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Introduction:
Quantum physics is a branch of physics that studies the behavior of matter and energy at very small scales, on the order of atomic distances. The quantum world is governed by laws that are very different from those of the macroscopic world, and its properties are often counterintuitive.
One of the most surprising features of the quantum world is superposition. A quantum particle can be in two or more states at once, meaning that it can be in two different places at the same time, or have two different speeds.
Question:
Is it possible that the fourth dimension is the key to understanding the quantum world?
Arguments in favor:
  • Shadow analogy: A three-dimensional object can be projected into two dimensions by its shadow. This suggests that our three-dimensional reality could be the projection of a four-dimensional object.
  • Perception of four-dimensional beings: In a fourth dimension, a four-dimensional being could be able to see a moment in multiple states. This would explain quantum superposition, as a four-dimensional being could see a quantum particle in all its possible states at the same time.
  • Inconsistencies with the macroscopic world: The properties of the quantum world are often inconsistent with those of the macroscopic world. This suggests that the quantum world is subject to different laws, which could be explained by the existence of a fourth dimension.
Arguments against:
  • Lack of experimental evidence: There is no direct experimental evidence for the existence of a fourth dimension.
  • Speculation: The idea that the fourth dimension is the key to understanding the quantum world is a speculation.
Keywords:
  • Fourth dimension
  • Quantum world
  • Superposition
  • Shadow
  • Beings of four dimensions
  • Experiments
  • Quantum laws
  • Controversy
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As long as we are speculating, why stop at four dimensions? (We are arguably four dimensional beings when time is considered to be a dimension.) Mathematics for multidimensional analysis already exists, but our evolved bias favouring three spatial dimensions is a very strong handicap when we try to imagine extra dimensions.
In the years before dark matter and dark energy were proposed by very sharp scientists, my 3d spatial bias would have led me to believe that extra dimensions are impossible. But today we are aware of phenomena which cannot be explained by standard physics. So there is a plausibility argument for extra dimensions.
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Abaqus can be used to calibrate material data. To do this, you need experimental data and a simulation of the experimental set-up. Experimental and simulated results are then compared to determine how good the fit is. If the fit is not satisfying, the material properties are modified and the simulation is rerun, trying to obtain a better fit. This can be done manually, but it is labour intensive. So I am trying to automate this optimisation in Python for my summer undergraduate research project.
The attached file is the logic flow chart showing the summary of needs to be done.
I am trying to compare simulated data to experimental data and match them by adjusting Johnson-Cook material parameters (A, B, C, n , m).
So far I have been using GEKKO optimisation module in Python. The Python file is also attached to this question. Last time, the code worked for a stress-strain data for another material and the problem was in units of data.
Currently, I am using force-displacement data which is stored in the attached .txt files (force_2 and displ_02). The attached image is the result of running the code.
Could anyone help me to find a problem in either my logic or my code,please? Is the problem in values of parameters and force-displacement data?
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Hello Anton,
I am also currently working on the same problem. By any chance could you help me on the task by sharing more information if you have solved your problem already. I would really appreciate the help.@AntonPermyakov
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Hello,
I would like to know what should be the magnitude of displacement in abaqus in order to simulate experimental tensile test on mild steel and verify the stress/strain values?
I tried using the displacement magnitude as 1 but I am unable to reach the desired strain at the end increment.
My data line for boundary condition is,
*BOUNDARY,OP=MOD,TYPE=DISPLACEMENT
TOPP,2,2,1
Note: TOPP - Node set and its a non-linear problem with plasticity defined
Any sort of help will be appreciated.
Thanks!
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I suggest the following.
Desired end engineering strain = E
Parallel length of specimen = L
Apply displacement = LE
Regards,
Simon
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What are the available tools for conducting simulation experimentation on natural fiber composites?
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Ansys (Explicit Dynamics) is a user friendly tool for simple simulations involving fiber based composites.
Ansys with LS-Dyna plugin can offer more accurate results when impact studies are performed.
ABAQUS software platform provides superior simulation images to study the damage/failure mechanisms of fibers.
