the view is distinct nevertheless.

Same for the Black Hole. I can see the Black Eye instead, with all data exactly the same, yet the perspective is what makes the view different.

There is truly no invisible mass required to explain everything we observe.

Preprint On The Scientific Black Eye

This may be my most important work. It puts me in opposition to the majority (nearly everyone) of the scientific community.

Thank you for your review.

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Cosmin Visan added a reply

24 hours ago

Fred-Rick Schermer Energy doesn't exist. "Energy" is just an idea in consciousness. See my paper "How Self-Reference Builds the World".

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Wolfgang Konle added a reply

12 hours ago

Fred-Rick Schermer

I have scanned your article about a "black eye".

But I could not identify the differences between your black eye model and the black hole model described in standard physics.

I have looked for differences in energy, mass, momentum, momentum of inertia, and external impact on the galaxy. But I could not find any substantial information about that kind of differences.

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Cosmin Visan added a reply

9 hours ago

Wolfgang Konle Energy doesn't exist. "Energy" is just an idea in consciousness. See my paper "How Self-Reference Builds the World".

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Fred-Rick Schermer added a reply

5 hours ago

Wolfgang Konle

Excellent, Wolfgang. You make me happy with that response, though I need to mention just a little more.

The Black Hole model contains an event horizon, whereas the Black Eye does not contain an event horizon.

There are two important positions which I describe as follows:

1. A Black Eye is a phenomenon like the Eye of the Storm is a phenomenon. The Eye of the Storm is really there, but it is not based on itself. The Eye is based on the wind force of the Storm. Inside the Eye, there is no wind force. Hence, it is a phenomenon, a byproduct of larger circumstances. It should be considered a major observation that physical realities can produce phenomena that then 'exist' in their larger context.

2. When a person closes an eye, then one can see what a Cyclops sees. Yet the eye that is open did not move toward the center of the face, so the reality of a Cyclops will not be achieved. That means that when an ordinary physical property among others is declared to be zero, then the remaining physical properties do not realign themselves around a center. There is no realigning. The physical reality remains intact, and the zero reality of a physical property cannot be used to declare how the standard reality is then something that it cannot be (i.e. singularities are outcomes on paper only; no scientific grounds were produced to declare singularities scientifically correct).

I do not undermine the Black Hole model other than proposing a better model in which there is no event horizon to consider. All is scientifically present in the Black Eye model. There is nothing to believe in the Black Eye model, while there is something to believe in the Black Hole model, and believing is of course a non-scientific activity.

Thank you, Wolfgang, for your reply.

Will you respond further based on what I wrote here above?

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A Black Hole is accepted in science by most physicists. Not many people suspect that an alternate model is available based on the same data, called a Black Eye.

The Black Eye model does not contain a mass in the center. The model is not based on a single mass. Rather, the outcome is explained based on the entire system, and for this article that system is a galaxy.

The Black Eye model takes a system-wide approach and bases the resulting outcome on all masses in a galaxy. The gravitational forces of all masses combined establish a collective gravitational depression in the center. In this model, we are witnessing a collective result.

Meanwhile, an additional component is involved as well, not considered by most physicists to play an important role. Next to the ordinarily considered motions of matter, a galaxy as a whole is also on the move through space in a single direction. This helps to establish a special outcome, right in the center.

Note that there is no difference in data between a Black Hole and a Black Eye. It is all in the scientific interpretation that the distinction between both models comes about. Like Rubin’s Vase, one can see a Vase, or one can see Two Faces. Either way, the data is identical.

--

Most will be familiar with the Black Hole model, so the emphasis for this article will be on presenting the Black Eye model.

The starting point is not the gravitational monster itself, but rather the circumstances of a galaxy as a whole.

The Milky Way contains about 100 billion stars and all of these stars have their own gravitational force, attracting all other masses. Yet as a collective, the center of this gravitational force establishes a deep depression.

· The pull on the center is enormous, coming from all directions in the galactic disk. The explanation that not all masses move toward the center with that pulling force is due to the circular motion of this collective, countering the action. Most masses are pushed out by the circular motion and pulled in by the gravitational force.

A depression is made up of various components. The most important aspect for understanding the Black Eye model is that the center of the depression is void of materials, except for happenstance materials (more on this later).

The center exists in a gravitational balance, called net-zero, yet the depression is experienced at its gravitational maximum. That net-zero reality takes up space; it is not a singular point, but rather an area, an Eye of net-zero gravitational force.

Perhaps a surprise, but at the exact spot of first moving away from the net-zero location, all hell breaks loose.

· This sudden boundary shift is like the shift seen with the inner core of planet Earth, with the solid part of the inner core located in the center flanked right around it by the fluid part of the inner core. The center is solid, not moving internally, while the fluid part is moving wildly. There is no transition zone right at the shift of both parts of the inner core.

The same shift occurs between non-motion in center /wild motion right next to it in the gravitational depression. In the center, there is net-zero gravity, not experiencing gravity. Right on the edge of it the gravitational force is exerted to its max. There is no greater gravitational expression in the entire galaxy than right here next to the net-zero location.

What happens right next to this edgy spot is that friction has become available whereas no friction is available anywhere inside the net-zero center. As soon as that friction is available, there is motion, lots and lots of it. All tension of all masses in the entire galaxy is kept at net-zero in the center and breaks loose with a fury at first opportunity.

