New 3D Design Capabilities Available in ANSYS 2019 R3 – Webinar

The ANSYS 3D Design family of products enables CAD modeling and simulation for all design engineers. Since the demands on today’s design engineer to build optimized, lighter and smarter products are greater than ever, using the appropriate design tools is more important than ever.

Rapidly explore ideas, iterate and innovate with ANSYS Discovery 3D design software, evaluate more concepts and rapidly gauge design performance through virtual design testing as you delve deeper into your design’s details, with the same results accuracy as ANSYS flagship products – when and where you need it.

Join PADT’s Training & Support Application Engineer, Robert McCathren for a look at the new 3D design capabilities available in ANSYS 2019 R3 for ANSYS Discovery AIM, Live, and SpaceClaim. These new updates include:

Mass flow outlets

Transient studies with time varying inputs

Structural beam support

Linear buckling support

Physics-aware meshing improvements

Mesh failure localization and visualization improvements

And much more

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

All Thing ANSYS 050: Updates and Enhancements in ANSYS Mechanical 2019 R3

 

Published on: November 4th, 2019
With: Eric Miller, Joe Woodward, & Ted Harris
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Specialist Mechanical Engineer/Lead Trainer Joe Woodward, and Simulation Support Manager Ted Harris, for a discussion on what’s new in the mechanical release for ANSYS 2019 R3, as well as a look at their favorite features. This includes a focus on updates and enhancements to improve ease of use, reduce set-up time, and provide more valuable solutions.

If you would like to learn more about what this release is capable of, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/376304

If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

Listen:
Subscribe:

@ANSYS #ANSYS

Property Controllers in ANSYS ACT

Customizations developed using ANSYS ACT adhere closely to the user experience that is native to Mechanical and other Workbench apps.  Obviously, this is to be expected, but sometimes it can be a little challenging to fit a particular workflow into the “tree object” plus “object properties” model.   One way to broaden the available set of user experiences from which to construct a customized behavior is to use what are known as property controllers.

Property controllers are classes, which can be implemented either in C# or Python that are associated with a given property in the details pane of a given ACT object.  These classes allow the programmer to specialize the functionality and behavior of the particular property to which they are associated.  The association between a given property and its property controller is made in the ACT XML definition file.  For this article, like most of the ones I write on ACT, I will be using C# as the implementation language.

The degree to which any given property can be specialized by a property controller is quite vast.  Therefore, I won’t be able to touch on all of the possible combinations.  However, I will demonstrate two that I have found particularly useful in various ACT apps I’ve written.

The first is a custom “select” controller that allows the user to pick one of a set of given Mechanical objects.  For most customizations, perhaps the canonical example, is a select controller that allows a user to pick a particular coordinate system out of all of the defined coordinate systems in the model.  Yes, there is a template for this that ships with Mechanical, but I will show a given implementation.  Understanding how the controller works will enable you to apply the same technique to other object types, even other ACT objects within the same app.

The second is a way to “fly out” a dialog box that can contain additional custom controls, and that is “anchored” to the side of the given property box within the details pane.  This is useful for scenarios when we can’t easily fit a particular data entry within a given property field.  Tabular data is a prime example.  Again, there are some templates for this in Mechanical, but understanding how to build it up from scratch will allow you to apply the same principles to more complex dialogs.  This second example will be covered in a subsequent blog post.

Foundations

Before we dive into the individual examples above, let’s understand some of the basics of property controllers in general.  First, how do we associate a given property controller class with a particular property.  This is accomplished by using the “class” attribute in the property tag within the XML definition.  So, here is an example from an extension XML file:

<property name="EngineAxis" 
  caption="Coordinate System" 
  control="select"                         
  class="PADT.PropertyControllers.CoordinateSystemSelectController">
  <attributes type_filter="cylindrical"/>  
</property> 

You can see that we’ve added a “class” attribute inside the property tag and set it equal to a fully qualified class name.  All other property attributes are the same as with a typical property.  In order for this to work, however, we will need to implement a class called “CoordinateSystemSelectController” in the PADT.PropertyControllers namespace.

You will also notice that there is a nested <attributes> tag inside the <property> tag.  This can be used to pass additional configuration data to the controller as we will see.  Clearly, in this case, the additional data is designed to constrain the types of coordinate systems that will be populated within the control.