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Hi, I wanna ask for opinions/suggestions to reduce the percentage error between experimental and simulation results in the ANSYS workbench ( static structural).
First, let me explain my research.
I have cubic lattice structure samples (60mm x 60 mm x 60 mm) made from ABS thermoplastic material using FDM additive manufacturing. These samples were compressed using INSTRON 5585 universal compression test machine. The data from the test were analyzed to get the stiffness, young modulus, yield stress, etc. However, the results from simulation using ANSYS static structural were nowhere near the experimental results. For example, to get the stiffness, I divided the exerted force to the deformation and I get +82% error (simulation >> experimental). for young modulus, I divided the equivalent stress to strain and get -234% error (simulation >> experimental). I suspect these as the factors for the error
1. The samples made from FDM AM have too many manufacturing defects (interlayer failure+ porosity), yet to try using other AM techniques.
2. During the experiment, the area used was 60 mm x 60 mm as I cannot specify exactly the contact area (much less contact area) for the lattice to the bottom platen as the machine only asked me to put in the width and length of the sample. where probably (I'm not sure) the area used in the calculation in ANSYS is the actual contact area to bottom platen.
3. The structure is simply too complex.
I've tried using actual density and young modulus I get from experimental in engineering data and use force distribution plate to the top and bottom of the samples in ANSYS environment but not much changes.
also for case #1 is there any way I can render the geometry in ANSYS to have layers and voids as in actual samples?
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It is not quite clear to me what you want to do. Are you interested in the properties of the beams in your spatial frame, or are you seeking the properties of a homogenized continuum model? In the latter case, I can highly recommend the nice theory of cellular solids by Lorna J. Gibson, which she started in her Phd thesis supervised by Prof. Ashby. It is simple and gives good results and insights. You may find it in one of their books: Gibson, L.J., Ashby, M.F. (1997) Cellular Solids: Structure and Properties. 2nd ed., Cambridge University Press. https://doi.org/10.1017/CBO9781139878326
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For an interaction study, what would be the ideal temperature range for REMD simulation? Is 300 - 350 range having close to 55 replicas be too short a range to consider this under Replica exchange MD?
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I'm not able to open http://folding.bmc.uu.se/remd/ since a few days now. Is anyone able to access the site? Kindly inform.
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I am working decision making in social robotics using reinforcement learning. Regarding that real world scenarios usually do not provide the necessary reproducibility for experiments, i am looking for a simulation environment and benckmarking. Thanks in advance.
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Good question
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Dear Colleagues,
As Guest Editor I have a great pleasure to invite you to submit papers to the Special Issue "Additive Manufacturing of Cellular Structures Based on Metal Materials" in MDPI Metals, IF: 2.259. The main aim of this Special Issue is to publish scientific papers covering the recent problems related to the additive manufacturing of 2D and 3D regular cellular structures using metal materials, including the following:
mechanical behavior in terms of energy-absorption and crashworthiness capabilities;
  • experimental testing under quasi-static and dynamic conditions;
  • numerical modelling and simulation coupled with experimental tests;
  • microscopic studies of additively manufactured materials;
  • fracture and damage under low and high strain rates;
  • and others...
Keywords
  • additive manufacturing
  • regular cellular structure
  • energy absorption
  • numerical simulation
  • experimental testing
  • behavior of material
  • fracture
  • damage
For more information please go here:
Looking forward for your submitted papers!:)
Dr. Paweł Baranowski Guest Editor
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Dear Colleagues,
I am happy to announce that the deadline of this Special Issue has been extended to the end of this year (31 December 2020). You can check it in the SI website. Also, Metals have received an updated Journal Impact Factor of 2.117, and now ranks at 18/79 (Q1) in the category "Metallurgy & Metallurgical & Engineering".
Looking forward for you excellent papers !!!!
Best regards,
Paweł
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Hello everyone, i am trying to simulate experimental load-deflection curve of a reinforced concrete beam from literature with an analytical procedure using strain compatibility method.
