Simulation has been and continues to be a powerful tool for helping to drive innovation in the medical industry. Everything from medical devices, to hospital equipment, and even pharmaceutical and clinical practices can benefit from the introduction of simulation technology. This is true now more than ever, as the we all are facing such turbulent times.
During the COVID-19 pandemic, Ansys is striving to combat the spread of the coronavirus, by backing the ongoing initiatives of customers and partners working in the medical sphere. In order to support healthcare professionals, policy makers, and communities around the world in this endeavor, Ansys is sharing key insights gained from their own analyse, along with those of partners and other collaborators, regarding how to prevent future spread, and treat those already effected by the virus.
Join PADT’s Co-founder and Principal engineer Eric Miller, along with Marc Horner, Principal Healthcare Engineer at Ansys, for a discussion on what the company is doing to combat the virus, as well as a look at some models that effectively illustrate how the tools are being used.
Ansys Polyflow is a Finite Element CFD solver with unique capabilities that enable simulation of complex non-Newtonian flows seen in the polymer processing industry. In recent releases, Polyflow has included templates to streamline two of its most common use cases: blow molding and extrusion. Similarly, Ansys Discovery AIM offers a modern user interface that guides users through blow molding and extrusion workflows while still using the proven Polyflow solver under the hood. It is not uncommon for engineers to be unsure about which tool to pursue for their specific application. In this article, I will focus on the blow molding workflow. More specifically, I will point out three features in Polyflow that have not yet been incorporated into Discovery AIM:
The PolyMat curve fitting tool to derive
viscoelasticity model input parameters from test data
Automatic parison thickness mapping onto an
Ansys Mechanical shell mesh
Parison Programming to optimize parison
thickness subject to final part thickness constraints
Keep in mind that either tool will get the job done in most
applications, so let us first quickly review some of the core features of blow
molding simulations that are common to Polyflow and AIM:
Parison/Mold contact detection
3-D Shell Lagrangian automatic remeshing
Generalized Newtonian viscosity models
Temperature dependent and multi-mode integral viscoelastic
models
Time dependent mold pressure boundary conditions
Isothermal
or non-isothermal conditions
For demonstration purposes, I modeled a sweet submarine toy
in SpaceClaim. Unfortunately, I think it will float, but let’s move past that
for now.
Figure 1: Final Submarine shape (Left), Top View of Mold+ Parison (Top Left), Side View of Mold+Parison (Bottom Right)
At this point, you could
proceed with Discovery AIM or with Polyflow without any re-work. I’lll proceed
with the Polyflow Blow Molding workflow to point out the features currently
only available in Polyflow.
PolyMat Curve Fitting Tool
With the blow molding template, you can select whether to treat the parison as isothermal or non-isothermal and whether to model it as general Newtonian or viscoelastic. Suppose we would like to model viscoelasticity with the KBKZ integral viscoelastic model because we were interested in capturing strain hardening as the parison is stretched. The inputs to the KBKZ model are viscosity and relaxation times for each mode. If they are known, the user can input the values directly. This is possible in Discovery AIM as well. However, the PolyMat tool is unique to Polyflow. PolyMat is a built-in curve fitting tool that helps generate input parameters for the various viscosity model available in Polyflow using material data. This is particularly useful when you do not explicitly have the inputs for a viscoelastic model, but perhaps you have other test data such as oscillatory and capillary rheometry data. In this case I have with the loss modulus, storage modulus and shear viscosity for a generic high density polyethylene (HDPE) material. For this material, four modes are enough to anchor the KBKZ model to the data as shown below. We can then load the viscosity/relaxation time into Polyflow and continue.
Figure 2: Curve Fitting of G’(Ω),G’’(Ω),η() [Left], KBKZ Viscoelastic Model inputs (Right)
The main output of the simulation is the final parison
thickness distribution. For this sweet submarine, the initial parison thickness
is set to 3mm and the final thickness distribution is shown in the contour plot
below.
Figure 3a: Animation of blow molding process
Figure 3b: Final Part Thickness Distribution
Thickness Mapping to Ansys Mechanical
The second Polyflow capability I’d like to point out is the
ability to easily map the thickness distribution onto an Ansys mechanical shell
mesh. You can map the thickness onto an Ansys Mechanical shell mesh by
connecting the polyflow solution component to a structural model in workbench
as shown below. The analogous work flow in AIM, would be to create a second
simulation for the structural analysis, but you would be confined to specifying
a constant thickness.
In Ansys Mechanical, the mapping comes through within the
geometry tree as shown below. The imported Data Transfer Summary is a good way
to ensure the mapping behaves as expected. In this case we can see that 100% of
the nodes were mapped and the thickness contours qualitatively match the
Polyflow results in CFD -Post.