Example 1: Coordinate System Select Property Controller

The method by which behavior is customized for a given property is by implementing overrides for a series of virtual functions defined on the property itself.  These virtual functions allow us to hook into various points within the properties lifetime and operation.  The names for these virtual functions correspond to the callbacks listed in the ANSYS help for the <property> tag in the ACT XML reference manual.  The names are always lowercase.  Common ones that I use are functions such as “onactivate”, “onshow”, “isvalid”,”value2string” and “getvalue”.  Except for the “value2string” function, most of these are probably self-explanatory as to when they would fire.   For this controller, I’ll demonstrate a few of these functions including when and how to use the “value2string”.

Let’s begin with the “onactivate” function.  This function is called when the user “selects” or activates the property.  So, within this function is a good place to populate the list of currently available coordinate systems.  It is tempting to cache this list so that it doesn’t have to be recomputed.  However, if the user deletes, or adds a coordinate system after we have cached the list, we would not display it as an option the next time they activated this control.  Therefore, on each activation, we build up a list of available coordinate systems.  Here is the code:

You can see the parameters to this function are parameter representing the “tree object” to which this property is a member of that object’s details pane, and a parameter representing this “property”.  The second parameter might seem counterintuitive.  You might think that we are subclassing the property itself and thus this parameter would be redundant. (i.e, it would be equivalent to the “this” object).  However, we are not subclassing the property per say, but rather implementing a controller object that property itself makes calls against to modify its own behavior.  Sounds convoluted, I understand, but my guess is that this is what allows us to specify all of this within the extension XML file.  So, it’s a good thing.

Once we get into the function proper, on line 55 we clear out all of the items within this properties associated drop down control.  Then, in lines 56-58 we figure out what are the enum constants that represent the types of coordinate systems (cylindrical, Cartesian, etc…) that we would like to present to the user.  Note, our attribute “type_filter” could contain multiple types. Then, in lines 59-67, we iterate over all of the coordinate systems current defined in the Mechanical session and pick out the ones that are of the right type.  We then add them to the “options” property of this SimProperty object.  Note, however, that we don’t add the coordinate system objects themselves, but rather string representations of the object Ids.  This is important.  The reason we don’t add the name of the coordinate system is because names (or labels) in Mechanical are not required to be unique.  You can create five coordinate systems and name all of them “Bob”, Mechanical doesn’t care and will treat them as unique.  So, we need a unique attribute of the coordinate system to store in our list of options.  The object Id is guaranteed to be unique.  So, we store this instead.

The final bit of code in this function just makes sure to default select one coordinate system if the user hasn’t already selected one.  That functionality is on lines 69-83.  If the Value property is null, then the user (or this code) has not populated the select with a given coordinate system. So, if there are any coordinate systems that are appropriate we just find the first one and select it automatically.  Note, if the user later changes this to a different CSYS and this function fires a second time, it will not overwrite their choice because the null check will fail on line 69.  The reason for this behavior is because the extension for which this code was written made extensive use of a single cylindrical coordinate system in a number of different objects.  Typically the user would add just this one coordinate system in addition to the default global Cartesian system.  So, by adding this code, the user would not be required to select this coordinate system each time they added a new object, but rather, the tool would do it for them.

The next bit of code to examine is the “value2string” function, which is shown below:

You may recall from above that the data we store in the options property was a list of string representations of the various coordinate system Ids.  Now, if we didn’t implement this function, when the user interacted with the drop down control what they would see would be a list of numbers.  They might see a list like “42” “87” and “94”.  Clearly, it’s not very intuitive as to which coordinate system these numbers may refer. 

So, what the “value2string” function allows us to do is to transform the data that the property actually stores into a visual representation that is meaningful to the user.  However, this is purely a stateless transformation.  The actual data store in the property always remains the string representation of the object’s id.  So, you can think of this function as sitting between the internal code that pulls a value out of the property, and the internal code that renders that value to the screen.  In between these two calls, we have the opportunity to transform what gets rendered.

So, essentially what we do inside this function is parse the Id string back into an integer.  If that’s cool, we then lookup the particular mechanical tree object that has this given Id.  Finally, if everything is kosher with this object, we return the name of the object we looked up.  If at any point something goes wrong, we just return an empty string.