I assume an initial strain at the extreme compression fibre of the RC beam and with the help of similar triangle concepts corresponding stress,strain in rebar, concrete bottom layer will be evaluated, moment and load are calculated using obtained stress multiplied by corresponding lever arm. The deflections are calculated by Branson's equation. My problem is that analytically obtained load-deflection curves does not match with the experimental curve available in the literature as seen in the attached picture. What could have been possibly gone wrong?
Can someone please help me?
Thank you in advance.
Best Regards
Naveen
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Dear Naveen Revanna,
I would recommend you to use in this particular case of a reinforced concrete beam the Degrading Trilinear Hysteresis Model.
Prof. Mikayel Melkumyan
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Hi everyone, hope you all are having wonderful day.
I am working on a baby inverse problem. I have a simple nonlinear model. To estimate the model parameters I can obtain two different sets of simulated experimental data (inverse problem crime) in the following ways:
Set 1. Experimental data obtained from 2^k factorial design. Set 2. Experimental data obtained from one extreme corner of the 2^k factorial design.
Interestingly, when I estimated parameters from these two data sets, I get better estimates when Set 2 was used. I thought that if the model is in good condition (without ill-posedness), using more experimental data should result in better estimates. My performance criteria is how close parameter estimates are to their assumed true values. Do you have any idea why I am seeing this behavior?
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Hello.
I assume that your calibration process involves using an optimization algorithm (to find a set of parameters values that minimizes the distance between the experimental data and the outcomes of the model).
With many points in the experimental data it is possible that there is more local minima in the optimization problem than with less experimental values. Depending on the optimization algorithm it can be harder not to be trapped in a local minimum that is not the global one. Thus non optimal values will be given. It is always a good idea to help the algorithm by giving him some a priori estimations of the parameters, though it is not always possible.
Another possibility is that the model is not able to reproduce some of the experimental data (this happens often in the real life). As a consequence these experimental data will always contribute a lot to the distance to minimize and may force the algorithm to search for the parameters values in a bad area, giving poor parameters values.
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Is there any inherent limitation in FDTD (as implemented by Remcom X-Fdtd or CST) that might preclude it from solving for certain electromagnetic wave modes? Specifically, I'm trying to simulate an experimental structure where we are measuring a surface wave in a lossy dielectric, but the X-fdtd model does not show the measured behavior.
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FDTD solvers use Maxwell's equations inside volume, there is no information about modes at this stage. Modes are attributed to "regular transmission lines". EM fields are calculated at ports to match modes. It might be done in different ways with some certain restrictions (depends on code). I think, You have faced some limitations of the code. The most propable reason is wrong port definition.
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Dear all,
I hope someone help me ,i made a model by abacus of expansive soil to evaluate the influence zone of pressurize water injection in the expansive soil to determine the best place of stabilizer.now i want to make calibration between my simulation and the experimental result .my properities is (density soroption moisture swelling permeability and mohr colomb),could someone help please
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A general answer on the calibration of simulations towards experimental data: it is called optimization. How does it work?
- You have a model.
- The model has parameters.
- Any change in the model's parameters produces different data output.
- Data output is compared to experimental data using some function (called cost function) that measures how similar are simulated and measured data.
- One possible cost function is a sum of squares of data differences.
- You find in the parameter space slightly changed parameters that lead to a decrease in the value of the cost function.
- It can be accomplished by a gradient method, swarm optimization, genetic programming, and many other optimization techniques.
The answer seems to be simple but in reality, some methods are more suitable for some problems and other methods for other situations.
Try to find an already existing, well-documented optimization software. I recommend Python, it is a very useful tool.
Additionally, try to study something about machine learning, Python is very good at it. Many problems are very well solvable using machine learning and AI techniques.
Good luck with solving your problem. :-)
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While reading the literature most of papers are based on these words.Normally these words are quoted to check the effectiveness of proposed method. What is the actual difference between these?Does online means your experiments are based on real time studies?
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According to my understanding online means the analysis gets done when the data is captured, i.e., Real time analysis. This approach results in speedy result generation.
In case of offline, the data is captured first and later on the analysis takes place, which may be a time consuming task.
