Discrete Element Method for better Mining Operations

The mining and mineral processing industry operate in a very high tonnage range which makes the equipment more prone to damage and wear. This also means that testing new designs at plant scale would lead to material wastage and time loss.

Another aspect that differentiates the mining and mineral industry from others is the fact that the raw material keeps changing from season to season. In the rainy season, the material will be wet and sticky and in summer it will be dry. This requires the design engineers and researchers to come up with a design that can work in both these conditions.

Discrete Element Method is a proven tool for designing and troubleshooting equipment. Mining and Mineral companies across the globe have included DEM in their development process and routinely use them for troubleshooting. In this blog,

What value will a DEM tool add?

Any simulation technology will help the user to get a deeper insight into their equipment/process. Testing at the plant scale also becomes an easy task as there will not be any material loss and downtime of the plant required.

Let’s take a very simple example of a chute. In case of a decrease in throughput of the chute, it becomes difficult to understand the underlying reason sometimes. Identifying the right area where material clogged will be really helpful in increasing the throughput. A DEM simulation can show the velocity profiles of all the particles inside the chute which help us identify the faulty region. Getting this type of insight from the real chute will be difficult as that would require us to have a chute made of transparent material which is impractical at plant scale.

What processes will I be able to design/troubleshoot?

Almost all mining-related processes can be handled using DEM. Here is a non-exhaustive list:

  • Excavation at the mining site
  • Wagon loading and unloading
  • Primary crushing
  • Stockpile
  • Conveying operations
  • Screening/sorting
  • Blast furnace
  • Fluidized beds
  • Packed beds

Is DEM capable of handling real-life material loading conditions? RockyDEM uses the power of multi-GPU processing to make plant scale simulations reasonable. Figure X shows a comparison of CPU vs GPU vs multi-GPU. Using multi-GPU will help us make these simulations way faster when compared to CPU. But that’s not it, with the new modules being introduced inside RockyDEM, the data saving time has decreased which would lead to total time-saving. Also, the advanced contact detection method helps reduce overall simulation time. We can further reduce the simulation time by using the Coarse Grain Model.

Figure x- Scalability using CPUs, GPUs and multi- GPU`s

How can I be sure that the simulation results are accurate?

All the models are implemented in Rocky are well-validated which means that we do not have to worry about the models predicting the behavior if supplied with accurate inputs. The most important input required will be material properties. To extract the material properties and correlate them to simulation parameters, calibration is required. For example, an angle of repose test which can be performed in the lab should also be performed with RockyDEM virtually and the same behavior should be replicated. Properly calibrated material properties will provide us with a highly accurate result.

I would also like to understand the effect of particle flow on my equipment, how can RockyDEM help me with that?

RockyDEM has some in-built post-processing capabilities which can be used to plot contours of stress and force on the equipment. RockyDEM is also integrated with ANSYS products like ANSYS Mechanical. So, the force maps from RockyDEM can be transferred to ANSYS Mechanical and a structural simulation can be performed to evaluate deformation, etc.., We can also perform a transient coupled analysis with ANSYS Mechanical.

We answered a few questions which any mining company might have while thinking about adopting simulations. Keep an eye on this space.

Author Name: Mr. Ishan Vyas (Application Engineer at CADFEM India)

Profile Bio: Mr. Ishan has completed his bachelor’s in Chemical Engineering with minors in Material Science. He is currently associated with CADFEM as an Application Engineer in the CFD and DEM domain. He holds an experience primarily in the areas of applications related to Chemical, pharma, oil, and gas industry involving both CFD and DEM codes. The Simulation of Chemical processes implementing DEM for New product development deeply interests him. He is an avid reader and loves Adventure sports.