Now, when the user interacts with the property controller, the will see a list of names corresponding to the coordinate systems of the appropriate type.  If they sadistically named all of these coordinate systems the same name, then they will see a list with multiple entries of the same name.  However, each one in the list is a unique coordinate system.  How they figure out which one is the one they actually want is now their problem…

Finally, the last function we will look at is the “getvalue” function.  As the “value2string” function made the experience of the end user more palatable, so too the “getvalue” function makes the experience of the developer more palatable.  Essentially what it does is analogous to the “value2string” function, but rather than returning a string, it returns an actual coordinate system object that can be used in other places in the system.  It looks like the following:

As you can see, it is very similar to the “value2string” object, but instead of returning a string, it returns the actual tree object itself.  Note, you have to cast the return value at the caller site to the appropriate type, but meh… it’s still nice to have.

Finally, to see this property controller in action, I’ve taken a quick screen grab of the properties pane of an ACT object I’ve implemented.  This is a little symmetry object that implements a homebrewed CPCYC, but you can see the coordinate system object.

That’s all for this post.  Next time we’ll look at how to implement the flyout feature.  Good luck with your ACT programming needs.  Oh, and if you need some help, or ever want to have some ANSYS customization done for you, let us know.  We do all sorts of customization work from more run of the mill type

Updates & Additions in ANSYS Mechanical 2019 R3 – Webinar

With ANSYS structural analysis software, users are able to solve more complex engineering problems, faster and more efficiently than ever before. Customization and automation of structural solutions is much easier to optimize thanks to new and innovative finite element analysis (FEA) tools available in this product suite. 

From designers and occasional users looking for quick, easy and accurate results, to experts looking to model complex materials, large assemblies and nonlinear behavior, ANSYS has you covered. The intuitive interface of ANSYS Mechanical enables engineers of all levels to get answers fast and with confidence.

Join PADT’s Specialist Mechanical Engineer Joe Woodward, for an in-depth look at what’s new in the latest version of ANSYS Mechanical, including updates regarding:

  • Software User Interface
  • Design Elements
  • Composites
  • Acoustics
  • External Modeling
  • And much more

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

All Things ANSYS 049: Predicting & Controlling Environmental Pollution with ANSYS Simulation

 

Published on: October 21st, 2019
With: Eric Miller & Clinton Smith
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s CFD Team Lead Engineer Clinton Smith for a discussion on how ANSYS fluids tools are being used to help predict and control environmental pollution. This information is helping engineers in a variety of ways, such as understanding the formation and dispersion of pollutants such as NOx, SOx, CO and soot.

If you would like to learn more about what this application is capable of, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/374571

If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

Listen:
Subscribe:

@ANSYS #ANSYS

Predicting & Controlling Environmental Pollution with ANSYS Simulation – Webinar

Environmental pollution has been a fact of life for many centuries, though it became a real issue after the start of the industrial revolution. An estimated 6.5 million premature deaths have been linked to air pollution every year.

In order to properly combat this growing issue, the world’s leading minds have turned to a more effective tool for environmental analysis; numerical simulation. Computational fluid dynamics has proven to be a powerful tool when it comes to predicting and controlling air, water, and noise pollution.

Join PADT’s CFD Team Lead Engineer Clinton Smith to learn how ANSYS fluid mechanics solutions provide insight and detailed understanding of the formation and dispersion of pollutants such as NOx, SOx, CO & Soot as well as effective ways for modelling pollution control equipment such as ESP’s, bag filters, and wastewater treatment plants.

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

ANSYS Mechanical – Overcoming Convergence Difficulties with the Semi-Implicit Method

In our last blog, we discussed using Nonlinear Adaptive Region to overcome convergence difficulties by having the solver automatically trigger a remesh when elements have become excessively distorted.  You can read it here:  http://www.padtinc.com/blog/ansys-mechanical-overcoming-convergence-difficulties-with-automatic-remeshing-nonlinear-adaptive-region/

This time we look at another tool for overcoming convergence difficulties, the Semi-Implicit method.  ANSYS, Inc. describes the semi-implicit method as a hybrid, combining features of both implicit and explicit finite element methods.