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Usually, for a damped lumped mass system, the accurate FRFs can be obtained easily. And the simulated experimental FRFs data contaminated with noise can be obtained by adding a random noise to the impulse response functions, which are then transformed back to the frequency domain. For systems with structural damping models, the impulse response functions may be unreasonable, so I want to add noise to the accurate FRFs directly to obtain the simulated experimental data.The question is how I can make it?
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Dear Oktav,
Could you please tell me how to calculate the number of spectral lines ?
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I remember i saw such kind of theory somewhere but can't find it anywhere now.
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Simulation tells us the results/outcomes of a model when a given set of theoretical assumptions are fulfilled. It's usefulness to real life setting depends on the validity of theoretical assumptions in reality.
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Is there any method (optimization) for time delay change between the time of the sample collection and when it is assessed/examined?
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A new method to reduce the number of time delays in a network
Time delays may cause dramatic changes to the dynamics of interacting oscillators. Coupled networks of interacting dynamical systems can have unexpected behaviours when the signal between the vertices are time delayed. It has been shown for a very general class of systems that the time delays can be rearranged as long as the total time delay over the constitutive loops of the network is conserved. This fact allows to reduce the number of time delays of the problem without loss of information. There is a theoretical lower bound for this number that can be numerically improved if the time delays are commensurable. Here we propose a formulation of the problem and a numerical method to even further reduce the number of time delays in a network. Source: Scientific Reports | 7: 2744 | DOI:10.1038/s41598-017-02978-5
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Dear Sharif Khan, 
Thanks for your suggestion, but I would normally do that.
best
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I am trying to create a simulation for an experiment that already has been conducted. I will then compare the obtained results with the data from the experiment. The experiment involved a 3m tall square tank full of soil, and a fragment of pipe burried 2m in it. This pipe has been then slowly pushed and pulled sideways by 4 actuators that were set to displace it for a curtain distance (e.g. 30cm) in 5 cycles back and forth. The output values were the forces on the pipe and the forces on the walls of tank that comes from soil. Is it possible to simulate this in Plaxis 2D? with the input data = displacement, and try and obtain forces on pipe.
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Exact pipe movement cannot be simulated in PLAXIS since it involves material relocation and strain incompatibility in surrounding zones. However, a cyclic movement in terms of cyclic displacement can be given to the zone , which represents the pipe and the corresponding forces in soil and walls can be computed.
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I need to gather the diminishing wave graphs of water ripples (height over time), as caused by a water droplet or other central disturbance, in a circular tank of water.  The points I'd measure would be along an arbitrary line that originates at the center and goes to an edge of the tank (because I'm assuming the central disturbance will generate circularly symmetric waves).  How could I gather the data necessary to generate the diminishing wave graphs at each point?  Using bobbers/floats and aiming a high-speed camera perpendicular to the water's surface with a grade scale behind the clear tank won't work because the bobbers/floats likely would move freely around in the tank and not stay still.  I only want them to move up and down--to measure the height of the surface of the water as it ripples.  Would multiple lasers be able to detect the water's height?
Alternatively, if there is a (preferably free) software that can simulate simple ripples and generate the needed data, I would gladly be open to that route.
Any help on this issue would be greatly appreciated! Thank you.
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Definitely a good idea, Michael; I was hoping that I could get away with using a single line laser, so I wouldn't have to invest in multiple lasers.  And yeah, if I could find an accurate simulator that I could pull data from, I would definitely love that approach (would save a lot of time and labor)
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I am doing an experiment of damper which is made of soft steel, the damper has the characteristic of complex-damping, the Hysteresis curve of experiment is perfect,but when I use abaqus to simulate experiment, the Hysteresis curve I got has Bauschinger effect, but it not appeared in experiment, how should I set property of damper in abaqus to avoid Bauschinger effect?
The style of steel in experiment is Q195
The parameters I set in abaqus:
young`s modulus: 210000, poission`s ratio: 0.3
 Yeild stress:250 ,Plastic strain:0
The comparison of Hysteresis curves I plot in the picture below, to understand my question more. Thanks for your answer
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When you create a new material in Abaqus, the preset hardening model is the isotropic model. It means that no Bauschinger effect is pre-established in materials.