For the Black Eye model, one can declare that edgy spot a gravitational Wall of Motion. The photons seen in images of a Black Hole/Black Eye show us the Wall of Motion. As is well-known, we only see photons when they move in our direction. This is a location of great turbulence.

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In the Black Eye model, photons will in general not make it through the center because the gravitational Wall of Motion will swat them out of the way before a photon can reach the center. As such, the Eye will be black. Meanwhile, the Wall of Motion in which the photons are swatted into our direction ensures that the ring of light (the donut) is visible to us. Thanks to the way photons works, we can see the ring of light, the Wall of Motion.

· Further out, photons continue on their straight path all around the Black Eye and Wall of Motion. As long as they are not swatted by the Wall of Motion, and when photons are not aimed toward us, we do not get to see them.

Once more, the most important aspect is the center at net-zero. This net-zero location is the solid backbone of all gravitational masses moving around it. In a way, all masses are moving around the gravitational center. Each mass is attracted both by the center and by all other specific masses in the galaxy. As such, specific individual behavior by a mass can also get established in this setting.

A partial collective of masses in the galaxy, when placed in opposite location to the center, can move any single mass individually as well. That means that for a single mass, the majority of the galaxy can end up establishing the direction that pulls this mass toward the center. As a result, a single mass may end up with a specific behavior in light of the net-zero center.

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In a visualization with a clock, this is like a single mass located at 2 o’clock being attracted via gravity through the large center location of the clock by the other side. Yet it is not just the masses located at 8 o’clock that are doing the attracting. It is more like all masses between 5 and 11 o’clock are attracting the single mass at 2 o’clock. Masses found at 7 o’clock or 10 o’clock would also be pulling the single mass at 2 o’clock in their direction.

Similarly, the mass at 2 o’clock is contributing (its very small part) to the attraction on all these masses between 5 and 11 o’clock. It will do so as part of its majority of masses of a galaxy. A mass at 8 o’clock will be pulled by all masses situated between 11 o’clock and 5 o’clock, including the single mass at 2 o’clock.

This is therefore a story of the single mass being attracted by the many in opposite direction, while the single mass contributes itself, as part of the many in opposition, to each and every other mass in their specific opposite location.

Once more, it is the circular motion that keeps all masses where they are. The inward pull by all masses is countered by the outward motion of the circular motion of all masses.

Through happenstance, a single mass may no longer follow the established path and become attracted to the very center of all masses in the galaxy. Yet when that mass reaches the center, it will still move around it. The single mass reverts direction in a smooth but perhaps rather fast transition.

In a static view, the exact center has an attraction that is equidistant in all directions of the galaxy. As such, a single mass will bypass the center in a circular motion, reversing direction, exactly because there is no single mass of attraction. This is a collective outcome played out on an individual mass.

Interestingly, the net-zero location can be entered also by a mass, yet this cannot happen at great velocity. At great velocity, the mass will always move around the net-zero center.

Yet when a slowly and gradually moving mass enters the net-zero location, it can get stuck on the ‘wrong’ side of the Wall of Motion.

· Like an airplane flying straight into the Eye of the Storm perhaps not encountering much trouble, when flying out back into the Storm the plane better not enter it at the wrong angle where the force can overwhelm it. Indeed, it is dangerous work for these pilots.

Naturally, a mass that ‘fell’ into a Black Eye will not have a steering wheel available and will not be able to exit the Black Eye exactly as desired. In short, it will not exit in a single piece.

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That mass will get churned into pieces. Some of it will fly right back into the galactic disk, unnoticed, yet a good amount can move outwardly through the perpendicular spouts. These spouts are located on both sides of the net-zero location in the galactic disk, themselves also net-zero areas. What propels these churned pieces is nothing but the speed the pieces achieved from the churning motion in their direction. Once more, the Wall of Motion will not let any mass escape whole once it entered the net-zero location.

To understand how a galactic system can establish a Black Eye, it is not sufficient to understand the gravitational motions only. A galaxy, or any matter in the universe, is always on the move. There truly exists no matter at a standstill.

The entire galaxy of, for instance, the 100 billion stars of the Milky Way, are on the move collectively, moving through space in what is basically a straight line.

This is the fastest speed that all these Milky Way masses are moving in. The outer regions of the galaxy are moving at the same speed as all other masses in the galaxy in that single direction.

· Like ice-skaters on a frozen canal, each skating under his- or her own force, all are moving like a group and yet there is no group force. There appears to be a group, but a single skater can stop skating on his or her own accord, with the remainder of the group continuing. There is no group powering the skaters.

The initial ‘push’ established by the Big Bang materialization process got applied to all Milky Way energy, moving it in one and the same direction, at one and the same speed. Therefore, it appears that there is a group that is powered by group action. Yet the group action that we see, the circling of these masses, is based on gravity. For the skaters, we see the evidence of their being a group when they are jostling or helping propel each other. Yet in general, each skater skates on their own power.

· The original push of the Big Bang is not based on gravity.

The true motions of all masses in the Milky Way are more complex than considered in our Einsteinian view of matter in which gravity is the essential force.

On the one hand, there is the fastest motion of all masses moving in the same direction at the same speed at the same time. On the other hand, there is the gravitational motion indeed that attracts these masses to one another.

· A circular motion is the result of both realities combined.