In highly nonlinear problems involving significant deformations we may get a solver error like this one: 

*** ERROR ***                           CP =   18110.688   TIME= 11:58:42
Solution not converged at time 0.921 (load step 1 substep 185).           Run terminated. 

Like it does with other problems that lead to convergence failures, the Solution branch will have telltale red lightning bolts, indicating the solution was not able to complete due to nonconvergence.

In this case, it can be difficult to determine from the error message in the solution output exactly what the problem is.  Plotting the Newton-Raphson residuals can be a good starting point.  In order to plot the Newton-Raphson residuals, though, we need to turn them on prior to solving.  See this older Focus blog for instructions on how to do that:

http://www.padtinc.com/blog/overcoming-convergence-difficulties-in-ansys-workbench-mechanical-part-i-using-newton-raphson-residual-information/

A plot of the Newton-Raphson residuals shows us where the highest force imbalance is in the model:

That’s a nice looking plot, but doesn’t tell us much without knowing more about the simulation.  The model is of a plastic bottle, subject to a force load tending to ‘crush’ the bottle from top to bottom.  There is a slight off center load as well, so that the force is not purely in the downward direction. 

The bottle is constrained with a fixed support on the bottom flat surface, and contact elements between the outer surface of the bottle and a fixed surface representing a table or floor.  This is to prevent the bottle from deflecting below the plane of that surface.

The material used is a polyethylene plastic, from the ANSYS Granta Materials Data for Simulation add-on, which is a great tool to get access to hundreds of materials for ANSYS simulations.  The geometry of the bottle was created in SpaceClaim as a surface body and meshed with shell elements in ANSYS Mechanical. 

The solution was run as nonlinear static, with large deflection effects turned on.  Automatic Time Stepping was manually activated with a starting and minimum number of substeps set to 200 and a maximum number of substeps set to 1000.

With these settings, the solution ran to about 92% of the full load, where it failed to solve after bisecting to the maximum number of substeps (minimum ‘time’ step).  The force convergence plots showed the bisections and failed convergence attempts started at about iteration 230 and ‘time’ 0.92.  (If you are not familiar with the convergence plots from a Newton-Raphson method solution, please see our Focus archives for an article on the topic – look for the link to the GST Plot:  http://www.padtinc.com/blog/wp-content/uploads/oldblog/PADT_TheFocus_08.pdf).

Even though our solution has not converged, it is probably helpful to view the deformation results for substeps which did converge (at partial load) as well as the unconverged results which will be written as the last set of results.

This plot shows the total deformation at the last converged substep (time value 0.92):

This plot shows the unconverged solution, ‘extrapolated’ to time 1.0:

From the unconverged deformation plot we can see that the top of the bottle is tending to experience very large deformations.  It’s not surprizing that convergence difficulties are being encountered.

One of the techniques we can utilize to get past this problem is the Semi-Implicit method in ANSYS Mechanical.  As of 2019 R2, this needs to be activated using a Mechanical APDL command object, but it can be as simple as adding a single word within the Static Structural branch:

SEMIIMPLICIT

There are some optional fields on that command, but minimally just the one word command is needed.

Once the semi-implicit method is activated, if the solver detects the default implicit solver is having trouble, it automatically switches to the semi-implicit solving scheme.  Like a traditional explicit solver, the semi-implicit method can better handle very large deformation, transitory-like effects.  The method can switch back to implicit if conditions warrant for a more efficient solution and in fact can switch back and forth between the two schemes.

The solver output will tell us if the semi-implicit scheme has been activated:

EQUIL ITER  26 COMPLETED.  NEW TRIANG MATRIX.  MAX DOF INC=  0.9526   

     NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO  FILE: file.nd004

     DISP CONVERGENCE VALUE   =  0.3918      CRITERION=   1.448     <<< CONVERGED

     LINE SEARCH PARAMETER =  0.4113     SCALED MAX DOF INC =  0.3918   

     FORCE CONVERGENCE VALUE  =   44.44      CRITERION=  0.9960   

     MOMENT CONVERGENCE VALUE =   3.263      CRITERION=  0.1423   

    Writing NEWTON-RAPHSON residual forces to file: file.nr001

    >>> TRANSITIONING TO SEMI-IMPLICIT METHOD

     NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO  FILE: file.nd001


    EQUIL ITER   1 COMPLETED.  NEW TRIANG MATRIX.  MAX DOF INC=  0.8788E-04

     NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO  FILE: file.nd002

 *** LOAD STEP     1   SUBSTEP   185  COMPLETED.    CUM ITER =    284

 *** TIME =  0.920010         TIME INC =  0.100000E-04

    Kinetic Energy = 0.2157        Potential Energy =  60.59   

 *** AUTO STEP TIME:  NEXT TIME INC = 0.10000E-04  UNCHANGED

     NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO  FILE: file.nd003

There are some ‘symptoms’ of the switch from implicit to explicit.  The most obvious is probably that the force convergence plot will stop updating. 