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Wield Enabling Tool to Master CFD Meshing

The quality of essential simulation output depends absolutely on getting the mesh right. The kind of refinement and the type of mesh used should go by physics in place. For instance, a flow with dominant turbulence generation from the boundary layer separation needs a better focus on the boundary layer refinement to keep the Y+ = 1. A premixed combustion simulation in a SI engine will require an LES turbulence model and therefore a mesh refinement in the bulk to capture about 80% of turbulence kinetic energy there. Cases with moving parts in a fluid flow have to mesh with a priority to avoid negative volumes through dynamic meshing. Narrow gaps, a long list of parts in a big automobile assembly, etc.., are some overhead complexities demanding diligent craftsmanship.

This blog article shall apprise on the befitting advantages of “Ansys Fluent meshing” to thrive through such challenging meshing tasks

Endorsing motivators to adopt and benefit from Ansys Fluent Meshing

  • Generates polyhedral meshes, polyhedral prisms can easily uphold mesh quality for refined boundary layer regions.
  • Offers wrapping advantage to mesh large assemblies.
  • Parallel mode execution without using any HPC licenses, consistent speed scale-up.
  • Can run with both Solver and Pre-post license.

I choose to narrate some of my recent personal experiences as a CFD user to shed more light on these features of flair, which is worth a deep dive. Going by its craft, an electric motor meshing pursuit is the best fit to kick off this section.

While performing a Conjugate heat transfer analysis for an electric motor I faced few challenges with mesh generation. Fluent meshing with its guided task-based workflows and best in place algorithm helped in meshing complex geometry with good quality within less time & economical mesh count. In this post, I will be discussing the fluent meshing approach & how it helped with the pre-processing for motor thermal analysis.

For the electric motor analysis, I was able to achieve conformal mesh with good mesh quality but the mesh count was higher initially. Higher mesh count will consume more solving time & hence I was looking up for options that can help me reduce the mesh count & still preserve the mesh quality. One of the reasons for high mesh count was the proximity settings where the solids were also meshed with a fine sizing to maintain conformal mesh. So, a non-conformal mesh approach was utilized with solids having a bit coarse mesh which provided an advantage to generate a fine mesh to the fluid regions. With fine-mesh confined to the fluid regions, mesh count with a non-conformal approach was reduced to ~60%. But with non-conformal mesh, I had to invest time in assigning the mesh interfaces, which eventually consumed more time as multiple mesh interfaces are involved.

The same model was tried in fluent meshing under the conformal polyhexcore mesh approach, this time with prism boundary layers included. 8 cores were used for parallel meshing which exponentially reduced the meshing time. The mesh count was reduced to 40% compared to the tetrahedral mesh & the mesh quality was within the acceptable limits. For the boundary layer resolution as well, the polyhedral prisms helped in maintaining the quality compared to tetrahedral prisms within narrow air gap regions. Results for the 3 cases were compared & there was a good agreement. So, from this experience, I observed that fluent meshing can help in reducing the pre-processing time to a greater extent & I would highly recommend this approach. In the next part, I will be discussing more regarding various capabilities provided in fluent meshing which will highlight the extent to which fluent meshing can simplify pre-processing work.

Fluent meshing is developed with the capabilities to provide native polyhedral mesh which helps in reducing the mesh count while preserving the mesh quality. It is integrated with fluent to form a single-window workflow for CFD simulations. So, one can switch directly from fluent meshing into fluent setup, solution & post-processing module. Additional advantages are parallel meshing where one can utilize parallelization over multiple cores not just for accelerating solving but also meshing which can reduce the pre-processing time drastically. Polyhedral prisms can fit in narrow gaps without suffering distortion compared to triangular prisms which are good for boundary layer resolution. The task-based workflows provide a guided stepwise meshing approach using which one can setup meshing parameters & can edit them later if the mesh resolution is not as expected. Fluent meshing can perform conformal mesh for Watertight geometry by capturing all detail features within the geometry. In case of poor quality with surface or volume mesh, one can add a “improve mesh” option which activates the auto node movement option to improve the quality of mesh to the desired value. Apart from tetrahedral, hex-core & polyhedral meshing, Fluent meshing offers a unique option of polyhexcore or Mosiac meshing technology.