In the center, there will be a net-zero location, and this will not be based just on gravity, but based on the established circular motion, which includes the single and fastest direction that all galactic matter is moving into.

--

Why did physicists consider there is a Black Hole instead of a Black Eye?

The answer lies in our achieving answers not just based on what we observe in reality, but also in our doing calculations on paper. When all the data is transposed onto paper, then one facilitates an environment in which is it easy to make a simple but fatal mistake.

Consider a piece of paper with a face drawn on it: two ears, hair, a nose, an eye, a mouth and chin.

All parts are scientifically correct.

And yet when there is just a single eye in the face, then the drawing shows us a Cyclops. It does not matter that the eye itself is scientifically correct. The drawing shows an outcome that does not corroborate what we witness in nature.

When all data about a galaxy is expressed in correct calculations on paper, then a melding of data into a single mass can still make the outcome become incorrect. The worst part of the Black Hole calculations is accepting that for the Cyclops the single eye sits in the middle of a face.

Naturally, it is easy to see what a Cyclops sees. All one needs to do is close an eye and we see exactly what a Cyclops sees.

· Yet the mistake is to think that the remaining open eye moved to the center of our faces.

The model demands therefore that all that is real remains in place, even when there is an established outcome of zero for whichever aspect that one has considered essential in a physical environment. The zero presence of any aspect does not allow us to eliminate the zero location from our equations.

· We are not allowed to play with models at will.

It is easy to undermine the Black Hole model with the Black Eye model, just like it is easy to undermine the Vase with the Two Faces. Only one outcome will be correct, and yet the data shows us two possibilities.

How to pick the best possible outcome?

The scientific weak spot in the Black Hole model is that the entity that establishes the scientific Black Hole cannot be shown itself. The event horizon prevents any fully scientific acknowledgment to ever occur. The Black Hole model contains a curtain beyond which no scientific access can be obtained, except on paper. That makes it a weak scientific model because the scientific essence is not available.

The Black Eye model does not suffer this scientific problem. All data is out there in the open. Everything is explained.

The real distinction is in the interpretation of the data.

I am trying to run a turbulent pipe flow simulation with turbulent Reynolds number of 600, for 15 seconds, the flow is pressure driven due to gravity enforced buoyancy force which gives kinematic viscosity input as 8.25E-0.5 N/m^2, if gravity value taken as 9.81.

But in this case the flow is unable to become turbulent for smagorinsky LES model. Is this because of the high viscosity input? because for same inputs and changing Re = 2400 and \nu = 2.06E-05 the flow starts to become turbulent within the span of 15 seconds.

1. to make my simulation run changing gravity value and keeping the nu = 2.06e-05 to obtain same Re = 600 will be a solution's to this?? (I'm trying to check this with trial runs)

2. Why does this happen? any physical intuitions for this kinda behavior with \nu values ??

I found this article: that claims circular profiles are better for heat transfer than streamlined profiles as they induce more turbulences. Is this the case with other shapes as well? what is the best profile?

I am working on WMLES (wall Modelled LES) for which if I calculate my wall shear stress analytically and want to enforce it as a boundary condition at the wall patch, so that I do not need to resolve my near wall mesh rather give the wall shear stress as an input. One of the approach is defined by Schumann (1975) -(added an image below for the model formulation by Schumann) which I am trying to implement in OpenFOAM. My major question: Is there any method to define such a boundary condition of shear stress enforcement??

Because as far as the OpenFOAM user guide is concerned I could not find any such options. And the only way to define wall models is by changing the value of \nu_t.

Dear Readers,

I am writing to request assistance in obtaining numeric or number format data related to turbulent flow in ducts, specifically focusing on square, rectangular, and other geometries. I require data for cases of steady, fully developed flow in the cross section of the duct, with a particular interest in cross-sectional details.

The data I am seeking should be presented in a format that includes the following parameters:

- Horizontal coordinate (x2)

- Vertical coordinate (x3)

- Flow properties: main velocity (U), secondary velocities (V and W), turbulent kinetic energy (K), turbulent viscosity, turbulence dissipation rate (e), turbulent stresses (shear and normal), pressure distribution in the cross section, boundary shear stress, and flow parameters (longitudinal pressure gradients, duct geometry dimensions, friction factor, fluid density and viscosity, wall roughness conditions, etc.).

I have come across several articles that contain relevant information, but the data is presented in graphical form, making it challenging to extract the specific numeric values. Therefore, I kindly request your assistance in providing the data in numeric or number format, as described above.

Examples of experimental data sources include:

- Leutheusser, H.J. 1963. "Turbulent flow in rectangular ducts." J. Hydr. Div. ASCE 89 (3), 1–19.

- Brundrett, E., Baines, W. D. 1964. "The Production and Diffusion of Vorticity in Duct Flow." J. Fluid Mech., 19 (3), pp. 375-394.

- Gessner, F. B., Jones, J. B. 1965. "On Some Aspects of Fully-Developed Turbulent Flow in Rectangular Channels." J. Fluid Mech., 23 (4), pp. 689-713.

- Gessner, F. B. 1973. "The Origin of Secondary Flow in Turbulent Flow along a Corner." J. Fluid Mech., 58 (1), pp. 1-25.

- Melling, A., and Whitelaw, J.H. 1976. "Turbulent flow in a rectangular duct." J. Fluid Mech. 78, 289.

