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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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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Robustness Evaluation – Why Bother?

This article will explain how ANSYS optiSLang can be used for robustness evaluation in virtual product development.

A successful product. Isn’t that the goal for every product company? It begins right from the step where engineers come up with world class product innovations to getting the right marketing mix that brings commercial success. Is every product successful? No. Is every product with a great design successful? Maybe.

The Symptom

Robustness Evaluation - Why Bother?
Courtesy: Android Authority

More often than not, we find market leaders stumble with product failures. The infamous Samsung’s Note 7 will come to your mind instantly. Hundreds of users were at the forefront of dangerous incidents where phones caught fire due to short-circuiting. Samsung conducted severe internal testing and several independent investigations. They found that, in certain extreme situations, electrodes inside each battery crimped, weakened the separator between the electrodes, and caused short circuiting. In some other cases, batteries had thin separators in general, which increased the risks of separator damage and short circuiting. Economics-wise, the incident caused Samsung to recall 2.5 million devices, lose over $5 billion and damaged its reputation.

Faulty Takata airbags’ inflators contained a defect that cause some of them to explode and project shrapnel into drivers and passengers. 50+ people worldwide lost their lives due to this design failure. 70 million Takata airbag inflaters were to be recalled at a cost of $9 billion to its automaker customers. For a Tier-I supplier, this liability was so huge that they filed for bankruptcy.

Such glaring errors after product launch, with severe economic implications, aren’t limited to Samsung and Takata alone. Honda, Michelin and many more companies have been involved in product recalls due to design failures.

Obviously, such design flaws need to be mitigated. Isn’t it?

The Probable Solution

To preempt design failures, today’s engineers use state-of-the-art engineering technology. Traditionally, product development teams used extensive prototyping and testing to validate design variants during the design life cycle. Of course, this is cumbersome, expensive and time-consuming.

Over the past few decades, engineering simulations have opened up a whole new range of possibilities for the design engineers. ANSYS, Inc., the market leader for engineering simulations, provides state-of-the-art technology to simulate systems involving mechanical, fluid, electrical, electronic and semiconductor components. With added insight, design engineers are able to test a lot more design variants on a virtual platform using this technology.

Consequently, the benefits – innovation, lowered cost of product development, higher product profitability and faster time-to-market. The staggering economic benefits and tremendous value on the offer have prompted several product companies to introduce simulations upfront using a Simulation-Driven Product Development approach.

Companies like Samsung and Takata were power users of engineering simulations. They used technology extensively in their design phase and perform virtual tests to validate designs. Only validated designs were put through production, QA and then sent off to the market. Despite simulating and validating designs, these companies witnessed monumental product failure in the market that caused loss of life, led to economic losses and damage to their reputation.

If they used simulation-driven product development, what went wrong?

The Cause

While the probable solution can mitigate and even eliminate design failures, there are other forces at play that you will need evaluate carefully. Hence it is imperative to understand the root cause for occurrence of design failures despite conducting extensive state-of-the-art simulations.

Many design engineers often undermine or do not consider one important aspect due to lack of proper understanding. Variability. Just as design parameters such as thickness or physical loads can be varied to test different design variants, some parameters display inherent variability.

Let me explain it with a material parameter: Young’s Modulus. If you’re an engineer by qualification, you would’ve come across the Universal Testing Machine (UTM) in your freshman or sophomore year of college. To test the Young’s Modulus of any given material (say steel), the UTM pulls a material specimen at extreme ends to create tension. Using mathematical calculations, you’ll arrive at a number close to 210 MPa as the Young’s Modulus of mild steel. Let’s say you repeat this test for 99 other specimens of the same material. Each test result will be different and it will never be the same. Other than the odd case of a faulty UTM apparatus, there’s only one reason for that. Natural Scatter.

The Hero: Robustness Evaluation

Such variability (statistical) will lead to variability in the performance parameters of the product. Obviously this is quite important and engineers need to assess designs for variability well ahead of product launch. For variability, you have only one way to assess designs for product failure or risks: Robustness Evaluation.

Robustness Evaluation with ANSYS optiSlang

The preferred choice of tool for robustness evaluation is ANSYS optiSLang. For better understanding, there is a lot of material available in more detail. Instead of reading, you may also want to consider watching these webinars here and here.

Can you attribute lack of design robustness to any other product failures that you have witnessed? Do you have alternate views? Please let me know in the comments section.