Changing the Solution Output to the Solver Output will show the explicit scheme being used in that case.  The telltale is the information on Response Frequency and Period (the example shown is a static structural solution).

Deformation plot trackers and contact trackers continue to work as expected during the solution, however.

Using the semi-implicit method, the solution was able to successfully converge to the full load, and converged results are available at the last time point:

We also used the new keyframe animation technique to animate the results time history.

The semi-implicit method is well documented within the Mechanical APDL 2019 R2 Help, in the Advanced Analysis Guide, chapter 3 on Semi-Implicit Method.  We suggest reviewing that information to get a much better handle on the technique.

We hope this is helpful in getting your nonlinear solutions to converge the full value of applied loads.

All Things ANSYS 048: Topology Optimization & Simulation for Additive Manufacturing in ANSYS 2019 R3

 

Published on: October 7th, 2019
With: Eric Miller & Doug Oatis
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s simulation support & application engineer Doug Oatis for a discussion on what is new in ANSYS 2019 R3 with regards to tools and applications for topology optimization and additive manufacturing.

If you would like to learn more about what’s new in this latest release, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/372133?

If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

Listen:
Subscribe:

@ANSYS #ANSYS

Frequency Dependent Material Definition in ANSYS HFSS

Electromagnetic models, especially those covering a frequency bandwidth, require a frequency dependent definition of dielectric materials. Material definitions in ANSYS Electronics Desktop can include frequency dependent curves for use in tools such as HFSS and Q3D. However, there are 5 different models to choose from, so you may be asking: What’s the difference?

In this blog, I will cover each of the options in detail. At the end, I will also show how to activate the automatic setting for applying a frequency dependent model that satisfies the Kramers-Kronig conditions for causality and requires a single frequency definition.

Background

Recalling that the dielectric properties of material are coming from the material’s polarization

(1)

where D is the electric flux density, E is the electric field intensity, and P is the polarization vector. The material polarization can be written as the convolution of a general dielectric response (pGDR) and the electric field intensity.

(2)

The dielectric polarization spectrum is characterized by three dispersion relaxation regions α, β, and γ for low (Hz), medium (KHz to MHz) and high frequencies (GHz and above). For example, in the case of human tissue, tissue permittivity increases and effective conductivity decreases with the increase in frequency [1].

Fig. 1. α, β and γ regions of dielectric permittivity

Each of these regions can be modeled with a relaxation time constant

(3)

where τ is the relaxation time.

(4)

The well-known Debye expression can be found by use of spectral representation of complex permittivity (ε(ω)) and it is given as:

(5)
(6)

where ε is the permittivity at frequencies where ωτ>>1, εs is the permittivity at ωτ>>1, and j2=-1. The magnitude of the dispersion is ∆ε = εs.

The multiple pole Debye dispersion equation has also been used to characterize dispersive dielectric properties [2]

(7)

In particular, the complexity of the structure and composition of biological materials may cause that each dispersion region be broadened by multiple combinations. In that case a distribution parameter is introduced and the Debye model is modified to what is known as Cole-Cole model

(8)

where αn, the distribution parameter, is a measure of broadening of the dispersion.

Gabriel et. al [3] measured a number of human tissues in the range of 10 Hz – 100 GHz at the body temperature (37℃). This data is freely available to the public by IFAC [4].

Frequency Dependent Material Definition in HFSS and Q3D

In HFSS you can assign conductivity either directly as bulk conductivity, or as a loss tangent. This provides flexibility, but you should only provide the loss once. The solver uses the loss values just as they are entered.

To define a user-defined material choose Tools->Edit Libraries->Materials (Fig. 2). In Edit Libraries window either find your material from the library or choose “Add Material”.