Figure 5: Imported Thickness in Ansys Mechanical
Figure 6: Thickness Data Transfer Summary
A force is applied normal to front face of the sail and simulated in Mechanical. The peak stress and deformation are shown below. The predicted stresses are likely acceptable for a toy, especially since my toy is a sweet submarine. Nonetheless, suppose that I was interested in reducing the deformation in the sail under this load condition by thickening the extruded parison. A logical approach would be to increase the initial parison thickness from 3mm to 4mm for example. Polyflow’s parison programming feature takes the guesswork out of the process.
Figure 7: Clockwise from Top Left: Applied Load on Sail, Stress Distribution, total Deformation, Thickness Distribution
Parison Programming
Parison programming is an iterative optimization work flow
within Polyflow for determining the extruded thickness distribution required to
meet the final part thickness constraints. To activate it, you create a new post
processor sub-task of type parison programming.
Figure 8: Parison Programming Setup
The inputs to the optimization are straight forward. The only inputs that you typically would need to modify are the direction of optimization, width of stripes, and list of (X,h) pairs. The direction of optimization is the direction of extrusion which is X in this case. If the extruder can vary parison thickness along “stripes” of the parison, then Polyflow can optimize each stripe thickness. The list of (X,h) pairs serves as a list of constraints for the final part thickness where X is the location on the parison along the direction of extrusion and h is the final part thickness constraint.
Figure 9: Thickness Constraints for Parison Programming
In our scenario, the X,h pairs form a piecewise linear thickness distribution to constrain the area around the sail to have a 3.5mm thickness and 2mm everywhere else. After the simulation, Polyflow will write a csv file with to the output directory containing the initial thickness for each node for the next iteration. You will need to copy over the csv file from the output directory of iteration N to the input directory of iteration N+1. The good news is the optimization converges within 3-5 iterations.
Figure 10: Defining the Initial Thickness for the Next Parison Programming Iteration
Polyflow will print the parison strip thickness distribution
for the next iteration in the .lst file. The plot below shows the thickness
distribution from the first 3 iterations. Note from the charts below that the
distribution converged by iteration 2; thus iteration 3 was not actually
simulated. The optimized parison thickness distribution is also plotted in the
contour plot below.
Figure 11: Optimized Parison Thickness (Top), Final Part Thickness (Bottom)
Figure 12:
% of Elements At or Above Thickness Criteria
As a final check, we can evaluate how the modification to the parison thickness reduced the deformation of the submarine. The total deformation contour plot below confirms that the peak deformation decreased from 2mm to 0.8mm.
Figure 13: Total Deformation in Ansys Mechanical After Parison Programming
Summary
Ansys Discovery AIM is a versatile platform with an intuitive and modern user interface. While Aim has incorporated most of the blow molding simulation capabilities from Polyflow, some advanced functionality has not yet been brought into AIM. This article simulated the blow molding process of a toy submarine to demonstrate three capabilities currently only available in Polyflow: the PolyMat curve fitting tool, automatic parison thickness mapping to Ansys Mechanical, and parison programming. Engineers should consider whether any of these capabilities are needed in their application next time they are faced with the decision to create a blow mold simulation using Ansys Discovery AIM or Polyflow.
In this episode your host and Co-Founder of PADT, Eric Miller is joined by two leaders in the Ansys response to COVID-19 – Thierry Marchal, Global Industry Director for Healthcare, Consumer Products & Construction, and Marc Horner, Principal Healthcare Engineer – for a discussion on what the company is doing to combat the spread of the virus, as well as give our listeners a more complete understanding regarding the specific applications that Ansys tools are being used for during this global pandemic.
If you would like to learn more about Ansys and their response to COVID-19, check out the following link: https://bit.ly/36bs8rR
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!
In 2017 Colorado based company Ursa Major Technologies put together an expert team of designers and engineers to realize its vision of providing the microsatellite industry with the best rocket engines in the business. Utilizing Ansys simulation software, additive manufacturing, and modernizing staged combustion, the company successfully designed and built two liquid oxygen and kerosene engines and has a third engine in development.
With Ansys, Ursa Major Technologies is accomplishing design goals faster and more efficiently than ever before. Using Finite Element Analysis (FEA), the company can run models with 30-40 unique parts to analyze entire turbo pumps in one simulation. Thrust analysis, which the company had previously done with 2D models, can now be done all in the Ansys CFX tool more cost-effectively.
JoinPADT and Ursa Major Technologies for a brief overview of applications for Ansys in the aerospace industry, followed by an exploration of how they are using these simulation tools to better design and optimize the next generation of rocket engines.
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In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Pam Waterman and Robert McCathren for a discussion on how Ansys Granta can be used to help optimize hardware selection for additive manufacturing. The Senvol Database details 1,000 AM machines and more than 850 compatible materials. Using this tool within Granta Selector, you can search and compare materials based on properties, type, or compatible machines.