- Gessner and Emery. 1980. [Additional information needed]

- Leutheusser, H. J. 1984. "Velocity distribution and skin friction resistance in rectangular ducts." J. Wind Eng. Ind. Aero. 16, 315–327.

- Thangam, S., Speziale, C. G. 1987. "Non-Newtonian Secondary Flows in Ducts of Rectangular Cross-Section." Acta Mech., 68 (3-4), pp. 121-138.

- Rokni, M., et al. 1998. "Numerical and Experimental Investigation of Turbulent Flow in a Rectangular Duct." Int. J. Numer. Meth. Fluids, 28 (2), pp. 225-242.

Additionally, I am interested in numeric data, such as numerical predictions and Direct Numerical Simulation (DNS) data, from studies conducted by Naot and Rodi (1982) and Demuren and Rodi (1984):

- Naot, D.; Rodi, W. 1982. "Calculation of secondary currents in channel flow." ASCE J. Hydraul. Div. 108, 948–968.

- Demuren, A.O.; Rodi, W. 1984. "Calculation of turbulence driven secondary motion in noncircular ducts." J. Fluid Mech. 140, 189–222.

Furthermore, if any numeric data is available for other flow types, such as flow in cavities, flow at backward-facing steps, flow around cylinders, and flow around square rods, it would be greatly appreciated.

Thank you in advance for your assistance and contributions toward fulfilling this request. Your support will significantly contribute to the advancement of turbulent flow research.

Sincerely and best Regards,

I am searching for an anemometer to measure turbulence intensity of a wind tunnel. Most of the hot wire aneomemeters on market are designed for HVAC applications so their accuracy is around 5%. Since moderate turbulence intensity is defined to be between 1% and 5%, using a anemometer with 5% accuracy does not sounds like appropriate. What should be the accuray of the device for this measurement?

Additional Question: What should be the sampling rate of the device?

I have a transparent pipe which takes water from a tank.

The flow happens thru a pump. Pump takes water from tank and makes it flow thru the pipe.

The water stored in tank is open to atmosphere.

When I see the flow through the transparent pipe, i observe very huge bubbles. I have attached an image for your review.

I am interested to learn: from where is this air coming in the flow?

This is a code block from nutWallFunction library in OpenFOAM where in, effective kinematic viscosity ($\nut_w$) at the wall is calculated using resolved field(in case of LES)/ mean field(in case of RANS) and $y^+_p$ (wall normal distance of the first cell center). this allows to set a new viscosity value as boundary condition at the wall using log law. Considering the first cell center is in the logarithmic layer of the universal velocity profile.

Now, in this code block of member function defined as nutUWallFunctionFvPatchScalarField::calcYPlus()

There has been iterations done for the yPlus value to reach convergence with maximum of 10 iterations. Why are these iterations needed? and why is the maximum number of iterations 10. I have given a reference of the code below;

tmp<scalarField> nutUWallFunctionFvPatchScalarField::calcYPlus

(

const scalarField& magUp

) const

{

const label patchi = patch().index();

const turbulenceModel& turbModel = db().lookupObject<turbulenceModel>

(

IOobject::groupName

(

turbulenceModel::propertiesName,

internalField().group()

)

);

const scalarField& y = turbModel.y()[patchi];

const tmp<scalarField> tnuw = turbModel.nu(patchi);

const scalarField& nuw = tnuw();

tmp<scalarField> tyPlus(new scalarField(patch().size(), 0.0));

scalarField& yPlus = tyPlus.ref();

forAll(yPlus, facei)

{

scalar kappaRe = kappa_*magUp[facei]*y[facei]/nuw[facei];

scalar yp = yPlusLam_;

scalar ryPlusLam = 1.0/yp;

int iter = 0;

scalar yPlusLast = 0.0;

do

{

yPlusLast = yp;

yp = (kappaRe + yp)/(1.0 + log(E_*yp));

} while (mag(ryPlusLam*(yp - yPlusLast)) > 0.01 && ++iter < 10 );

yPlus[facei] = max(0.0, yp);

}

return tyPlus;

}

My doubt is concerning the do-while loop at the end for yPlus iteration.

Usually the distance between inlet boundary and body about 5 size of body. For what purpose? Why cannot located body very close to boundary?

I simulate the flow around bridge section, VIV and Karen vortex street. Try to understand the influence on inlet Turbulence Intensity. I choose 0.01% and 10%, ratio 10 and result was the same.

I have a fully developed pipe flow in with Inner radius (r) and outer radius (R), using pressure driven flow condition due to buoyancy,

- (1/rho) dP/dx = g

and velocity scaling u* = sqrt ( (-R/ (2 rho)) * (dP/dx)) [ friction velocity ], if Reynolds number is fixed ( Re = 600 ), along with r and R

we can get y+ value based on y values we give for cell size at both the walls,

But the question is when y+ calculated from this formula is y+ at the outer wall ( general pipe flow condition ) but how to get a y= for the inner annulus? ( concentric annular pipe flow ) ?

is there any analytical method to find this y+ or the only solution is to get us after simulation run when we have calculated friction velocities wall shear stresses at wall cell centers.

basically two different y+ to get analytically, in order to set up minimum cell size for my LES grid.

Art x Mental Health

Dear fellow researchers,

Art has always been a way of expression for me. It has calmed my inner turbulence and helped me to make better decisions. Let's talk about how picking up a paintbrush, dancing to your favorite song, or simply doodling can make a real difference in how we feel.