Fig. 2. Edit Libraries screen shot.

To add frequency dependence information, choose “Set Frequency Dependency” from the “View/Edit Material” window, this will open “Frequency Dependent Material Setup Option” that provides five different ways of defining materials properties (Fig. 3).

Fig. 3. (Left) View/Edit Material window, (Right) Frequency Dependent Material Setup Option.

Before choosing a method of defining the material please note [5]:

  • The Piecewise Linear and Frequency Dependent Data Points models apply to both the electric and magnetic properties of the material. However, they do not guarantee that the material satisfies causality conditions, and so they should only be used for frequency-domain applications.
  • The Debye, Multipole Debye and Djordjevic-Sarkar models apply only to the electrical properties of dielectric materials. These models satisfy the Kramers-Kronig conditions for causality, and so are preferred for applications (such as TDR or Full-Wave SPICE) where time-domain results are needed. They also include an automatic Djordjevic-Sarkar model to ensure causal solutions when solving frequency sweeps for simple constant material properties.
  • HFSS and Q3D can interpolate the property’s values at the desired frequencies during solution generation.

Piecewise Linear

This option is the simplest way to define frequency dependence. It divides the frequency band into three regions. Therefore, two frequencies are needed as input. Lower Frequency and Upper Frequency, and for each frequency Relative Permittivity, Relative Permeability, Dielectric Loss Tangent, and Magnetic Loss Tangent are entered as the input. Between these corner frequencies, both HFSS and Q3D linearly interpolate the material properties; above and below the corner frequencies, HFSS and Q3D extrapolate the property values as constants (Fig. 4).

Fig. 4. Piecewise Linear Frequency Dependent Material Input window.

Once these values are entered, 4 different data sets are created ($ds_epsr1, $ds_mur1, $ds_tande1, $ds_tandm1). These data sets now can be edited. To do so choose Project ->Data sets, and choose the data set you like to edit and click Edit (Fig. 5). This data set can be modified with additional points if desired (Fig. 6).

Fig. 5. (Left) Project data set selection, (right) defined data set for the material.
Fig. 6. A sample data set.

Frequency Dependent

Frequency Dependent material definition is similar to Piecewise Linear method, with one difference. After selecting this option, Enter Frequency Dependent Data Point opens that gives the user the option to use which material property is defined as a dataset, and for each one of them a dataset should be defined. The datasets can be defined ahead of time or on-the-fly. Any number of data points may be entered. There is also the option of importing or editing frequency dependent data sets for each material property (Fig. 7).

Fig. 7. This window provides options of choosing which material property is frequency dependent and enter the data set associated with it.

Djordjevic-Sarkar

This model was developed initially for FR-4, commonly used in printed circuit boards and packages [6]. In fact, it uses an infinite distribution of poles to model the frequency response, and in particular the nearly constant loss tangent, of these materials.

(9)

where ε is the permittivity at very high frequency,  is the conductivity at low (DC) frequency,  j2=-1, ωA is the lower angular frequency (below this frequency permittivity approaches its DC value), ωB is the upper angular frequency (above this frequency permittivity quickly approaches its high-frequency permittivity). The magnitude of the dispersion is ∆ε = εs-ε∞.

Both HFSS and Q3D allow the user to enter the relative permittivity and loss tangent at a single measurement frequency. The relative permittivity and conductivity at DC may optionally be entered. Writing permittivity in the form of complex permittivity [7]

(10)
(11)

Therefore, at the measurement frequency one can separate real and imaginary parts

(12)
(13)

where

(14)

Therefore, the parameters of Djordjevic-Sarkar can be extracted, if the DC conductivity is known

(15)

If DC conductivity is not known, then a heuristic approximation is De = 10 εtan δ1.

The window shown in Fig. 8 is to enter the measurement values.

Fig. 8. The required values to calculate permittivity using Djordjevic-Sarkar model.

Debye Model

As explained in the background section single pole Debye model is a good approximation of lossy dispersive dielectric materials within a limited range of frequency. In some materials, up to about a 10 GHz limit, ion and dipole polarization dominate and a single pole Debye model is adequate.

