Efficient and Accurate Simulation of Antenna Arrays in Ansys HFSS – Part 2

Finite Arrays

In addition to explicit modeling of finite arrays in Ansys HFSS, there are three other methods based on using unit cells. To learn more about the unit cell, please see part 1 of this blog.

In part 2, I will introduce and compare these 3 methods (1) Finite array defined using the array setting in unit cell, (2) Finite Array using Domain Decomposition Method (FADDM), (3) 3D component arrays (Figure 1). Please note that that the method 3 requires HFSS 2020R1 or newer.

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Figure 1. (a) Unit cell, (b) FADDM, (c) 3D component array.

Finite Array using Unit Cell

After defining a unit cell (Figure 1a), you may simply define the number of elements, the spacing between them, and the scan angle. The assumption is that there is no mutual coupling, every element has the same radiation pattern and the same excitation. This is a good approximation for large arrays (10×10 or larger). This method may not be accurate enough in some cases, for example for a small number of elements; and when the antenna elements have main beams toward the angles that are close to the plane of the array and toward the other elements, causing a higher level of mutual coupling. However, smaller arrays won’t require as large of a compute resource as a large array.

The advantage of this method is its simulation speed. It requires the minimum memory and time to provide a quick array simulation. To define the array (after running the analysis for unit cell), right-click on Radiation from the Project Manager window. Select Antenna Array Setup, and then Regular Array Setup. In the Antenna Array Setup under Regular Array type, define the location of the first cell, the direction, the distance between the cells and number of cells in each direction.

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Figure 2. Steps to define a finite array, (a) Antenna array setup, (b) & (c) Regular array setup.

Finite Array using Domain Decomposition Method

General Domain Decomposition (DDM) for a single domain provides a way to reduce memory requirement, however, it does not reduce the meshing time for large explicit arrays. Using Finite Array DDM (FADDM) addresses this shortcoming. The FADDM bypasses the adaptive meshing stage by duplicating the mesh that was generated for a unit cell. While the unit cell is used to create the mesh, the assumption of uniform excitation is no longer present. Each element in FADDM can have different magnitude and phase and is individually modeled, however, the mesh created in the unit cell is used to generate the overall mesh, therefore, no CPU time is spent on generating the mesh. This can be seen in Figure 3, by linking the mesh of the unit cell to the FADDM, the mesh is copied, and no mesh refinement will be needed. You may compare it with the explicit array of the same size (Figure 3(c)) where the entire array has to be meshed and mesh refinement will be necessary for adaptive meshing. This can be a huge simulation time saving when the array size is large.

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Figure 3. (a) Mesh from a unit cell, (b) mesh linked to FADDM, (c) explicit array mesh.

To create FADDM from unit cell, create a new HFSS design. Then copy the unit cell into the new design. In the Project Manager window, right-click on Model and choose Create Array, as shown in Figure 4. This opens the window that allows user to define the number of elements of the array along the lattice directions. By selecting “Active Cells” tab, user can define where active, passive and padding cells are located. This gives the user a means of creating different lattice shapes.

Please note that padding cells defined in the General tab represent the size of vacuum buffer surrounding the array. They are not visible to the users but are included in the FADDM simulation. The same mesh from the unit cell simulation is duplicated to padding cells (Figure 5). It is also possible to add padding cells in the Active Cells tab, and those cells are also invisible, but can be used to create the array lattice of the desired shape (Figure 6).

Figure 4. The steps to create a DDM array, the Padding Cells are used to create the vacuum box and are invisible to the user.

Figure 5. The FADDM needs a padding cell to create a vacuum box around the design. The padding cells are invisible to the user.

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Figure 6. (a) Padding cells can be used to create a lattice, (b) the lattice created does not show the padding cells.

The next step is to link the mesh to the unit cell. First, an analysis setup should be created. Choose Advanced Solution Setup. In the Driven Solution Setup General tab, reduce the number of Maximum Number of Passes to 1, as shown in Figure 7(a), then choose Advanced tab and click on Import Mesh (Figure 7(b)). Click on Setup Link. This is to link the simulation to the mesh of a unit cell. There are two steps needed here. First, choose the file or design that contains the mesh information (Figure 7(c)), second is to map the variables Figure 7(d). The last step to setting up the analysis is selecting Advanced Mesh Operation tab and selecting “Ignore mesh operation in target design” (Figure 7(d)). Now the array is ready and simulation can be run. You notice that adaptive meshing goes to only one pass. If in Setup Link window the option of “Simulate source design as needed” is checked (Figure 7(c)), then if a design variable that affects the geometry is changed, the meshing of the unit cell is repeated as needed. After the simulation is completed the elements magnitude and phases can be changed as a post processing step by “Edit Sources” (right-click on Excitations). The source names provided in the edit sources is slightly different than an explicit array (Figure 8)

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Figure 7. Different windows related to setting up a linked mesh in FADDM.

Figure 8. Edit sources gives the ability to change the magnitude and phase of each element.

To compare the run-time and array patterns an example of circular polarized microstrip patch antenna of a 5 ´ 5 element array is shown in Figure 9 and Figure 10. The differences can be seen at angles away from the broadside angle. This shows how the edge effects are ignored in the unit cell approximation. Table 1 shows the comparison of memory and runtime for the three methods.

Table 1. Comparing run time and memory needed for a 5 x 5 array, explicit array vs FADDM.

Elapsed Time (min:sec) Memory (MB)
Unit cell01:0683.1
FADDM03:1781.4
Explicit Array25:0694.7

Figure 9. Comparison of the far-field patterns for LHCP (co-polarization) created using FADDM and unit cell array.