If you would like to learn more about the Ansys tool and it’s applications for additive, check out our webinar on the topic here: https://bit.ly/2SAZN8G
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!
There are hundreds of industrial AM machines and materials. New products come to market weekly, and picking the best option for a manufacturing or research project is a tough call. A wrong direction can be costly. This is where Ansys Granta and the Senvol Database come in handy.
The Senvol Database details 1,000 AM machines and more than 850 compatible materials. Using this tool within Granta Selector, you can search and compare materials based on properties, type, or compatible machines. Identify and compare machines based on supported processes, manufacturer, required part size, cost, or compatible materials (and their properties). Quickly focus on the most likely routes to achieve project goals, save time and get new ideas as you research AM options.
JoinPADT’s Application Engineer Robert McCathren for an overview of Ganta Material Selector, along with its importance and applications for those working with or interested in additive manufacturing.
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!
In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior Simulation Support & Application Engineer Sina Ghods for a look at what is new with Ansys Cloud and how the tool provides access to higher fidelity models, faster turnaround, and multiple supported solvers, anywhere and anytime.
If you would like to learn more about the Ansys tool offering access to simulation on the go, check out our webinar on the topic here: https://bit.ly/3al5PjH
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!
In this article I will cover a Voltage Drop (DC IR) simulation in SIwave, applying realistic power delivery setup on a simple 4-layer PCB design. The main goal for this project is to understand what data we receive by running DC IR simulation, how to verify it, and what is the best way of using it.
And before I open my tools and start diving deep into this topic, I would like to thank Zachary Donathan for asking the right questions and having deep meaningful technical discussions with me on some related subjects. He may not have known, but he was helping me to shape up this article in my head!
Design Setup
There are many different power nets present on the board
under test, however I will be focusing on two widely spread nets +1.2V and
+3.3V. Both nets are being supplied through Voltage Regulator Module (VRM),
which will be assigned as a Voltage Source in our analysis. After careful
assessment of the board design, I identified the most critical components for
the power delivery to include in the analysis as Current Sources (also known as
‘sinks’). Two DRAM small outline integrated circuit (SOIC) components D1 and D2
are supplied with +1.2V. While power net +3.3V provides voltage to two quad
flat package (QFP) microcontrollers U20 and U21, mini PCIE connector, and hex
Schmitt-Trigger inverter U1.
Fig. 1. Power Delivery Network setting for a DC IR analysis
Figure 1 shows the ‘floor plan’ of the DC IR analysis
setup with 1.2V voltage path highlighted in yellow and 3.3V path highlighted in
light blue.
Before we assign any Voltage and Current sources, we need to
define pin groups for all nets +1.2V, +3.3V and GND for all PDN component
mentioned above. Having pin groups will significantly simplify the reviewing
process of the results. Also, it is generally a good practice to start the DC
IR analysis from the ‘big picture’ to understand if certain component gets
enough power from the VRM. If a given IC reports an acceptable level of voltage
being delivered with a good margin, then we don’t need to dig deeper; we can
instead focus on those which may not have good enough margins.
Once we have created all necessary pin groups, we can assign
voltage and current sources. There are several ways of doing that (using wizard
or manual), for this project we will use ‘Generate Circuit Element on
Components’ feature to manually define all sources. Knowing all the components
and having pin groups already created makes the assignment very
straight-forward. All current sources draw different amount of current, as indicated
in our setting, however all current sources have the same Parasitic Resistance
(very large value) and all voltage source also have the same Parasitic
Resistance (very small value). This is shown on Figure 2 and Figure 3.
Note: The type of the current source ‘Constant Voltage’ or
‘Distributed Current’ matters only if you are assigning a current source to a
component with multiple pins on the same net, and since in this project we are
working with pins groups, this setting doesn’t make difference in final results.
Fig. 2. Voltage and Current sources assigned
Fig. 3. Parasitic Resistance assignments for all voltage and current sources
For each power net we have created a voltage source on VRM and multiple current sources on ICs and the connector. All sources have a negative node on a GND net, so we have a good common return path. And in addition, we have assigned a negative node of both voltage sources (one for +1.2V and one for +3.3V) as our reference points for our analysis. So reported voltage values will be referenced to that that node as absolute 0V.
At this point, the DC IR setup is complete and ready for
simulation.
Results overview and validation
When the DC IR simulation is finished, there is large amount
of data being generated, therefore there are different ways of viewing results,
all options are presented on Figure 4. In this article I will be
primarily focusing on ‘Power Tree’ and ‘Element Data’. As an additional source
if validation we may review the currents and voltages overlaying the design to
help us to visualize the current flow and power distribution. Most of the time
this helps to understand if our assumption of pin grouping is accurate.