Consider the various ways in which art serves as a medium for emotional expression and processing. Reflect on the role of symbolism, metaphor, and abstraction in conveying deeply felt emotions. Share anecdotes, research findings, or theoretical perspectives that shed light on the therapeutic potential of art in this regard.

Your unique insights and experiences are invaluable contributions to this conversation.

what are different turbulent inlet conditions in T-Junction pipe flows, which leads to turbulence at T-Junctions during fluid flows

What is the accepted percentage of error? in case of I developed a mathematical model for the parameters of pump, when the flow is turbulent? What is the accepted percentage of error? in case of I developed a mathematical model for the parameters of pump, when the flow is turbulent? compared with the reported values of previous studies?

is wind spectrum consume mean wind and turbulence both ? and how the mean wind will be calculated from measured wind data?

In fluid mechanics and computational fluid dynamics (CFD), the fluctuating velocity components u' v' w'′ represent the turbulent fluctuations in the velocity field. These components are used to model the turbulent behavior of fluid flow. The prime notation (u' v' w') denotes the deviation of the velocity from its mean value (U,V,W).

For laminar flow, turbulence is generally not considered, and the flow is assumed to be smooth and ordered. In this case, the fluctuating velocity components (u' v' w') are essentially zero.

In CFD simulations using software like FLUENT, you typically specify the turbulence model and relevant parameters to simulate turbulent flows. Common turbulence models include the k-epsilon model, the k-omega model, and the Reynolds stress model. These models provide equations for the turbulent kinetic energy (k) and the turbulent dissipation rate (ϵ), from which the fluctuating velocity components (u' v' w') can be derived.

In FLUENT, you will need to set up your simulation by defining the geometry, boundary conditions, and fluid properties. Additionally, you'll need to specify the turbulence model and provide initial conditions for the turbulence variables. The software will then solve the governing equations, including the RANS equations and turbulence model equations, to obtain the mean flow field and turbulence quantities, including the fluctuating velocity components.

The specific steps may vary depending on the version of FLUENT and the turbulence model chosen, so it's recommended to refer to the FLUENT documentation or user guide for detailed instructions based on your simulation setup.

In engineering applications, understanding the Reynolds number is crucial for predicting the behavior of fluid flow. For low Reynolds numbers, laminar flow is desirable in scenarios such as internal pipes to minimize frictional losses. However, at higher Reynolds numbers, turbulent flow can enhance mixing and heat transfer, beneficial in applications like chemical reactors or cooling systems. Conversely, excessive turbulence can increase drag on vehicles or structures, impacting efficiency. Therefore, engineers carefully consider the Reynolds number to optimize the performance and efficiency of various systems involving fluid flow.

We consider cases of compressible and incompressible flows.

Hello everybody,

I could not simulate wind speed fluctuation by assigning turbulence intensity (TI).

I simulate the fluid-structure interaction by using STAR-CCM+ and Abaqus, and I simulate the flow field of through IDDES. I set field functions to assign inlet velocity and turbulence intensity, respectively. Some points are set to record the time history of wind speed in freestream direction. However, results show that the wind speed do not fluctuate obviously (i.e. the wind speed just slightly vary with time). I calculated the turbulence intensity (TI) according to the definition of TI. The TI was 0.0001%, indicating that the wind speed was almost constant.

I set “Synthetic Turbulence Specification” from “none” to “intensity + length scale” in “continuum” and “region-boundary -inlet-physical conditions” when I just use STAR-CCM+ and do not use Abaqus. In this case, the wind speed vary obviously with time. However, there was warning when I set “Synthetic Turbulence Specification” in fluid-structure interaction:

“The Synthetic Turbulence Specification applied to boundary ‘S.Con.inlet’ in region “flow”, is not compatible with the Motion Specification in that region.”

The Synthetic Turbulence Specification should be suppressed when the mesh morphing of flow field was considered.

The setting of regions and simulation are shown in attachments.

The computational region was 1000 m (x) *500 m (y) * 500 m (z)

The section of structure was 6 m (x) *6 m (y) * 50 m (z)

Is there anything I forget to check? How can I get a certain level of turbulence at a specific spot in the field through inlet boundary specification?

Any feedback on the approach/idea itself is welcome too.

I determined the area weighted average pressure drop between inlet and outlet and used ((2*dell_p*D)/(L*rho*velocity ^2)) to determine friction factor. But the results do not match the expected results for a turbulent pipe flow. Any suggestion is appreciated.

Can it be posited that the presence of a severe boundary layer separation, characterized by the detachment of the fluid flow from a solid surface, serves as a significant contributing factor to the augmentation of turbulent flow phenomena? In other words, is there a substantiated relationship between the adverse separation of the boundary layer and the amplification of turbulence in the flow field? Is it plausible to achieve a state of boundary layer separation characterized by an exceptionally smooth flow transition, resulting in nearly negligible levels of turbulence? In other words, can the phenomenon of boundary layer separation be effectively controlled and manipulated to minimize the formation and propagation of turbulent flow structures?

Reactor working schematic shown in the annex, driven by the stirring rod fan blade rotation of the metal hanging piece of the rotating flow impact, and the external conditions are satisfied with the Taylor number (Ta) to reach the critical value of the Taylor vortex. Then. Can flow in a high-temperature, high-pressure rotary reactor be analyzed using Taylor vortex theory?