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The Decade That Was …

In the CADFEM Journal (previously Infoplaner; in German), an announcement was made in the first issue of 2007 about the commencement of India business. This March, CADFEM Engineering Services India (CADFEM India) celebrates its 10th birthday – a decade in business. The company started out as a four person team with the vision that it could help customers in India recognize and realize the benefits of simulation-driven product development. 10 years on, the company has evolved into a confident engineering business, with over 50 colleagues, that has helped hundreds of engineers to realize their product promise.

The Decade That Was …

During this time so much has changed. The world has got smaller, faster and ever more changing. Technology has both been an enabler and a challenge to small businesses and large enterprises alike. As a responsible business, the company’s constant endeavour has been to offer customers the best-in-class solutions to their engineering problems. Today CADFEM India is proud to have gained trust from several local and global companies whose engineers rely on its products, services and know-how on a daily basis.

CADFEM India is a strong channel partner to ANSYS in India by offering the full range of physics (structural, fluids and electronics) across India. This partnership is helping CADFEM increase the rate of adoption of simulation in the country. The organization is structured towards providing and supporting customers with ANSYS software. Today the company has more than 40 engineers comprising of the core technical team, sales and marketing that engage customers in multiple areas of engineering analysis. The team is highly skilled to offer training programs for novices and experienced engineers on a plethora of engineering topics. Several customers, with origins in Germany, are long standing customers of CADFEM in India. CADFEM is the preferred simulation partner for customers owing the nature of strong and high-quality support. Deepak Joseph, the Head of Development (Truck) at Knorr-Bremse Technology Center India, and his team in Pune have been recipients of CADFEM’s technical support regularly. While thanking CADFEM for offering “extended support” to his team, Deepak recently said that CADFEM ”helped us understand ways to achieve accuracy.”

Listing of milestones of CADFEM India

All tools which are critical for success

CADFEM India offers several complementary solutions such as optiSLang (of Dynardo GmbH), Rocky DEM (particle simulations) and simulation-ready hardware. Since engineering simulation requires more than just software, CADFEM India supplies all the tools which are critical for success in simulation – all from one source. As a result, customers in India not only benefit by receiving leading software and IT-solutions, but also obtain support, consultancy and transfer of know-how. The core philosophy ingrained within every colleague is to ensure that customers realize the most return of their simulation investment. Dynardo’s CEO, Dr. Johannes Will, says “Over the last 7 years, CADFEM India has become an important partner for Dynardo to serve the optiSLang business in India as well as to support the Dynardo consulting activities. I personally enjoy that relationship and look forward to intensify the joint business success over the next years.” Since 2011, CADFEM India has organized the Indian edition of the Weimar Optimization & Stochastic Days. In 2016, over 80 attendees came together to discuss the topics of optimization and robust design for sixth year in a row.

In addition to the software business, many customers consider CADFEM India as a reliable engineering consulting partner. Several customers choose to contact CADFEM to seek simulation on demand. CADFEM India’s Managing Director, Madhukar Chatiri says that “this offers a good opportunity for us to demonstrate the power of ANSYS to the customer.” Over the years, CADFEM has solved many engineering problems in automotive, aerospace, consumer appliances, rotating machinery, watches, food & beverage and many more industries. One such example of a strong customer relationship is with Traunreut-based Bosch und Siemens Hausgeräte GmbH (BSH). For over two years from 2008, BSH worked intensively with two engineers from CADFEM India. As a result, there has been a strong partnership between BSH and CADFEM India. Speaking about this, Dan Neumayer, Head of Pre-Development at BSH said “we could have a mutual cultural understanding and a common way of thinking and working. This intensive learning forms a particularly important basis for our long-term cooperation and we see this as one fundamental success factor.”

Group Photo in the decade that was
Mrs. & Mr. Guenter Mueller while visiting CADFEM India in 2015

esocaet program starts in September 2017

One of the top most challenges for employers in India is the low number of engineers skilled with simulations. To bridge this demand-supply gap, CADFEM India has invested in ANSYS Authorized Training Centre that started in September 2015; over 50 engineers have graduated from this centre. Furthermore, CADFEM has partnered with PES University in Bangalore to bring the much-acclaimed esocaet Master Program in Applied Computational Mechanics to India. The esocaet program offers tremendous opportunities to engineers for continuous learning. The first course will begin in September 2017.

CADFEM India has been operationally profitable since many years – this has allowed the company to scale its investments in India consistently. The company has a long-term orientation, offers employees a lot of independence but functions as a responsible partner to customers. This allows the company to respond with agility to the dynamic needs of the market.