Fig. 4. Options to view different aspects of DC IR simulated data
Power Tree
First let’s look at the Power Tree, presented on Figure 5. Two different power nets were simulated, +1.2V and +3.3V, each of which has specified Current Sources where the power gets delivered. Therefore, when we analyze DC IR results in the Power tree format, we see two ‘trees’, one for each power net. Since we don’t have any pins, which would get both 1.2V and 3.3V at the same time (not very physical example), we don’t have ‘common branches’ on these two ‘trees’.
Now, let’s dissect all the information present in this power tree (taking in consideration only one ‘branch’ for simplicity, although the logic is applicable for all ‘branches’):
We were treating both power nets +1.2V and +3.3V as separate voltage loops, so we have assigned negative nodes of each Voltage Source as a reference point. Therefore, we see the ‘GND’ symbol ((1) and (2)) for each voltage source. Now all voltage calculations will be referenced to that node as 0V for its specific tree.
Then we see the path from Voltage Source to Current Source, the value ΔV shows the Voltage Drop in that path (3). Ultimately, this is the main value power engineers usually are interested in during this type of analysis. If we subtract ΔV from Vout we will get the ‘Actual Voltage’ delivered to the specific current source positive pin (1.2V – 0.22246V = 0.977V). That value reported in the box for the Current Source (4). Technically, the same voltage drop value is reported in the column ‘IR Drop’, but in this column we get more details – we see what the percentage of the Vout is being dropped. Engineers usually specify the margin value of the acceptable voltage drop as a percentage of Vout, and in our experiment we have specified 15%, as reported in column ‘Specification’. And we see that 18.5% is greater than 15%, therefore we get ‘Fail_I_&_V’ results (6) for that Current Source.
Regarding the current – we have manually specified the current value for each Current Source. Current values in Figure 2 are the same as in Figure 5. Also, we can specify the margin for the current to report pass or fail. In our example we assigned 108A as a current at the Current Source (5), while 100A is our current limit (4). Therefore, we also got failed results for the current as well.
As mentioned earlier, we assigned current values for each Current Source, but we didn’t set any current values for the Voltage Source. This is because the tool calculates how much current needs to be assigned for the Voltage Source, based on the value at the Current Sources. In our case we have 3 Current Sources 108A, 63A, 63A (5). The sum of these three values is 234A, which is reported as a current at the Voltage Source (7). Later we will see that this value is being used to calculate output power at the Voltage Source.
Fig. 5. DC IR simulated data viewed as a ‘Power Tree’
Element Data
This option shows us results in the tabular representation. It lists many important calculated data points for specific objects, such as bondwire, current sources, all vias associated with the power distribution network, voltage probes, voltage sources.
Let’s continue reviewing the same power net +1.2V and the power distribution to CPU1 component as we have done for Power Tree (Figure 5). The same way we will be going over the details in point-by-point approach:
First and foremost, when we look at the information for Current Sources, we see a ‘Voltage’ value, which may be confusing. The value reported in this table is 0.7247V (8), which is different from the reported value of 0.977V in Power Tree on Figure 5 (4). The reason for the difference is that reported voltage value were calculated at different locations. As mentioned earlier, the reported voltage in the Power Tree is the voltage at the positive pin of the Current Source. The voltage reported in Element Data is the voltage at the negative pin of the Current Source, which doesn’t include the voltage drop across the ground plane of the return path.
To verify the reported voltage values, we can place Voltage Probes (under circuit elements). Once we do that, we will need to rerun the simulation in order to get the results for the probes:
Two terminals of the ‘VPROBE_1’ attached at the positive pin of Voltage Source and at the positive pin of the Current Source. This probe should show us the voltage difference between VRM and IC, which also the same as reported Voltage Drop ΔV. And as we can see ‘VPROBE_1’ = 222.4637mV (13), when ΔV = 222.464mV (3). Correlated perfectly!
Two terminals of the ‘VPROBE_GND’ attached to the negative pin of the Current Source and negative pin of the Voltage Source. The voltage shown by this probe is the voltage drop across the ground plane.