Dear CFD Researchers,

Since AI tools are currently very popular, I am wondering if anyone use them to choose turbulence model for a CFD case.

So if you did, please share your experiences. Due to the answer, we can extend the boundaries of this discussion.

Thank you for your comments.

Kind regards,

Guven

Hello everyone, Im studying an airflow inside a finned tubular heat exchanger at various inlet velocities in Fluent. Starting at 10 ms inlet velocity it runs smoothly and converges but with increasing velocity it starts to diverge (at 12,5ms) and i just cant explain why. Ive tried many different things: k-epsilon and omega SST models, different inlet turbulence intensities and settings (initial pressure), restricted backflow reversal, lower relaxation factors, etc.. Im using NIST real gas model of air. My initial guess was that it has to do with the mesh quality and y+ value, but after doubling the number of cells i still get the same results. Ideal gas model works fine at even larger velocities. Analyzing the results, my turbulent Reynolds number just skyrockets at the end of the domain. I would appreciate any help .

I am running sloshing simulation in a rectangular tank using ANSYS fluent. reynold's number lie in turbulence and as it is a wall bounded problem I calculated the first cell height of inflation layer assuming y+ value=50.(turbulence range is 30 to 200). but, some literatures stated that having y+ value=1 (laminar) resulted in better accuracy. so, how should I assume my y+ value?

More specifically, I want the comparison in wavenumber-based power spectra, opposed to frequency-based. That would include converting multiple signals into a cross spectrum, finding the phase, and then the final conversion to wavenumber.

Extra points if it is in the field of plasma physics and magnetic signals.

Thanks in advance!

Turbulence structure is described by the formation of eddies. Size of eddies vary based on the turbulence length scale ( (Kolmogrove, Reynold and Hinze). Smaller eddies describes intense turbulence, larger eddies display low turbulence zone.

Recently, I came across a book by Bertin and Cummings, which says there are no physical laws to describe detailed turbulence structure pattern.

Are there any ?

[[ lfh note: This question has been hijacked by mistakenly celebrating a very non-responsive lecture on turbulence with Popular Answers. The lecture is even placed before discussion of the question. In my opinion it was without malice by R_^G or the person who posted it. Please read that "answer" on Page 4 below after reading actual discussion and clarification of the question, because the lecture is good history. ]]

[Original question comment] I think that the process of flow curvature formation at any scale in a fluid requires a pressure gradient across the curvature. The result outside the curve is increased internal thermal energy there. During subsequent decay of the curved flow, the curving kinetic energy fills the low pressure inside the curvature.

Every organization that strives to survive, to develop and to be sustainable, must be ready to face all the challenges that today’s turbulent and uncertain times carry with them. Organizations of all types and sizes are faced by external and internal factors and influences that make it uncertain whether they will achieve their objectives. The almost unimaginable pace of technical and technological progress, the dramatic acceleration of changes in all spheres of life, as well as the general feelings of uncertainty, actually can raise the question as to what extent is prevention still really possible at all?

Hi folks!

It is well known that the apparent stochasticity of turbulent processes stems from the extreme sensitivity of the DETERMINISTIC underlying differential equations to very small changes in the initial and boundary conditions human beings aren't able to measure.

Given our limitations, for the same measured conditions, a deterministic turbulent flow can thus display a wide array of different behaviours.

Can QUANTUM MECHANICAL random fluctuations also change the initial and boundary conditions in such a way that the turbulent flow would behave in a different manner?

In other words, if we assume that quantum mechanics is genuinely indetermistic, can it propagate that "true" randomness towards (some) turbulent processes and flows?

Or would decoherence hinder this from happening?

I wasn't able to find any peer-reviewed papers on this.

Many thanks for your answers!

This question delves into the fundamental nature of turbulence, a ubiquitous phenomenon in fluid dynamics that is characterized by chaotic and unpredictable fluid motion. Exploring the mechanisms behind turbulence and finding ways to better understand and predict its behavior is a challenging and active area of research with broad implications in various fields, including engineering, meteorology, and environmental sciences. This question opens up avenues for investigating turbulence models, turbulence control strategies, and the development of advanced computational techniques to simulate and analyze turbulent flows.

Hello everyone, I'm currently working on a project in which I need to simulate turbulent flow in a duct. For the inlet boundary condition, I'd like to use the Synthetic Eddy Method (SEM) to superimpose synthetic turbulence onto the mean flow. While I understand the basic theory behind SEM, I'm struggling a bit with the actual implementation in code. Specifically, I'm unsure about generating the eddies according to the energy spectrum, imposing a divergence-free condition, and handling eddies that exit the computational domain. Would anyone be able to provide guidance or recommend resources (books, papers, online tutorials) that include detailed explanations or examples of SEM implementation in code? Additionally, are there any open-source CFD software or libraries that provide SEM as a built-in feature, which I could use as a reference? Any advice or direction would be greatly appreciated!

Does anyone know the mechanism of shear production of turbulence? We know that a term with this name appears in the RANS energy equation, but what does it represent? Notice that energy can be transported or transformed, but never `produced’.