The company has geared up for the next decade of business in the Indian subcontinent. Having recognized the needs of the market, the company is betting big in the areas of Additive Manufacturing, Electronics and Digital Cities. CADFEM India has made another significant investment into the newest partner of CADFEM International – CADFEM SEA Pte. Ltd. in Singapore.

In 2016, the company was recognized as one of the 20 Most Promising Engineering & Design Solution Providers in India by the popular CIO Review magazine. Madhukar still fondly recalls the day when he formulated the vision for the Indian business in his mind. He adds “What a journey it has been for many of us! While waiting for our connecting flight at Mumbai airport, Guenter Mueller discussed the idea of a joint company in India. We thank our customers and partners for choosing to work with us. It has been and is our pleasure to serve the engineering market in India in the past decade.”

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3 Benefits of ANSYS SpaceClaim for 3D Printing

In this article, I will describe 3 benefits of ANSYS SpaceClaim Direct Modeler for 3D Printing and other applications. Specifically I will focus my attention on the Facet Tool in this article.

While searching for freely-available CAD models on GrabCAD.com, I chanced upon the challenges section because it piqued my interest. To my surprise, I found about 75% of the recent challenges to be related to topology optimization. For most of these challenges, lightweighting will yield a final design output that is optimum in weight. However such an output will be complex for traditional manufacturing processes. In the recent years, additive manufacturing or, often referred to as, 3D printing has appeared to be the manufacturing process of choice for several contemporary applications.

For topology optimization, ANSYS is the simulation tool of choice. In the latest Release 18, a significant thrust was provided to this topic. The technology is very powerful and highly-effective for lightweighting the designs. Typically, topology optimization results in the design in STL file format. In my experience, this design output is often fraught with poor facet quality and this requires cleanup by a competent tool.

Typical STL File Output of a Bracket after Topology Optimization towards 3D Printing
Typical STL File Output of a Bracket after Topology Optimization

The full suite of ANSYS Simulation Software offers not just solvers for multiple physics, but also several value added tools such as ANSYS SpaceClaim Direct Modeler (SCDM). This tool allows product companies to launch their offerings faster to market.

Now SCDM has several useful features that allow geometry manipulation and clean-up. Among many features, I found the Facet Tool to be extremely useful. After completion of topology optimization, the STL file output from ANSYS is imported into SCDM.  This Facet Tool helps in cleaning up the STL file output containing poor facet quality and helps me prepare the design for validation using ANSYS Mechanical.

For better understanding, I have included the typical workflow below.

Workflow for Topology Optimization for 3D Printing
Workflow for Topology Optimization

With this context in place, I will now introduce you to the 3 significant benefits of using ANSYS SpaceClaim Direct Modeler for 3D Printing applications.

HIPP Add-In for Reverse Engineering

HIPP is an SCDM add-in developed by ReverseEngineering.com. This tool is quite useful for engineers performing reverse engineering – with the eventual goal of producing the desired part using 3D Printing. For this case, the approach typically starts with scanning of the part desired for reverse engineering. The scan results in an STL file format created directly in SCDM; this automatic scan to STL is powered by the HIPP add-in. The Facet Tool in SCDM is then used to repair and prepare a watertight geometry.

Here’s an example of the scanned geometry of top profile of a piston rod that was generated in SCDM using the HIPP add-in. The facets in this geometry did not capture the profile accurately. Furthermore the geometry has undesired holes along with unwanted parts.

Image of a scanned geometry of a part in SCDM (using HIPP Add-In) for 3D Printing
Scanned geometry of a part in SCDM (using HIPP Add-In)

Using the Facet Tool, the repaired geometry is now ready for topology optimization and design validation before producing it using 3D Printing.

Image of the modified geometry in SCDM using Facet Tool for 3D Printing
Modified geometry in SCDM using Facet Tool

Save Resources – Faster to Market

There are numerous software tools for STL preparation, however SCDM Facet Tool has many value-adding, additional capabilities. With a very little investment, the Facet Tool provides a strong hold in combining multiple solid parts with faceted geometries in a user-friendly manner; this feature has several advantageous implications for 3D printing. Furthermore the tool is very easy and requires little knowledge for geometry repair and preparation. To prepare the bracket geometry (illustrated at the beginning of the article), it took me 10-15 minutes. See the below image. Now I found it to be fairly quick when compared to 2-3 times more using other facet modeling tools.