For More Information on task based Meshing watch the webinar

For dirty geometries with leakage and overlapping parts, an analyst has to spend hours preparing a watertight geometry for simulation. Fluent meshing is equipped with a Wrapping technology to capture the complex or dirty geometry. Developing a watertight geometry for components like engine or complex assemblies can be a time-consuming activity. Under hood analysis with complex & interfering geometries like engine, radiator & chassis can easily mesh with wrapping technology. Native cad file formats like STL & step or can be directly imported & part management can be performed to select the simulation model. While meshing a dirty geometry some complex features which have less impact on the simulation results can be overlooked using wrapping technology. In case the wrapper has missed any features of interest than using the edge feature extraction method one can fine-tune the wrapping function. Another option of Leakage detection allows the wrapper to patch the leakage in geometry below the provided threshold value thus eliminating the tedious need to work at the geometry level. Fault tolerant YouTube video.

For More Information on Wrapping Technology watch the Webinar

In a nutshell, fluent meshing can incorporate any type of geometry, and using the appropriate mesh strategy one can develop high-quality meshes with appreciable ease. Considering the type of complex products being deployed into the market, fluent meshing with wrapping technology and guided workflows is definitely the promising technology to reduce the overall pre-processing effort.

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Confronting COVID-19 with Optics & Photonics

At the zenith of such a situation when the whole world is fighting against a pandemic COVID-19, a multidirectional approach for combating requires excessive attention. The quick detection of infected patients stands as a primary challenge due to variability in the symptoms. Though the field of Optics and Photonics provides various conventional molecular analysis instruments such as multiple spectrum cameras, multispectral optical spectrometers, and they still couldn’t achieve a potential detection method for the masses. Moreover, these methods are time-consuming, prone to error, and may lead to respiratory infections.

Recently, a method involving biosensor which uses thermal and optical effect for safe and reliable detection of COVID is to be seen in a picture. The Plasmonic biosensor used for detection combines Plasmonic Photothermal (PPT) and Localized Surface Plasmon Resonance (LSPR) on a tiny gold nanoisland chip kept on a glass substrate. Artificially produced DNA receptors that match specific RNA sequences of the SARS-CoV-2 virus are grafted onto the AuNI chips. Through Nucleic Acid Hybridization, sensitive detection of specific RNA sequences of the SARS virus is done. LSPR creates plasmonic near field by exciting the metallic nanostructure. The change in the refractive index is measured using an optical sensor helping in determining if the sample contains RNA strands of SARS. The LSPR response due to plasmonic sensing determines the concentration of sequences ranging from 1 pM to 1 nM. PPT helps in boosting the ambient temperature to secure the detection of only reliable matching of RNA strand and DNA receptor.

The sensing stability, sensitivity, and reliability of the device can be significantly enhanced by measuring the same at 2 different angles under 2 different wavelengths. The thermo-plasmonic heat is generated on the AuNI chips while being illuminated at their plasmonic resonance frequency for better sensing performance. The localized PPT heat can elevate the hybridization temperature during the process and facilitate the accurate discrimination of two similar gene sequences.
Though this technique is not yet efficient for a high frequented location, yet it embarks a step closer towards the modern techniques of simulations for such complicated processes.

ANSYS Lumerical Suite comes with all the tools which are required to simulate surface plasmon resonance(SPR) and photothermal heating in plasmonic nanostructures. It comes with a Finite difference time domain (FDTD) solver and heat transport solver for thermal simulation.

The picture below (fig 1) shows the simulation done for getting a source incidence angle that excites the SPR. The absorption in the Silver was measured to determine the angle with the strongest coupling.