If you want to simulate the hysteresis loop after high number of cycles of an experiment, I believe that it would be easier if you use a hardening model "Combined" with data type "Stabilized", where the tabulated values of Yield Stress and Plastic Strain define directly the curve of the hysteresis loop, not the first curve of the experiment. The plastic strain that you should introduce in Combined/Stabilized model is the upper plastic curve of your experimental hysteresis loop when it is stabilized after some cycles, and it should be extracted from experimental results as shown in figure attached.
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Hello,
I am simulating Wakefields/impedances of some accelerator components like taper transitions, cavity like structures and some others. I heard that indirect test beam method yields more accurate result than direct one. Is it true? If so, how can I simulate more accurately the wakefields produced by tapered structure? Can I add some PEC material at both ends to make cavity like structure?
The manual says if the cross sections at both ends are same we should be able to use indirect test beam method however in some cases, I could not use indirect test beam method though their cross section where the beam enters and leaves are same (see the following screen shot image).
Would you mind sharing your views based on your simulation experience about these issues? Thank you in advance for your answer.
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Thank you Mohamed for providing me good resources related to my research. Hopefully, I will get my answers after reading these materials.
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Hi evryone, I want to ask where can I find " Uncapacitated Facility Location Problem " pseudo-code.
Is there any Library of Simulation Experiments or Datasets like for constrained and unconstrained optimization test functions ? Otherwise, any documentation to start with ?
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As the Uncapacitated Facility Location Problem can be formulated as an
integer linear program,you can also try Lingo software to solve it.
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I want to make combustion model of HCNG Engine then how I would start the combustion modelling
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@Maciej Mikulski Thankyou so much for your detail answer. I got full idea that how I have to do my physical modelling.
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I have few questions if any one can add to it:
1. I want to run a simulation with experiments done in free surface water tunnel. Which model you think can predict good reattachment length.
2. I am using turbulent length scale 6mm and turbulent intensity of 5%. If the reattachment length is not close which parameter should i adjust.
3. I am using 0 pressure gauge as outlet boundary condition. However experiment was conducted in close recirculating water tunnel. Is it correct? or should i use outflow as boundary conditions.
Thanks
Looking forward to hear from you soon.
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I did in the past several numerical simulation using the k-ε standard model with reasonable results. However, The k-ω model was claimed to perform better in adverse pressure gradient and to reproduce better flow separation than the k-ε model. You could even try the SST model, which is aimed at using the favourable properties of both models.
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Hi everyone i have do some optical measurement which shows me a particle which is of 900 nm when i have performed SEM with higher magnification then i amazed that it was not a single particle instead it is the sum of the particles. now i have spent a lot of time on the simulation and experiment things the particle look like as in figure attached. i want to get the scattering near field.Now if i have to draw this type of figure in the FDTD software or FEM software like comsol can i get still right response there are about 20 particles are inside it.. Please suggest me your suggestions are really useful for me at this time. kindly if you have literature then also please suggest.i will appreciate that.thanks
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hello,
First of all this is very interesting question, i would like to ask are the  particles mono dispersed. If not then it is quite possible that there are random aggregation of smaller nano particles to form a large cluster like 900nm. if your SEM image shows equally distributed mono dispersed particles then this is something new, u can definitely define number of smaller spherical particles in a large sphere and work the simulation.
also scattering depends on the number of particles in your sphere so if the number of particles are strictly defined (mono dispersed )u can get desired results by simulation.
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I am having a problem getting my solution to converge.
  • I have a tank with oil in it, and a heater in the center, the temperature difference causes natural convection flow within the tank. I am trying to simulate this experiment using FLUENT. I am fairly certain I set up the boundary conditions correctly, and my mesh seems OK.  It was converging (excruciatingly slowly) but at least it was. Then it seemed to get "stuck" before reaching the convergence criteria. So I tried many different ways to get it un-stuck . All I have succeeded in doing is cause it to start to diverge again. I need some serious help. Can someone point me in the right direction please?