To explain: my interest is in wall-bounded shear flows, particularly in the atmospheric boundary layer. I know that the RANS energy equation is a statement that the divergence of the flux of mechanical energy equals the local dissipation rate. Why is the idea of local `production’ of turbulence kinetic energy so widely held when motions in the surface layer are, in reality, sustained by downwards transfer of mechanical energy from the flow above?

Do obstacles in a channel change the regime from laminar to turbulent while the Reynolds number is under 2300 (approximately 1000)?

Please introduce related studies.

I've worked on simulating 3D VIV (a cylinder forced by Karman vortex street) and been stuck with settings. Considering the transition to turbulence due to the oscillation of cylinder (Re≤200, laminar upstream), "SST k-ω coupling with intermittency transition" is my scheme now.

However, I'm not sure whether I should enable "intermittency transition" since I don't fully understand the statement given in the user's guide, which says "The Transition SST model is not Galilean invariant and should therefore not be applied to surfaces that move relative to the coordinate system for which the velocity field is computed; for such cases the Intermittency Transition model should be used instead."

I don't understand the bold sentence in the statement especially. Does it mean the moving surface of cylinder (in my case)? Hope anyone can provide any guideline. Thank you so much.

G'day,

I'm working on simulating 3D Karman Vortex Street, confused how to distinguish laminar and turbulence. As the pic shows (this is a frame before starting oscillation), there is downwash near the top of the cylinder, but the Reynolds number in this case doesn't exceed 200, namely, it should be laminar. ----------------update--->

Yesterday, I asked Perplexity Ai to find some info and it provided some reference talking about the wake structure due to the end of finite cylinder. So now I just want to collect your suggestions, since it seems no reference directly indicates "the downwash is inherent no matter the flow is laminar or turbulent". So if anyone knows relevant paper, please let me know, thank you so much!

[1]

I have been trying to get forced homogeneous isotropic turbulence field. I added a linear forcing term to NS equation with a fixed coefficient A. It works when my domain is 2pi cube. However when I scale my domain to mm i.e. my domain is 2pi*1e-3, the method is not working.

Hello all

I have two questions;

1- First of all, is it possible to generate HIT inside a rectangle? If so, it is called HIT again?

2- if Nx=Nz but Ny is different, How can I calculate power spectrum?

when Nx=Ny=Nz, it is possible to use below openfoam algorithm;https://www.openfoam.com/documentation/guides/latest/api/energySpectrum_8C_source.html

but in my case Nx=Nz =/ Ny

Thanks,

Farzad

SMEs need to build its resilience to survive in turbulent times. Identifying core competencies and factors that build resilience are challenging for SMEs. The question persists, what makes SMEs resilient in turbulent times?

Hello everyone

I have read Lundgren(2002) article "Linearly forced isotropic turbulence" which by adding a linear forcing to momentum equation, he was able to get forced isotropic turbulence. I did the same thing(I had initial HIT velocity field) and after a while flow filed start to make mean flow which theoretically must be zero. Also, TKE oscillates very dramatically which is not my desire. Now, my question is that, is there any other Linear forcing method or any other simple method to keep forcing to initial HIT and make it Forced HIT?

Thanks,

Farzad

Need a turbulence generator for generating the initial velocity field for direct numerical simulation of decaying compressible isotropic turbulence.

I have witnessed that higher values of TI lead to a decay in the Ct curves in the moderate range of wind speed (8-13 m/s). For high wind speeds it is seen that the changing TI does not have an impact on the curves but it does for moderate wind speeds. I've been trying to find a response for that, but it is not clear at all.

In the case of open channel flow, why does the turbulence shear stress remains constant in the turbulent logarithmic layer?

This developed turbulent flow codes is a finite element computer model, written in FORTRAN which was developed to solve the Reynolds equations of motion and continuity for steady and fully developed mean turbulent flow. The Finite Element mathematical treatment of this matter is reported in the following Ph.D. reference at CSU, Fort Collins, Co, the USA (please the link to this dissertation is https://mountainscholar.org/handle/10217/235417). Later on this code was extended to three dimensional and unsteady flows. The turbulent stresses appearing in the Reynolds equations are modeled in terms of mean velocity nonlinear gradients and turbulent viscosity. The non-linear algebraic stress model used in closed channels is totally different from the one used in open channels with new algebraic open surface proximity functions. A two equation turbulence model consisting of the turbulent kinetic energy (K), and its rate of dissipation (ε) evaluates the turbulent viscosity that appears in the algebraic stress models.

For wall bounded flows especially at corners (such as at an airplane's body to wing), my code succeeded in an excellent manner in simulating the main velocity, secondary velocities and turbulence structure (turbulent viscosity, K, and ε). In addition, distributions of the non-gravitational pressure and turbulent stresses are well predicted too. You may contact me for samples of the results of simulating turbulent flow in a square duct. Along the corner bisector the maximum secondary velocity divided by the average shear velocity is calculated as 0.31 which is in agreement with experimental data of Brundrett & Baines (1964) of about 0.32. For the wall bisector a value of 0.21 is predicted versus a measured value of 0.20 by Gessner and Emery 1980. Bulging of the velocity contours toward the corner is well predicted which is important in reducing separation due to adverse longitudinal pressure gradients. No up-winding or (over/under) relaxation are used. Non-linear Newton–Raphson that has quadratic convergence is used for dealing with the system of nonlinear equations. The boundary shear stress is calculated and is shown to be affected by the secondary velocities.