Fig:1 Electric Field intensity vs Incident Angle
Source: Lumerical Knowledge Base
Simulation is done on plasmonic nano-structures to understand the effect of varying optical intensity on the performance
Source: Lumerical Knowledge Base

Want to explore how ANSYS solutions can help you in speeding your photonics research? Visit us at https://www.lumerical.com/

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Interpretation & Importance of Mass Participation in Modal Analysis

We often come across the dynamics or vibration problems in most of the product development across all industries.  We have gained enough knowledge through the degree of Bachelors or Masters, with an example of the famous engineer disastrous design of “Tacoma Narrows Bridge-1940”, that considering the dynamics effects in design is quite critical.  Generally, a dynamics problem is categorized into linear or nonlinear dynamics. This blog is related to the linear dynamics and nonlinear dynamics will be discussed in length in another blog. The most commonly heard terminology which we stumble upon in the design of such vibrating products is “natural frequencies“, “resonance“, “eigenmodes” etc. We have many linear dynamics simulation techniques such as “Harmonic Analysis“, “Random Vibration“, “Response Spectrum“, “Transient Analysis“, “Acoustic Analysis“, etc based on the loading it undergoes and also on the output we are interested in. But all these dynamics simulations start with the first step called “Modal Analysis“.

Modal Analysis

What makes something unique is an identity of its own, right? What if I say natural frequency is the identity of a body? Let’s see why. Assume you are imparting energy (striking) to a body. What will happen is obvious, the body will start to vibrate. But the vibrational frequency will be in its natural frequencies. It doesn’t matter how much or where you give the energy it will always vibrate in its natural frequencies, period.  This is why the base of any dynamic analysis is said to be modal analysis as it shows its identity or behavior.

The modal analysis calculates natural frequencies and mode shapes of the designed model. It’s the only analysis that doesn’t require any input excitation or loads, which also makes sense, as mentioned before natural frequencies are independent of the excitation loads. Natural frequency depends only on two things mass and stiffness.

Why do we need to do the modal analysis?

Yes, we can find natural frequencies, and mode shapes through modal analysis but why to do it? Let me explain that through an example, when you ride a motorcycle, sometimes you might have experienced vibration in your handle. Some bikes have too much vibration that, you can’t even use your rear mirror. This is because the handle’s natural frequency is matching with engines RPM.

Structure borne noise: A structure makes peak noise when it’s vibrating in its natural frequency.

Whirling of shafts: To find the critical RPM of a shaft so that, crossing the critical speed will be done with precautions to avoid resonance.

Avoiding resonance is always preferred, thus shifting or modifying natural frequency can help to control, above mentioned issue. Natural frequency depends on only two parameters, modifying them will help to design a dynamic body part.

As an engineer, I always go in search of the physical meaning rather than proving by an equation. But at times equations are unavoidable.

Now let us see how we can get the natural frequency of a system.

Excitation force Equation of any dynamic system can be represented as

Every natural frequency is associated with each mode shape and mode shape represents the displacement behavior. There is enough material on the internet which explains mode shape. Right now, let’s concentrate on the “Mass Participation Factor” other important information from modal analysis, which would assist us in the simulation of other linear dynamic simulations.  What is mass participation in general? Consider a 3 massed system as shown below.

The figure shown above is a mode of the system. Here masses participating in the X direction are M1 and M2  and thus total mass participation is M1 +M2.  The only difference in FEA is that instead of lumped masses, the system will be discretized by elements.

Now let’s see ANSYS modal Analysis. Here I’m taking a cantilever beam, with 0.8478 kg. Having a cross-section of 3*60*600.

As you can see from the participation factor calculation. I have solved the first 12 natural frequencies. In this table, ANSYS solver calculates only frequency and Participation factor ie 1st and 4th column. Every other column is calculated from these two values. Let’s look at the most important column one by one.

As you can see this ratio is in direct comparison with total mass, which gives a better clarity on how much of the total mass participation is extracted. In other words, the sum of the effective masses in each direction should equal the total mass of the structure. But clearly, this depends on the number of extracted modes.

The same logic is for rotational mass participation. But here you might get ratios bigger than 1. Then, there can be confusion as mass participating is more than the total mass of the system? The answer is, participation factors for rotational DOFs are calculated about the global origin (0,0,0). The calculated effective mass essentially contains a moment arm (effective mass multiplied by the distance from centroid). Thus, the effective mass for the rotational DOFs can be greater than the actual mass and the participation factors can be greater than 1.0.