If we have 1.2V at the positive pin of VRM, then voltage drops 222.464mV across the power plane, so the positive pin of IC gets supplied with 0.977V. Then the voltage at the Current Source 0.724827V (8) being drawn, leaving us with (1.2V – 0.222464V – 0.724827V) = 0.252709V at the negative pin of the Current Source. On the return path the voltage drops again across the ground plane 252.4749mV (14) delivering back at the negative pin of VRM (0.252709V – 0.252475V) = 234uV. This is the internal voltage drop in the Voltage Source, as calculated as output current at VRM 234A (7) multiplied by Parasitic Resistance 1E-6Ohm (Figure 3) at VRM. This is Series R Voltage (11)
Parallel R Current of the Current source is calculated as Voltage 724.82mV (8) divided by Parasitic Resistance of the Current Source (Figure 3) 5E+7 Ohm = 1.44965E-8 (9)
Current of the Voltage Source report in the Element Data 234A (10) is the same value as reported in the Power Tree (sum of all currents of Current Sources for the +1.2V power net) = 234A (7). Knowing this value of the current we can multiple it by Parasitic Resistance of the Voltage Source (Figure 3) 1E-6 Ohm = (234A * 1E-6Ohm) = 234E-6V, which is equal to reported Series R Voltage (11). And considering that the 234A is the output current of the Voltage Source, we can multiple it by output voltage Vout = 1.2V to get a Power Output = (234A * 1.2V) = 280.85W (12)
Fig. 6. DC IR simulated data viewed in the table format as ‘Element Data’
In addition to all provided above calculations and explanations, the video below in Figure 7 highlights all the key points of this article.
Fig. 7. Difference between reporting Voltage values in Power Tree and Element Data
Conclusion
By carefully reviewing the Power Tree and Element Data reporting options, we can determine many important decisions about the power delivery network quality, such as how much voltage gets delivered to the Current Source; how much voltage drop is on the power net and on the ground net, etc. More valuable information can be extracted from other DC IR results options, such as ‘Loop Resistance’, ‘Path Resistance’, ‘RL table’, ‘Spice Netlist’, full ‘Report’. However, all these features deserve a separate topic.
As always, if you would like to receive more information related to this topic or have any questions please reach out to us at info@padtinc.com.
Engineering simulation has long been constrained by fixed computing resources available on a desktop or cluster. Today, however, cloud computing can deliver the on-demand, high performance computing (HPC) capacity required for faster high-fidelity results offering greater performance insight, all from the comfort of your home.
Ansys Cloud delivers the speed, power and compute capacity of cloud computing directly to your desktop — when and where you need it. You can run larger, more complex and more accurate simulations to gain more insight into your product — or you can evaluate more design variations to find the optimal design without long hardware/software procurement and deployment delays.
Join PADT’s Senior Application & Simulation Support Engineer Sina Ghods for a look at how Ansys is working to drive adoption by providing users a ready to use cloud service that provides:
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In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s seasoned expert at working with Ansys from home Matt Sutton for a quick discussion on tips and best practices that make working from home more productive and effective.
If you would like to learn more about how PADT and Ansys can help you to better run your simulation from your home office, check out our webinar on the topic here: https://bit.ly/3dSa8WN
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!
HFSS offers different method of
creating and simulating a large array. The explicit method, shown in Figure
1(a) might be the first method that comes to our mind. This is where you create
the exact CAD of the array and solve it. While this is the most accurate method
of simulating an array, it is computationally extensive. This method may be
non-feasible for the initial design of a large array. The use of unit cell (Figure
1(b)) and array theory helps us to start with an estimate of the array
performance by a few assumptions. Finite Array Domain Decomposition (or FADDM)
takes advantage of unit cell simplicity and creates a full model using the
meshing information generated in a unit cell. In this blog we will review the
creation of unit cell. In the next blog we will explain how a unit cell can be
used to simulate a large array and FADDM.
In a unit cell, the following assumptions are made:
The pattern of each element is identical.
The array is uniformly excited in amplitude, but not necessarily in phase.
Edge affects and mutual coupling are ignored
Fig. 2 An array consisting of elements amplitude and phases can be estimated with array theory, assuming all elements have the same amplitude and element radiation patterns. In unit cell simulation it is assumed all magnitudes (An’s) are equal (A) and the far field of each single element is equal.
A unit cell works based on
Master/Slave (or Primary/Secondary) boundary around the cell. Master/Slave
boundaries are always paired. In a rectangular cell you may use the new Lattice
Pair boundary that is introduced in Ansys HFSS 2020R1. These boundaries are
means of simulating an infinite array and estimating the performance of a
relatively large arrays. The use of unit cell reduces the required RAM and
solve time.
Primary/Secondary (Master/Slave) (or P/S) boundaries can be
combined with Floquet port, radiation or PML boundary to be used in an infinite
array or large array setting, as shown in Figure 3.
Fig. 3 Unit cell can be terminated with (a) radiation boundary, (b) Floquet port, (c) PML boundary, or combination of them.
To create a unit cell with P/S boundary, first start with a single element with the exact dimensions of the cell. The next step is creating a vacuum or airbox around the cell. For this step, set the padding in the location of P/S boundary to zero. For example, Figure 4 shows a microstrip patch antenna that we intend to create a 2D array based on this model. The array is placed on the XY plane. An air box is created around the unit cell with zero padding in X and Y directions.