- For open channel flows: a very distinct feature in my nonlinear k-ε model is the use of an-isotropic turbulent viscosity in which the turbulent viscosity in the vertical direction differs from that in the lateral direction, a feature not existing in any existing CFD code to my knowledge. The model succeeded in predicting the depression in the main velocity maximum to be at 0.6 from the bed in a channel with aspect ratio of 1:2. The secondary velocity structure is also well predicted . Another important feature is prediction of the cellular secondary cells due to periodic roughness changes along the bed or walls. This helps in investigation of flow over ribbed surfaces.

Now How much does the source code for this turbulent flow CFD code worth in US $?

I appreciate honest answers and offers.

For more details I can be reached at my email:

Should I use openfoam for direct numerical simulation of compressible turbulence (decaying compressible isotropic turbulence)?

Dear All,

Good time,

In Ansys Fluent, I am using 2D non-premixed turbulent combustion model, I am using WSGG model to find the influence of radiation on participating species, my query is,

After thousand of iteration step, contour for temperature, is convincing and giving a good flame structure but incase of CO2 and H2O, what I see is, from flame till outlet the whole combustor is filled of CO2 or H2O, what parameter should I change/consider in my fluent simulation to make it better?

For your convenience I am attaching contour of both T and CO2.

TIA.

The maximum theoretical effect of a drag reducing agent (DRA) is the same as a pipe in laminar flow, where all of the turbulence is eliminated by the drag reducing agent. In this context, can a laminar viscous model be chosen for the simulation of the effect a drag reducing agent on the pressure drop in a pipeline with Reynolds number (Re) > 10^7?

Hello,

I have a hot-wire measurement record in the boundary layer in the wind tunnel. There should be fully developed turbulent boundary layer in the wind tunnel flow. I used Matlab to calculate the integral lentgh scale a then the non dimensional spectra. In the pictrure, I used the pspectrum function (red), Welch (cyan), Fast-Fourier transform (blue) to calculate the spectral densities. Then I added the von Karman spectrum (green), but the slope of its right part is not the same as the others. I added also the Kolmogorov inertial scale f^(-5/3) and its slope corresponds with the calculated spectrums. Do you have any ideas why the von Karman spectrum is tilted and the Kolmogorov scale is not? I am running out of ideas, thanks for help.

The artery geometry has an irregular shape where there is possibility of transitioning from laminar to turbulent for most cases in the stenoid(narrow) region , but for some of arteries flow remains laminar. What happens if I use KEpsilon for modeling all my 50 patients?

Hi all,

I am going to use either Ansys CFX or Ansys Fluent solver to simulate flow past a semi-submerged rectangular cylinder, as shown in Fig. 1. The main goal here is to achieve an accurate prediction of pressure distribution over the cylinder, which seems to be particularly reliant on the capability of the CFD model to predict the separation and the reattachment of the flow correctly. I would appreciate any tips regarding the following questions.

Q1. How important is the choice of the turbulence model? Which models are superior? Could the flow be modelled as being laminar at all?

Q2. How important is the approach to the free surface? Could the free surface be modelled as a free-slip wall to reduce computational costs? Is it necessary to precisely track the free surface using the VOF model for this study?

Q3. What are the most appropriate boundary conditions for the simulation?

Q4. Which is more suitable for this problem? CFX or Fluent?

All, It's known of curse that viscosity depends on pressure. Often this dependence is weak and sometimes it is significant. It is an odd and unmotivated question but, nevertheless, I am wondering if our parameterizations of turbulent viscosities should also include some measure of local pressure fluctuations? (This may be a question resolved long ago by people doing compressible turbulence.) I'm hoping to learn from people's answers and ideas. Thanks! Bill

Hello,

I am trying to measure the quality metrics of some turbulence blurred images with respect to the diffraction-limited image. I have about 9000 blurred images for each cases of lower and higher turbulence. But, it looks like my measures for mean PSNR and mean SSIM increases with higher turbulence. I am trying to find an explanation for this intriguing behavior. Any thoughts on this will be helpful.

Sudden reversal in magnetic field is the origin of switchback. The reconnecting field lines creates shear driven turbulence.

Dear All,

I want to generate an initial velocity field for homogenous isotropic turbulence. I learned that inverse Fourier calculates it from the energy spectrum of the form.

E(k)=k⁴e^{-2(k/k₀)^2}

However, the inverse Fourier transform of the function is not so straightforward. Does anyone know any good documentation for Fourier or Inverse Fourier transform for the turbulence energy spectrum?

Also, I have seen some papers using sinusoidal functions as the initial velocity field in HIT. However, I don't understand how it pops out from the inverse FFT of the energy spectrum. Can someone through some light on it?

Thank you

Krishna

I have a specific case about internal pipe flow with constant heat flux. Although the inlet boundary condition is laminar, the flow is a passing transition (a significant part of the tube) and turbulent regime along the tube (because of the change of thermophysical properties depending on implied heat). SST models with intermittency term (For fully laminar flow, γ = 0 and the model reverts to a laminar solver. When γ = 1, the flow is fully turbulent.) can catch laminar/transitional and turbulent flow regimes. These models were designed for turbulent inlet boundary conditions (models solve intermittency term, so it needs extra boundary conditions such as turbulent intensity). Can Transitional SST Models be used for laminar inlet / turbulent outlet boundary conditions? If so, what is the approach?

Regards,

EB