Ideally, the ratio of effective mass to total mass can be useful for determining whether or not a sufficient number of modes have been extracted for the “Mode Superposition” for further analysis. Modal superposition is a solution technique for all linear dynamic analyses. Any dynamic response can be calculated as the sum of the mode shapes, that are weighted with some scaling factor (modal coefficient). Also, the time step of the transient simulation is calculated based on the most influential mode in the modal analysis.

Modal analysis is usually done without considering any force or working environment. This assumption is taken by considering mass and stiffness to be constant. But at times this doesn’t work, like a pressurized vessel or pipe. As compressive or tensile force changes the stiffness. In such cases, we have to do a prestressed Modal Analysis.

In some cases, like rotating shafts with motors, turbines, the natural frequency will not match with the working condition. This is because, for different RPM, stiffness also changes,(Centrifugal force changes). In these cases, we need to consider the rotational speed of the shaft. In ANSYS Modal Analysis (Rotodynamic Analysis) rotational speed with Coriolis effect can be directly given to find the critical speed of the shaft.

Modal analysis is a prerequisite for all linear dynamics analyses like Harmonic, Random vibration, and Response spectrum analysis. Hence Modal Analysis plays a huge role wherever you are trying to find out vibration characteristics of a design. Thereby it is relevant for most industries like Rotating machines, construction, automobile, Machine tool, agricultural equipment, and Mining.

Engineering Simulation opens up a huge range of possibilities. Since CAEsimulation requires more than just software, we at CADFEM help our customers to adopt simulation for success in every phase of product development, thereby enabling them to realize their product promise.

Please click here to understand more about NextGen Engineering Simulation Possibilities.

Please feel free to connect with us at marketing@cadfem.in or +91-9849998435, for a quick Demo on this Product.

Author Bio:

Mr. Vineesh Vijayan, Application Engineer

CADFEM India

Mr. Vineesh Vijayan has done his Master of Technology in Machine Design. With good expertise in Structural Simulation currently, he is contributing his efforts to CADFEM India as an Application Engineer.

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ANSYS Fluids R19 – Release Update

This post discusses latest developments and enhancements in ANSYS Fluids R19 applications. Maximize your RoI and productivity with the latest ANSYS release.

How we use simulations has changed drastically since its inception. A couple of decades back simulations were majorly used for research purpose.  But today it is used for various applications ranging from airplanes to microfluidics. Simulations have also evolved to handle more complex problems in smaller run times. ANSYS, a leading simulation software company is constantly innovating to make simulation easier to use and at the same time making them more robust. With every release, the GUI is getting better and the solver is getting smarter. Hence, without further ado, let’s take a dive into a few enhancements in ANSYS Fluids R19.

In this article, I will discuss some of these developments however, I recommend you to join the upcoming webinar that I will deliver on April 17.

Enhancements for Spray Modelling

The new feature in ANSYS Fluids R19 would significantly reduce the computational effort needed for spray nozzle designers to optimize product performance. CFD has been used for modelling sprays for a while now. Multiple approaches are available for spray modelling namely, full resolution (resolving all the length scales in the spray), semi-empirical (uses empirical correlation for droplet break up and stability analysis to generate droplet data), etc. ANSYS Fluids R19 has significantly enhanced spray modelling using VOF (volume of fluid)-to-DPM (discrete phase modelling) approach. As a result, you can directly track interface instabilities and surface tension effects that result in ligament and droplet formation. Due to this, you’ll get fast, accurate spray breakup and droplet distribution with minimal effort.

ANSYS Fluids R19 - Simulation of Fuel Injector
Simulation of high pressure fuel injector spray (Fluent R19)

ANSYS Fluids R19 - Spray Jet Simulation
Simulation of a Spray Jet in Cross Wind (Fluent R19)

Accurate Preventive Maintenance

Engineers seeking to maximize up time and optimize preventive maintenance programs need to reliably predict the location and extent of erosion in pipelines that are carrying particle-laden flows. Previously, static meshes could not account for structural changes in the pipe caused by erosion and its subsequent impact on fluid flow, thereby reducing prediction accuracy. New technologies in Fluent R19 automatically couple structural changes due to erosion with a dynamic mesh so that the simulation more fully captures the degradation arising from erosion.