Image of bracket geometry modified after using SCDM Facet Tool for 3D Printing
Bracket geometry modified after using SCDM Facet Tool

Preventing Failures in 3D Printing

The Facet Tool has features to detect thickness and overhang problems before the model is sent for 3D Printing. Now these overhangs present a challenge to 3D printing without using support material. Problems such as these can be prevented by few techniques like tear-dropping, tapering among others. The effects of overhang cannot be judged immediately until you are a 3D Printing professional.

Facet Tool has a feature which detects the overhangs by providing parameters specific to 3D Printing. In particular, the thickness feature detects all geometry that is thinner than the minimum thickness specified by the printer OEM. In addition, I could understand thickness and overhangs-related problems beforehand by providing the direction of printing as well.

Other Applications

This topic is also of CADFEM’s particular interest because we invest into Digital Cities – a strategic initiative of CADFEM International that aims to simulate cities of our future. This topic is quite special and important since it involves studying the effects of disaster scenarios such as earthquake, tsunamis, pollution, crowd behavior among others.

virtualcitySYSTEMS, a CADFEM International group company, develops 3D city models using scanned data of terrains. For these city models, we use the Facet Tool to repair the geometry before performing urban simulations.

In future posts, I will delve further into using CFD and particle simulations for better modeling of 3D Printing applications.

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Electromagnetic Simulation for Antennas

In this first part of a multi-part series, I will discuss many aspects of antenna design & analysis with the underlying theme of electromagnetic simulation-driven product development. In this part, I will briefly talk about performing stand-alone Antenna Design, Analysis & Optimization using Electromagnetic Simulation.

Increasing Importance of Electromagnetic Simulation (EM)

While still in university, I imagined antenna design to be very simple. Based on the given frequency, we will need to calculate dimensions and then fabricate the design. That’s it. A decade ago, I found simulation to appear like dark art or black magic. If the fabricated antenna did not work well, I needed to iterate the physical design till it gave good results.

During the recent years, several EM simulation tools have emerged to evaluate the exact solution of Maxwell Equations for estimating the electromagnetic behavior of the devices. These tools used underlying methods like Finite Element Method (FEM), Method of Moments (MoM) and Finite Difference Time Domain (FDTD). Generally, we can divide the part components of the electronic design into active and passive devices. The modelling of the active devices is based on nonlinear measurement data parameters like S-parameters and X-parameters. When we come across modelling of passive devices, they are very simple because of their linear nature. However, it is important to understand the limitations of those devices.

The main role of the simulation is for to engineers to be able to accurately predict how complex products will behave in real-world environment enabling the complete virtual prototyping. ANSYS HFSS, a state-of-the-art high-frequency electromagnetic simulation, helps to estimate the radiation characteristics of the antenna and optimize the design as per requirement.

Parameters To Be Considered For Antenna Simulation

In general, engineers know that dimension can be reduced by increasing the substrate dielectric constant. Using standardized equations, we can estimate the size of the patch. However we cannot estimate radiation characteristics among a few other quantities. Using simulation tools, we can replace physical iterations with virtual iterations; we can identify the optimal design that matches the required specifications.

Why do some engineers get different results? Is there anything else that needs to be considered? Yes, engineers who focus only on model dimensions and not on boundaries and excitation will obtain inaccurate results.

Modeling and Setup

Let me consider the example of a GPS antenna that needs to have a gain of 3.5dB. For this gain, we’ll need to identify a antenna design with the smallest possible antenna dimension.

Let’s look at three substrates RT Duriod 5880, FR4 and Alumina. Using ANSYS HFSS, you can model the full antenna by using in-built modeling options or import the design from external CAD software. Initial dimensions of the patch are calculated using standard formulas available in academic literature.

Image of 3D CAD model of a patch antenna before performing electromagnetic simulation
Antenna Model

Patch antennas can be fed power by various methods such as microstrip line or coaxial/SMA. While using coaxial input, many don’t consider the dimensions of the coaxial. A good engineer initially checks for the dimensions of the coax in order to get the characteristic impedance, which directly affects the frequency of operation and voltage standing wave ratio or VSWR.

For assignment of different materials for model, HFSS has an inbuilt material library where you can select the required material for substrate, conductors, etc. If you want to use a material which is not in the library or if you want to add some frequency-dependent properties, then you can modify or create a new material.

Image of Materials available in HFSS before performing electromagnetic simulation
HFSS Material Library

Image showing addition/modification of materials before performing electromagnetic simulation
New Material Creation

For antenna design, radiation is another important boundary in order to accurately estimate the EM emission. As a good practice, the distance of at least λ/4 or λ/8 must be maintained between the antenna and the boundary. For example, λ/4 will be a good distance for radiation boundary and λ/8 for PML boundary. This is an important aspect that many engineers fail to consider. Upon completion of the initial setup, I ran the simulation to check for its performance.