(16)
(17)
(18)
(19)
(20)

The Debye parameters can be calculated from the two measurements [7]

(21)

Both HFSS and Q3D allow you to specify upper and lower measurement frequencies, and the loss tangent and relative permittivity values at these frequencies. You may optionally enter the permittivity at high frequency, the DC conductivity, and a constant relative permeability (Fig. 9).

Fig. 9. The required values for Single Pole Debye model.

Multipole Debye Model

For Multipole Debye Model multiple frequency measurements are required. The input window provides entry points for the data of relative permittivity and loss tangent versus frequency. Based on this data the software dynamically generates frequency dependent expressions for relative permittivity and loss tangent through the Multipole Debye Model. The input dialog plots these expressions together with your input data through the linear interpolations (Fig. 10).

Fig. 10. The required values for Multipole Debye model.

Cole Cole Material Model

The Cole Cole Model is not an option in the material definition, however, it is possible to generate the frequency dependent datasets and use Frequency Dependent option to upload these values. In fact ANSYS Human Body Models are built based on the data from IFAC database and Frequency Dependent option.

Visualization

Frequency-dependent properties can be plotted in a few different ways. In View/Edit Material dialog right-click and choose View Property vs. Frequency. In addition, the dialogs for each of the frequency dependent material setup options contain plots displaying frequency dependence of the properties.

You can also double-click the material property name to view the plot.

Automatically use causal materials

As mentioned at the beginning, there is a simple automatic method for applying a frequency dependent model in HFSS. Select the menu item HFSS->Design Setting, and check the box next to Automatically use casual materials under Lossy Dielectrics tab.

Fig. 11. Causal material can be enforced in HFSS Design Settings.

This option will automatically apply the Djordjevic-Sarkar model described above to objects with constant material permittivity greater than 1 and dielectric loss tangent greater than 0. Keep in mind, not only is this feature simple to use, but the Djordjevic-Sarkar model satisfies the Kramers-Kronig conditions for causality which is particularly preferred for wideband applications and where time-domain results will also be needed. Please note that if the assigned material is already frequency dependent, automatic creation of frequency dependent lossy materials is ignored.

If you would like more information or have any questions about ANSYS products please email info@padtinc.com

References

  • D.T. Price, MEMS and electrical impedance spectroscopy (EIS) for non-invasive measurement of cells, in MEMS for Biomedical Applications, 2012, https://www.sciencedirect.com/topics/materials-science/electrical-impedance
  • W. D. Hurt, “Multiterm Debye dispersion relations for permittivity of muscle,” IEEE Trans. Biomed. Eng, vol. 32, pp. 60-64, 1985.
  • S. Gabriel, R. W. Lau, and C. Gabriel. “The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues.” Physics in Medicine & Biology, vol. 41, no. 11, pp. 2271, 1996.
  • Dielectric Properties of Body Tissues in the Frequency Range 10 Hz – 100 GHz, http://niremf.ifac.cnr.it/tissprop/.
  • ANSYS HFSS Online Help, Nov. 2013, Assigning Materials.
  • A. R. Djordjevic, R. D. Biljic, V. D. Likar-Smiljani, and T. K. Sarkar, “Wideband frequency-domain characterization of FR-4 and time-domain causality,” IEEE Trans. on Electromagnetic Compatibility, vol. 43, no. 4, p. 662-667, Nov. 2001.
  • ANSYS HFSS Online Help, 2019, Materials Technical Notes.

Useful Links

Piecewise Linear Input

Debye Model Input

Multipole Debye Model Input

Djordjevic-Sarkar

Enter Frequency Dependent Data Points

Modifying Datasets.

Topology Optimization & Simulation for Additive Manufacturing in ANSYS 2019 R3 – Webinar

ANSYS offers a complete simulation workflow for additive manufacturing (AM) that allows you to transition your R&D efforts for metal additive manufacturing into a successful manufacturing operation. This best-in-class solution for additive manufacturing enables simulation at every step in your AM process. It will help you optimize material configurations and machine and parts setup before you begin to print. As a result, you’ll greatly reduce — and potentially eliminate — the physical process of trial-and- error testing.

Through the use of ANSYS tools such as Additive Prep, Print, and Science, paired with topology optimization capabilities in ANSYS Mechanical Workbench, the need for physical process of trial-and-error testing has been greatly reduced.