ANSYS Fluids R19 - Erosion Modeling
Erosion Modeling

More Computational Power

To empower the users with more computational power, significant changes have been made to the High-Performance Computing (HPC) solution.

  • High-Performance Meshing Technologies that help in meshing the complex geometries at lightning speed. Higher Productivity.
  • All core solver technologies utilize four (4) cores without HPC License Checkout. HPC products add on top of these four cores. Hence, this gives you more value for money.
ANSYS Fluids R19: Other Noteworthy Enhancements
  • Blade flutter modelling
  • Risk assessment for Urea Solid Deposition for SCR
  • Lagrangian wall film
  • Thermolysis model
  • Local residual scaling for multiphase
  • Shar/Dispersed discretization schemes with Mixture Multiphase
  • FSI: Accurate leakage flows through narrow gaps
  • Species mass transport improvements
  • Cavitation modelling improvements
  • Native rolling ball fillets
  • Variable shroud gap
  • New Workbench templates

In conclusion, this article has only covered the tip of the iceberg. There is much more to learn about ANSYS Fluids R19. Join us on April 17 for the ANSYS Fluids R19 Update Webinar to get the details! Register now.

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Porous Media: Top 3 Modeling Challenges

In this article, I will describe a fairly common procedure to model porous media accurately and address frequently-asked questions.

Tesla Model 3 was recently launched amid much media reporting. In fact, Elon Musk tweeted to his followers about filtration. There was an article which said that Tesla’s Model X purifies air in less than 2 minutes!

So, how does Tesla make it possible? Porous media cane help them achieve this. By porous, we can infer a substance to have minute interstices through which fluid may pass through it. Porous material is permeable if the interstices are interconnected or continuous thereby making a fluid to flow through them. Massive amount of consolidated energy wastage (due to improper combustion and left of un-burnt particles) happens due to this impure air. For efficient fuel burning, there is the pressing need to filter air before passing it through any combustion device. Another application that is quite relevant to this topic is of air conditioners – all pervasive at homes and our workplaces. In all these applications, impure air passes through a series of filters. The interstices present in these porous zone filter holds off solid dust particles and parses clean air.

These concepts are ubiquitous in nano- and micro-scale applications, oil reservoirs and geophysics applications, electronics cooling, thermal insulation engineering, nuclear waste repository, biomedical, biological and environmental applications, grain storage and enhanced recovery of petroleum reservoirs among many others. Today we need to explore innovative approaches to effectively apply existing porous media technologies to these applications. These porous media play a vital role in gas turbine inlet filtration systems. A typical pollution eliminator contains different type of filters such as bag filters, cartridge filters, EPA (Efficient Particulate Air), HEPA (high efficiency particulate air) and ULPA (Ultra Low Particulate Air) filters etc.: each filter has a specific purpose and level of efficiency.

Fluid Flow Effects in Porous Media

Porous Media Flow
Example of Porous Zone with minute interstices through which fluid can pass through (Courtesy: ANSYS Inc.)

Design and shape of the filter plays a crucial role in evading compressor surge and improving the performance of a system as a whole. It is very crucial to keep the flow conditions at a minimum total pressure drop by adopting a filtration system that suits the operational environment.

During filtration, fluid experiences certain changes such as:

  • static pressure rise due to diffusion,
  • reduction in the flow energy, thereby making it more laminar based on the porous medium’s permeability,
  • heat transfer effects through the porous zone, etc.

Today simulation plays a significant role in understanding filter performance and filter housing design to deliver adequate air flow distribution by translating a physical scenario into a math-based numerical model. As simulation engineers, we will need to model porous media to recreate these effects.

Using ANSYS FLUENT interface, I will explain the process here onward. In ANSYS FLUENT, porous media model adds a momentum sink in the governing momentum equations. You can model this in two ways:

  1. Using cell zone conditions
  2. Porous jump boundary conditions, especially if our only concern is about the pressure drop.