Parameterization of Antenna

After simulation, check the input electric field in coaxial and the impedance of the transmission line/coax in order to verify the expected excitation. In post-processing, do check important parameters for radiation characteristics like pattern and gain. Even the near field data, which is complex to obtain from measurement, can be estimated with simulation.

Since we are not considering any fringing field and probe effects, there will be variation of results. To further improve the design, I suggest using optimization algorithms such as Optimetrics or ANSYS optiSLang. Such tools also permit sensitivity of the design due to fabrication tolerances.

The available optimetrics options in HFSS
Optimetrics in HFSS

Image describes the effect on resonance frequency due to probe position variations while performing electromagnetic simulation
Probe position effect on resonance frequency

Optimal Design of Antenna

Finally, the best design can be selected after evaluating the gain characteristics of the all variations. For the three substrates, I evaluated the optimized dimensions of the patch using Optimetrics:

  • 12.5 x 10 cm² for Duroid
  • 9.5 x 7.5 cm² for FR4
  • 7 x 5 cm² for Alumina

Image shows the estimated gain plot for different substrate materials while performing electromagnetic simulation
Gain Variation vs Substrate

Per this, antenna with FR4 substrate meets the required gain of 3.5dB with the least possible dimension. Better performance can be obtained by varying other parameters such as height of the substrate, etc.

The next time you perform electromagnetic simulation of antenna, do remember to consider all the boundaries.

This concludes the first part of a multi-part series on antenna design & analysis. In the next part, I will discuss about antenna placement analysis.

If you have any questions, please feel free to comment or fill out the contact form.

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Weekly Simulation Round-Up | Issue 3

Welcome back to the Weekly Simulation Round-Up. I hope you had a good start to the new year. Are your sticking to your new year resolutions so far? Either way, I hope this week’s posts provide you motivation to your resolutions.

CAE simulation opens up untold opportunities and it allows engineers to advance in many more areas of application. In this weekly simulation round-up, you will learn about CADFEM’s contribution to the world of simulation.


ANSYS: The IoT and the Economic Data

The economics of the internet, marketplace design auction theory, the statistical analysis of auction data, and the intersection of econometrics and machine learning are all interesting topics to ponder over. In this article, the author says that “there is so much uncertainty about big data that most companies have become risk-averse — so afraid to make a mistake that they fail to do anything at all. This is the single greatest error executives can make (read more).”


Virtual Test Analysis System

ViTAL is a software tool created by CADFEM on behalf of AIRBUS for the fast generation and fully nonlinear analysis of fuselage skin panel within the FE solver ANSYS. This case study says, “simulation is used to reduce the number of necessary tests, perform parametric studies and get additional information on loaded structures which are inaccessible to measuring devices. ViTAL provides boundary conditions to simulate shear pressure tests and frame bending tests performed at AIRBUS (read more).


esocaet – Master’s in Simulation

Since several years, CADFEM has been committed to transfer know-how to the community. Sharing our experiences allows us to grow the industry because better-qualified and better-equipped simulation professionals enter our industry. CADFEM offers extensive education and training, stimulates the global exchange of knowledge among simulation experts, and promotes a strategic rapport between companies and research institutes. Learn about our Master’s Program in Simulation (read more).”


Hardware & IT Solutions

We recognize that Simulation is more than Software®. To ensure that our customers derive RoI faster and in an effective manner, CADFEM offers a comprehensive range of services, from installation and configuration to maintenance and hardware support for individual components, planning, implementation, and support of entire CAE data centers. Since performance of a simulation also depends on the hardware, we offer workstations and servers that function robust to scale up your simulation speeds. Learn more about what we can do to help you! (read more)


Image showing a 3D city model and contour plot depicting the impact of a detonation inside the city model. This image is being used while discussing an article in the Weekly Simulation Round-Up.
Revolutionary Applications – Cities of The Future

We constantly work on tailoring existing solutions to meet specific customer needs and applications. City Simulations are an area of particular interest to CADFEM. While we possess the competency to address the needs of the cities, disaster risk reduction is the reason for our foray into this revolutionary application. To build cities of our future, we are at the forefront of applying simulations and building digital city twins (read more).

Thank you! Hope you liked this edition of Weekly Simulation Round-Up.

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