Fig. 4 (a) A unit cell starts with a single element with the exact dimensions as it appears in the lattice
Fig. 4 (b) A vacuum box is added around it
You notice that in this example
the vacuum box is larger than usual size of quarter wavelength that is usually used
in creating a vacuum region around the antenna. We will get to calculation of this
size in a bit, for now let’s just assign a value or parameter to it, as it will
be determined later. The next step is to define P/S to generate the lattice. In
AEDT 2020R1 this boundary is under “Coupled” boundary. There are two methods to
create P/S: (1) Lattice Pair, (2) Primary/Secondary boundary.
Lattice Pair
The Lattice Pair works best for
square lattices. It automatically assigns the primary and secondary boundaries.
To assign a lattice pair boundary select the two sides that are supposed to
create infinite periodic cells, right-click->Assign
Boundary->Coupled->Lattice Pair, choose a name and enter the scan angles.
Note that scan angles can be assigned as parameters. This feature that is introduced
in 2020R1 does not require the user to define the UV directions, they
are automatically assigned.
Fig. 5 The lattice pair assignment (a) select two lattice walls
Fig. 5 (d) Phi and Theta scan angles can be assigned as parameters
Primary/Secondary
Primary/Secondary boundary is the same as what used to be called Master/Slave boundary. In this case, each Secondary (Slave) boundary should be assigned following a Primary (Master) boundary UV directions. First choose the side of the cell that Primary boundary. Right-click->Assign Boundary->Coupled->Primary. In Primary Boundary window define U vector. Next select the secondary wall, right-click->Assign Boundary->Couple->Secondary, choose the Primary Boundary and define U vector exactly in the same direction as the Primary, add the scan angles (the same as Primary scan angles)
Fig. 6 Primary and secondary boundaries highlights.
Floquet Port and Modes Calculator
Floquet port excites and
terminates waves propagating down the unit cell. They are similar to waveguide
modes. Floquet port is always linked to P/S boundaries. Set of TE and TM modes
travel inside the cell. However, keep in mind that the number of modes that are
absorbed by the Floquet port are determined by the user. All the other modes
are short-circuited back into the model. To assign a Floquet port two major
steps should be taken:
Defining Floquet Port
Select the face of the cell that you like to assign the Floquet port. This is determined by the location of P/S boundary. The lattice vectors A and B directions are defined by the direction of lattice (Figure 7).
Fig. 7 Floquet port on top of the cell is defined based on UV direction of P/S pairs
The number of modes to be
included are defined with the help of Modes Calculator. In the Mode Setup tab
of the Floquet Port window, choose a high number of modes (e.g. 20) and click
on Modes Calculator. The Mode Table Calculator will request your input of
Frequency and Scan Angles. After selecting those, a table of modes and their
attenuation using dB/length units are created. This
is your guide in selecting the height of the unit cell and vaccume box. The
attenation multiplied by the height of the unit cell (in the project units,
defined in Modeler->Units) should be large enough to make sure the modes are
attenuated enough so removing them from the calcuatlion does not cause errors.
If the unit cell is too short, then you will see many modes are not attenuated
enough. The product of the attenuatin and height of the airbox should be at
least 50 dB. After the correct size for the airbox is calcualted and entered,
the model with high attenuation can be removed from the Floquet port
definition.
The 3D Refinement tab is used to control the inclusion of the modes in the 3D refinement of the mesh. It is recommended not to select them for the antenna arrays.
Fig. 8 (Left) Determining the scan angles for the unit cell, (Right) Modes Calculator showing the Attenuation
In our example, Figure 8 shows
that the 5th mode has an attenuation of 2.59dB/length. The height of
the airbox is around 19.5mm, providing 19.5mm*2.59dB/mm=50.505dB attenuation
for the 5th mode. Therefore, only the first 4 modes are kept for the
calculations. If the height of the airbox was less than 19.5mm, we would need to
increase the height so accordingly for an attenuation of at least 50dB.
Radiation Boundary
A simpler alternative for Floquet port is radiation boundary. It is important to note that the size of the airbox should still be kept around the same size that was calculated for the Floquet port, therefore, higher order modes sufficiently attenuated. In this case the traditional quarter wavelength padding might not be adequate.
Fig. 9 Radiation boundary on top of the unit cell
Perfectly Matched Layer
Although using radiation boundary
is much simpler than Floquet port, it is not accurate for large scan angles. It
can be a good alternative to Floquet port only if the beam scanning is limited
to small angles. Another alternative to Floquet port is to cover the cell by a
layer of PML. This is a good compromise and provides very similar results to
Floquet port models. However, the P/S boundary need to surround the PML layer
as well, which means a few additional steps are required. Here is how you can
do it:
Reduce the size of the airbox* slightly, so after adding the PML layer, the unit cell height is the same as the one that was generated using the Modes Calculation. (For example, in our model airbox height was 19mm+substrte thickness, the PML height was 3mm, so we reduced the airbox height to 16mm).