The approach to model porous media using porous jump boundary conditions is useful when we don’t have all the necessary flow transport properties. With this approach, however, you can expect a decline in accuracy because you need to assign the boundary conditions only on the surfaces. This makes it critical for the solver to understand a sudden rise in the pressure value at the imposed location.

Modeling Porous Media using Cell Zone Conditions

Once you import your meshed model into ANSYS FLUENT, you can edit the fluid cell zone condition. Here you will find options like Frame Motion, 3D Fan Zone, Source Terms, Laminar Zone, Fixed Values and Porous Zone. By selecting the Porous Zone feature, you will find input options mainly related to Inertial and Viscous resistances and direction vectors.

Inertial and Viscous resistances are the coefficients combined with other parameters of the Hagen-Darcy’s equation. This equation calculates pressure drop across the porous zone. This zone provides the capability to model pressure drop inside the fluid volume in the axial direction. The pressure drop in this medium is contributed due to viscous and inertial resistances; we can define it as:

∆p = ∆pViscous +  ∆pInertial

where the pressure drop due to viscous resistance is given as the product of viscous resistance coefficient, thickness of the porous zone, viscosity of the fluid and the velocity of the flow. Since we provide viscosity, thickness (from the geometric model), velocity of the fluid (as calculated by the solver at the corresponding place in the domain through iterations) and the coefficient (user input values), solver calculates the pressure drop attributed due to this viscous effect loss.

Similarly, the pressure drop due to inertial losses is given as the half product of inertial resistance coefficient, square of the velocity of the fluid, thickness of the porous zone and density of the fluid. Take sufficient care while entering coefficient values into the software; sometimes the values may be given of the negative exponential order. Confusion arises because coefficient is represented as C¹= 1/K. In the software, you need to enter the value of K to accurately account for the right coefficient value.

Porous Media Flow
Cut Section View. Sample model of an inlet filtration unit for a gas turbine generator. Blue-colored components act as walls while inlet and outlet. I mounted the three series of weather hoods at the front intakes air from atmosphere through porous zone packed beds arranged beneath.

Porous Media Flow
Streamlines on a planar section colored with pressure as variable, originating from the inlet

Porous Media Flow
Total pressure drop in the planar section view. The blue colored region is due to the lack of fluid presence at that region.

Achieve Faster Convergence

Occasionally, the convergence rate slows down when the pressure drop is relatively large in the flow direction. For example, when the coefficient value of C² is large or permeability (alpha) is low, convergence rate is slower. You can resolve this by providing a good initial guess for the pressure drop across the medium. You can obtain the initial guess from two ways:

  • by performing standard initialization, or
  • by supplying an initial flow field without the effect of the porous region by temporarily disabling the porous media model.
Frequently-Asked Questions: The Top Three
  1. Direction vectors, especially for conical or cylindrical faces, are automatically calculated by ANSYS FLUENT. Engineers fail to check if the direction vectors are normal to the surface. If the direction vectors are not normal to the surface, then results will be incorrect. Be careful, there!
  2. Does every porous flow application have pressure losses due to the combination of both the viscous and inertial effects? The CADFEM’s Support Hotline gets this question quite often. The answer is no. For laminar flows, you’ll not find any inertial effects. Whereas for flows through a planar porous media (not a standard industrial use case though), you’ll not find viscous effects as well.
  3. I don’t have the either of the viscous or inertial coefficient values. With information about pressure drop across the porous zone, can I simulate the fluid flow? This one is tricky because the pressure drop is due to the combined effect of both the inertial and viscous effects. Without knowledge about the significant contribution to the pressure loss due to either effect, it’s impossible to accurately model the flow. However if you are willing to ignore one of the two effects, then you can utilize the information about pressure drop to model the flow.

It’s not difficult to model porous flow problems, however you need to right software and the right partner to guide you through the solution. Talk to us, and we’ll glad to help you!

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ANSYS License Management Made Easy!