Join PADT’s Simulation Support and Application Engineer Doug Oatis for an exploration of the ANSYS tools that help to optimize additive manufacturing, and what new capabilities are available for them when upgrading to ANSYS 2019 R3. This presentation includes updates regarding:

  • Level-set based topology optimization
  • The export of build files directly to AM machines
  • Switching between viewing STL supports, mesh, or element densities
  • Multiple support being made in a single simulation (volume-less & solid supports)
  • And much more

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

All Things ANSYS 047: Mechanical Solver, Element, & Contact Enhancements in ANSYS 2019 R3

 

Published on: September 24th, 2019
With: Eric Miller, Joe Woodward, Doug Oatis, & Ted Harris
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s simulation support manager Ted Harris, specialist mechanical engineer Joe Woodward, and simulation support & application engineer Doug Oatis for a discussion on what is new in ANSYS 2019 R3 with regards to the mechanical solver, element, and contact enhancements.

If you would like to learn more about what’s new in this latest mechanical release, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/371263

If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

Listen:
Subscribe:

@ANSYS #ANSYS

Mechanical Solver, Element, & Contact Enhancements in ANSYS 2019 R3 – Webinar

ANSYS 2019 R3 brings a whole host of improvements to various mechanical features, designed to enhance overall optimization and ease of use. Key updates such as those made in regards to the mechanical solver, MAPDL elements, and contact modeling capabilities help make this release essential for performing effective analyses, and deriving valuable results from said analyses. 

For example, being able to simulate contact correctly means that engineers can simulate the change in load paths when parts deform and confidently predict how assemblies will behave in the real world.

Join PADT’s Simulation Support Manager Ted Harris, for a look at the latest mechanical solver, element, and contact updates available in ANSYS 2019 R3. This presentation includes enhancements made for:

Improved scaling for various solvers

Surface stress evaluation for axisymmetric solid elements

Piezoelectric analyses

Nonlinear radial gap elements

And much more

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

All Things ANSYS 046: The Founding of CFX

 

Published on: September 9th, 2019
With: Eric Miller, Paul Galpin, & Brad Hutchinson
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by Paul Galpin and Brad Hutchinson, two founders of the Computational Fluid Dynamics (CFD) simulation tool now owned by ANSYS, called CFX. They discuss how they initially got into the world of simulation, the current state of CFD, and what is important to be aware of as it continues to grow and develop.

If you would like to learn more about what’s new in the latest version of CFX, check out PADT’s webinar on fluids updates in ANSYS 2019 R3 here: https://www.brighttalk.com/webcast/15747/369903

If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

Listen:
Subscribe:

@ANSYS #ANSYS

Fluids Innovations in ANSYS 2019 R3 – Webinar

Products such as ANSYS Fluent, CFX, and Ensight work together in a constantly improving tool kit that is developed to provide ease of use improvements for engineers simulating fluid flows and the impact those flows have on physical models. 

Fluids simulation users will find that ANSYS 2019 R3 includes many enhancements that further simplify the user experience and broaden use to new applications. The new Fluent experience has been improved so you can enjoy more CFD in less time, with less training.

Join PADT’s Simulation Support and Application Engineer, Sina Ghods, for a look at what is new and improved for fluids simulation tools in ANSYS 2019 R3. This presentation includes updates regarding: 

  • Usability Enhancements
  • Watertight Geometry Workflow
  • TurboGrid & BladeEditor
  • Meshing Enhancements
  • And many more innovative capabilities

Register Here

If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).

You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!

Video Interview: Topology Optimization versus Generative Design

While attending the 2019 RAPID + TCT conference in Detroit this year, I was honored to be interviewed by Stephanie Hendrixson, the Senior Editor of Additive Manufacturing magazine and website. We had a great chat, covering a lot of topics. I do tend to go on, so it turned into two videos.

The first video is about the use of simulation in AM. You should watch that one first, here, because we refer back to some of the basics when we zoomed in on optimization.

Generative design is the use of a variety of tools to drive the design of components and systems to directly meet requirements. One of those tools, the most commonly used, is Topological Optimization. Stephanie and I explore what it is all about, and the power of using these technologies, in this video:

You can view the full article on the Additive Manufacturing website here.

If you have any questions about how you can leverage simulation to add value to your AM processes, contact PADT or shoot me an email at eric.miller@padtinc.com.