Choose the top face and add PML boundary.
Select each side of the airbox and create an object from that face (Figure 10).
Select each side of the PML and create objects from those faces (Figure 10).
Select the two faces that are on the same plane from the faces created from airbox and PML and unite them to create a side wall (Figure 10).
Then assign P/S boundary to each pair of walls (Figure 10).
*Please note for this method, an auto-size “region” cannot be used, instead draw a box for air/vacuum box. The region does not let you create the faces you need to combine with PML faces.
Fig. 10 Selecting two faces created from airbox and PML and uniting them to assign P/S boundaries
The advantage of PML termination over Floquet port is that it is simpler and sometimes faster calculation. The advantage over Radiation Boundary termination is that it provides accurate results for large scan angles. For better accuracy the mesh for the PML region can be defined as length based.
Seed the Mesh
To improve the accuracy of the PML model further, an option is to use length-based mesh. To do this select the PML box, from the project tree in Project Manager window right-click on Mesh->Assign Mesh Operation->On Selection->Length Based. Select a length smaller than lambda/10.
Fig. 11 Using element length-based mesh refinement can improve the accuracy of PML design
Scanning the Angle
In phased array simulation, we are mostly interested in the performance of the unit cell and array at different scan angles. To add the scanning option, the phase of P/S boundary should be defined by project or design parameters. The parameters can be used to run a parametric sweep, like the one shown in Figure 12. In this example the theta angle is scanned from 0 to 60 degrees.
Fig. 12 Using a parametric sweep, the scanned patterns can be generated
Comparing PML and Floquet Port with Radiation Boundary
To see the accuracy of the
radiation boundary vs. PML and Floquet Port, I ran the simulations for scan
angles up to 60 degrees for a single element patch antenna. Figure 13 shows
that the accuracy of the Radiation boundary drops after around 15 degrees
scanning. However, PML and Floquet port show similar performance.
Fig. 13 Comparison of radiation patterns using PML (red), Floquet Port (blue), and Radiation boundary (orange).
S Parameters
To compare the accuracy, we can also check the S parameters. Figure 14 shows the comparison of active S at port 1 for PML and Floquet port models. Active S parameters were used since the unit cell antenna has two ports. Figure 15 shows how S parameters compare for the model with the radiation boundary and the one with the Floquet port.
Fig. 14 Active S parameter comparison for different scan angles, PML vs. Floquet Port model.
Fig. 15 Active S parameter comparison for different scan angles, Radiation Boundary vs. Floquet Port model.
Conclusion
The unit cell definition and options on terminating the cell
were discussed here. Stay tuned. In the next blog we discuss how the unit cell
is utilized in modeling antenna arrays.
As companies are closing their doors in order to help ensure the health and safety of their employees and customers, those that can are pivoting to working from home.
But what about those working on product design, testing, and analysis that require a physical presence?
Here at PADT we know that the show must go on, and companies working across various technical professions are needed to keep the world moving forward, especially in these trying times. Thus we would like to introduce a solution: Ansys Engineering Simulation Software.
Join The PADT team for a panel discussion on how you can use simulation to move your in-person workflow to a digital environment, as well as what specific Ansys tools can be used to access your work from home.
All of this will be followed by a live Q&A in which our expert staff will take any questions regarding your specific concerns with transitioning your workflow and all other things related to working from home.
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As systems become more complex, the introduction and
adoption of detailed Multiphysics / Multidomain tools is becoming more
commonplace. Oftentimes, these tools serve as preprocessors and specialized
interfaces for linking together other base level tools or models in a
meaningful way. This is what Ansys Sherlock does for Circuit Card Assemblies
(CCAs), with a heavy emphasis on product reliability through detailed life
cycle definitions.
In an ideal scenario, the user will have already compiled a detailed ODB++ archive containing all the relevant model information. For Sherlock, this includes .odb files for each PCB layer, the silkscreens, component lists, component locations separated by top/bottom surface, drilled locations, solder mask maps, mounting points, and test points. This would provide the most streamlined experience from a CCA design through reliability analysis, though any of these components can be imported individually.
These definitions, in combination with an extensive library of package geometries, allow Sherlock to generate a 3D model consisting of components that can be checked against accepted parts lists and material properties. The inclusion of solder mask and silkscreen layers also makes for convenient spot-checking of component location and orientation. If any of these things deviate from the expected or if basic design variation and optimization studies need to be conducted, new components can be added and existing components can be removed, exchanged, or edited entirely within Sherlock.