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Check also if the regime is not unsteady (chaotic, turbulent, periodic) due to high flow characteristic number (Reynolds, grashoff, ...): this if the problem is correctly set up (modelling, boundary conditions,...).
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I would like to measure the performance and observe the pattern of I/O interactions between VM and SAN. Anyone can suggest any tools/simulators or experiment based for me to study. I'm not sure whether CloudSim can do simulation for SAN
Thank you
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 Hi Prof Rashmi,
Thank you and I'll look into that too.
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I am using witness to simulate a production line... I wish to do ten times replications with wotness experimenter... anypne could help ?
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Hi there,
Experimenter is the name used in versions after the 13 one, before that they used to call it optimizer. In order to used the experimenter you need to have something to optimize, i.e.: you need to define a function from the designer elements bar. After doing that you should define a function body and then its other parameters. The Atime would give you the time part(s) spent in the system. Moreover, if the elements of your system/devices/machines have elements of variability, e.g.: levels of speed, pressure, etc...then you can, e.g. use the different methods in the optimizer to look for the best combinations/permutations that minimizes the total time parts spend in the system. At the end, this is just an example, your particular case may well demand different functions and procedures
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Would reproduce this experiment numerically to observe the turbulent behavior of the ink
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Thanks ... I tried to do what you say
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I want to design some simulation-based scenarios involving students from different health care disciplines, and also assess the effectiveness of interprofessional simulation experience on student's learning. 
Would you like to share some suggestions about the whole process (design, implementation, and assessment)?
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Hi! 
Why don't you get in touch with Icons Project? They design simulations and may give you a hand.
best,
Laura
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Normally, the environment of a quantum system could be supposed to be a thermal state, or a squeezed vacuum state or a squeezed thermal state. Then can these baths be realized or simulated in experiments?
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Thanks very much! Could you suggest a few papers that exactly demonstrates the experimental scheme?
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Dust-crystal double helix structure
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It seems to me that the class of helical structures described in the papers quoted by Bacharis falls within the broader class of helical structures discussed in the enclosed paper (http://arxiv.org/abs/1404.6959).
In fact, in this paper it is shown that a Faddeev-Niemi non-linear sigma model describes in the long wavelength limit a wide class of steady-state, knotted configurations of physical systems far from thermodynamical equilibrium where all following assumptions are satisfied:
a) they are stable against perturbations of temperature;
b) they interact weakly with the external world;
c) they exhibit negligible temperature gradients;
d) Coulomb interactions are effectively screened;
e) entropy is mainly produced through Joule and viscous heating,
f) inertial effects are negligible in comparison with diffusion effects (or, alternatively, the assumption of "local thermodynamic equilibrium" holds).
If, furthermore, g) the Gauss linking number is lower than a threshold, then the model describes filamentary structures.
Regardless of their detailed microscopic structure, in the long wavelength limit stable filaments adjust themselves in order to offer minimum resistance to the medium (fluid, plasma) embedding them and to the electric currents (if any) flowing across them. 
The resulting structures exhibit heicoidal symmetry. 
These structures include a broad class of filamentary structures routinely observed in Dense Plasma Focus physics [H. Herold, A. Jerzykiewicz, M. Sadowski, H. Schmidt, , Nucl. Fusion  29 , 1255 (1989)].
(However, they seem to be relevant to many other fields, as spontaneous relaxation to knotted configurations turns out to be e.g. the final outcome of the evolution of waves propagating across dispersive media described by non-linear Schroedinger equation [A. S. Desyatnikov, D. Buccoliero, M. R. Dennis, Y. S. Kivshar, Scientific Reports 2, 771 (2012)]). 
If the conclusions of the enclosed paper are confirmed, then irreversibility affects the very topology of the final outcome of a relaxation process which leads to the spontaneous formation of helicoidal structures both in dusty plasmas and Dense Plasma Focus plasmas. In a nutshell, thermodynamics is the same.