Virtual Product Development has enabled companies to predict with confidence that their product will thrive in the real world, helping them to the solve the most complex problems which are limited only by imagination. This wouldn’t have been possible without ANSYS, the market leader in engineering simulations, that is used by many companies spanning enterprises to startups. Consequently, one of the prime goals of these product companies is license management – manage software license requirements among different teams effectively without affecting team’s productivity or asset utilization.

In this article, I will describe the new developments in ANSYS 18.0 that will make it easier for managers and license administrators to manage licenses better.

How To Get Started?

To begin with, the first step in managing the licensing resources is to track current usage of these resources. Previously, tracking and preparing the reports of ANSYS software license usage was always a tedious manual task of looking into the log files and searching for a specific license. As a result, one common question I received on CADFEM’s support hotline -“Is there a better way to track our license usage?” With the release of ANSYS 18.0, this job has eased to a certain extent.

With ANSYS 18.0, License Management Center provides the tools which help license administrators to obtain effective reports from the usage log files. Therefore, reports can be extracted about anything from current usage to peak usage and license denials in a tabular or a histogram form for a requested duration.

ANSYS License Management Center

Opening the ANSYS License Management Center will open up the license manager in the default browser.

  • Windows: Start -> Programs -> ANSYS Inc. License Manager -> ANSYS License Management Center
  • Linux:

    License Management
    License Management Center

    /ansys_inc/shared_files/licensing/start_lmcenter

New subsection has been added for reporting with 4 options. We will discuss each of these options in brief.

Current License Usage

With the View Current License Usage option, you can track current license usage. It highlights all available licenses on the server along with the maximum number of licenses. It also reports the current total license usage along with the license usage per user; different color for each user. In addition, clicking on Show Tabular Data will provide you more information about user count, user names, hostname and Start date in tabular format.

License Management
Current License Usage

Also, you can obtain similar data from Client ANSLIC_Admin Utility for older versions of ANSYS. For the manager and organization, the most important report is the licenses usage over a period of time. Next three options will help them in getting it.

License Usage History

License Management
License Usage History

This option helps in tracking the usage of license for a given period of time. Click on License Usage History and choose the duration and then click on Generate to obtain the histogram for the given duration. Once the data is generated, you get the option of monitoring the data for a specific license. Even a customized duration can be specified to track a particular license usage.

License Management
License Usage History – Specific License Type

Peak License Usage

License usage history report can be confusing at times even for experienced users. Hence if you want to track more simplified averaged peak usage per day for a given period of time, please select Peak License Usage option. By following similar steps as for License Usage History, select the time period and hit generate.

License Management
Peak License Usage

Here you will have more options for filtering out the data with respect to licenses type and months of specific interest. Along with it, you can also extract data for a complete week (24/7) or only for working days (24/5). Clicking on Show Tabular Data provides daily, weekly and monthly average of each license in a tabular form. Now, that’s going to be quite useful for the managers and licenses administrators.

License Management
Peak License Usage: Tabular Form

License Denials

Similar to Peak License Usage, the License Denials option will show the average denial of license due to insufficient licenses or for any other reason for a day for requested time duration. This helps in tracking the requirement and planning for future needs.

License Management
License Denials

Though the Reporting Tool in ANSYS doesn’t include more sophisticated options and filtering methods, it allows managers to track the license usage in many different ways without manually going through log files or investing in third-party tools.

License Management Made Easy

Thanks to ANSYS 18.0, License Management Center is even more potent and useful for you – the department heads, managers and license administrators. You can monitor license usage in real-time or historically, evaluate peak license demands and license denials. As a result, this new feature will allow you to evaluate asset utilization, manage internal license demands, forecast the need to acquire additional license among others.

There’s also a nice YouTube video that is a little more crisper than my article. It covers pretty most of the options that I have described in this article. If you are short on time, this video may help.

I would like to know if there are questions regarding license management that you’ve not been able to address so far. Maybe I can help? Hence please do use the comments section below to reach out to me. I’ll be glad to be of help.

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