Figure 1: Sherlock’s 2D layer viewer and editor. Each layer can be toggled on/off, and components can be rearranged.
While a few of the available analyses depend on just the
component definitions and geometries (Part Validation, DFMEA, and CAF Failure),
the rest are in some way connected to the concept of life cycle definitions.
The overall life cycle can be organized into life phases, e.g. an operating
phase, packaging phase, transport phase, or idle phase, which can then contain
any number of unique event definitions. Sherlock provides support for vibration
events (random and harmonic), mechanical shock events, and thermal events. At
each level, these phases and events can be prescribed a total duration, cycle
count, or duty cycle relative to their parent definition. On the Life Cycle
definition itself, the total lifespan and accepted failure probability within
that lifespan are defined for the generation of final reliability metrics. Figure 1 demonstrates an example layout for a
CCA that may be part of a vehicle system containing both high cycle fatigue
thermal and vibration events, and low cycle fatigue shock events.
Figure 2: Product life cycles are broken down into life phases that contain life events. Each event is customizable through its duration, frequency, and profile.
The remaining analysis types can be divided into two categories: FEA and part specification-based. The FEA based tests function by generating a 3D model with detail and mesh criteria determined within Sherlock, which is then passed over to an Ansys Mechanical session for analysis. Sherlock provides quite a lot of customization on the pre-processing level; the menu options include different methods and resolutions for the PCB, explicit modeling of traces, and inclusion or exclusion of part leads, mechanical parts, and potting regions, among others.
Figure 3: Left shows the 3D model options, the middle shows part leads modeled, and right shows a populated board.
Each of the FEA tests, Random Vibration, Harmonic Vibration,
Mechanical Shock, and Natural Frequency, correspond to an analysis block within
Ansys Workbench. Once these simulations are completed, the results file is read
back into Sherlock, and strain values for each component are extracted and
applied to either Basquin or Coffin—Manson fatigue models as appropriate for
each included life cycle event.
Part specification tests include Component Failure Analysis for electrolytic and ceramic capacitors, Semiconductor Wearout for semiconductor devices, and CTE mismatch issues for Plated Through-Hole and solder fatigue. These analyses are much more component-specific in the sense that an electrolytic capacitor has some completely different failure modes than a semiconductor device and including them allows for a broad range of physics to be accounted for across the CCA.
The result from each type of analysis is ultimately a life
prediction for each component in terms of a failure probability curve alongside
a time to failure estimate. The curves for every component are then combined
into a life prediction for the entire CCA under one failure analysis.
Figure 4: Analysis results for Solder Fatigue including an overview for quantity of parts in each score range along with a detailed breakdown of score for each board component.
Taking it one step further, the results from each analysis are then combined into an overall life prediction for the CCA that encompasses all the defined life events. From Figure 5, we can see that the life prediction for this CCA does not quite meet its 5-year requirement, and that the most troublesome analyses are Solder Fatigue and PTH Fatigue. Since Sherlock makes it easy to identify these as problem areas, we could then iterate on this design by reexamining the severity or frequency of applied thermal cycles or adjusting some of the board material choices to minimize CTE mismatch.
Figure 5: Combined life predictions for all failure analyses and life events.
Sherlock’s convenience for defining
life cycle phases and events, alongside the wide variety of component
definitions and failure analyses available, really cement Sherlock’s role as a
comprehensive electronics reliability tool. As in most analyses, the quality of
the results is still dependent on the quality of the input, but all the checks
and cross-validations for components vs life events that come along with
Sherlock’s preprocessing toolset really assist with this, too.
Eric Miller, Ted Harris, Alex Grishin & Joe Woodward
Description:
In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Ted Harris, Alex Grishin, and Joe Woodward to discuss their favorite features in the MAPDL Updates in Ansys 2020 R1.
If you would like to learn more about this topic, you can view PADT’s webinar covering these updates here: https://bit.ly/2WD88vt
Additionally, if you would like to take part in the survey mentioned at the start of the episode click the link here: https://bit.ly/3biWkCp
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!
The ANSYS finite element solvers enable a breadth and depth of capabilities unmatched by anyone in the world of computer-aided simulation. Thermal, Structural, Acoustic, Piezoelectric, Electrostatic and Circuit Coupled Electromagnetics are just an example of what can be simulated. Regardless of the type of simulation, each model is represented by a powerful scripting language, the ANSYS Parametric Design Language (APDL).
APDL is the foundation for all sophisticated features, many of which are not exposed in the Workbench Mechanical user interface. It also offers many conveniences such as parameterization, macros, branching and looping, and complex math operations. All these benefits are accessible within the ANSYS Mechanical APDL user interface.
Join PADT’s Principle & Co-Owner Eric Miller for a look at what’s new for MAPDL in ANSYS 2020 R1, regarding:
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!
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