Figure 10. Comparison of the far-field patterns for RHCP (cross-polarization) created using FADDM and unit cell array.

Finally, we compare the co-polarization pattern with an explicit 5 x 5 element array for scan angles of 0 and 30 degrees in  Figure 11 and Figure 12, respectively.

Figure 11. Comparison of far-field LHCP created by explicit array vs. FADDM.

Figure 12. Comparison of scanned far-field LHCP, scan angle of 30 degrees.

3D Component Array

In 2020R1 the option of 3D component array was added. This option provides a means of combining different unit cells in one array. The unit cells are defined and imported as 3D components. To create a 3D component unit cell, define each type of the cells in a separate HFSS design, run the analysis, then select all objects in the model. In the Model ribbon, click on Create 3D Component, assign a name (no spaces are allowed in the name), add any information you like to add such as owner, email, company, etc., then click OK. Once all the 3D component cells are created, create a new HFSS design for the 3D component FADDM.

The next step is to create Relative CS for each of the 3D component elements in the HFSS design that will contain the array. For this step you need to plan the array lattice ahead of time, so the components are placed in the proper locations (Figure 13). Overlap is not allowed.

The unit cells should have the followings:

  • Identical dimensions of the bounding boxes
  • Identical Primary/Secondary (Master/Slave) boundary on the unit cells.

The generation of FADDM is similar to single unit cell array, except that when you select Model->Create Array, the window will be shown as 3D Component Array Properties (Figure 14). After choosing the number of elements and the number of padding cells, the unit cells window (Figure 15) will give you options of choosing one of the 3D component unit cells for each location of the elements in the lattice. The cells can be color coded. In the example shown in Figure 16  there are 3 components, the blank cell, the vertical cell and the horizontal cell. The sources under Edit Sources window are also arranged based on the name of the 3D component cells. At this point there is no option of linking the mesh. Therefore, the number of passes for adaptive mesh should be set to a number that is appropriate for getting a convergence.

Figure 13. 3D component unit cells are arranged to create a 3D component array.

Figure 14. The 3D component array can be created the same way as creating FADDM using a unit cell.

Figure 15. The unit cells are color coded for easier lattice creation.

Figure 16. The result of lattice created using 3D component array.

Conclusion

Unit cell and Finite Array Domain Decomposition are excellent options for simulating large finite arrays within a reasonable runtime and memory requirements. The 3D component finite array is a nice added feature in 2020R1 that now provides a way to combine unit cells with different geometries in one array.

If you would like more information related to this topic or have any questions, please reach out to us at info@padtinc.com.

Windows Update KB4571756 Triggers Error 3221227010 for Ansys Electronics Products

On September 7, 2020 Microsoft released a Windows update KB4571756, which may cause the Ansys electronic products to fail with the Error:

3221227010 at ‘reg_ansysedt.exe’ and ‘reg_siwave.exe’ registration.

This is the error message, users would see if they right-mouse-click and run the following file as administrator:

C:\Program Files\AnsysEM\AnsysEM20.2\Win64\config\ConfigureThisMachine.exe

To resolve this issue, here are the steps we recommend users take:

  1. Revert the updates.
    1. If the issue is not resolved or something your IT won’t let you do, continue to the next steps.
  2. Set an environment variable that turns off the driver that is causing the error. 
    1. Use windows search and type “system environment” and click on “Edit the system environment variables”
    2. This opens the “System Properties” tool
    3. Go to the “Advanced” tab
    4. Click on “Environment Variables…” at the bottom
    5. In the System Variables window click on “New…”
    6. Create the following variable:

      Variable Name: ANSYS_EM_DONOT_PRELOAD_3DDRIVER_DLL
      Variable Value: 1
    7. Click OK 3 times to exit out of the tool and save your changes. 
  3. If the issue is still not resolved, there is one more step:
    1. Go to C:\Program Files\AnsysEM\AnsysEM20.2\Win64\config\
    2. Right-Mouse-Click on “ConfigureThisMachine.exe” and run as Admin. 

If these steps helped to resolve the issue, you will see the following info message when ‘ConfigureThisMachine.exe’ is run:

If this does not work, please contact your Ansys support provider. 

Revolutionizing the Way Data Moves Through Space with Ansys Simulation – Webinar

Ever since NASA began its race to space, U.S. technology companies have searched for solutions to solve a variety of challenges designed to push us further in our exploration of the stars. Whether the purpose is for space travel or for launching satellites that track weather patterns, space innovation is gaining momentum. One of the most critical challenges we are trying to solve is how to optimize communication with moving spacecrafts. Tucson Arizona’s FreeFall Aerospace has an answer; developing unique antenna systems for both space and ground use.

When working to develop this technology, FreeFall ran into a number of roadblocks due to limitations in its engineering software tool-set. The company was able to bypass these hurdles and successfully optimize development thanks to the introduction of Ansys HFSS, a specialized 3D electromagnetic software used for designing and simulating high-frequency electronic products such as antennas, antenna arrays, RF/microwave components, and much more. Because of the speed of this tool and its ability to solve multiple simulation challenges in different domains, FreeFall is able to make design changes more quickly and with better data.

Join PADT’s Lead Electromagnetics Engineer Michael Griesi and President of FreeFall, Doug Stetson for a discussion on Ansys electromagnetics offerings, and how FreeFall is able to take advantage of them for their unique application.

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!

Efficient and Accurate Simulation of Antenna Arrays in Ansys HFSS

Unit-cell in HFSS

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.

Fig. 1 (a) Explicit Array
Fig. 1 (b) Unit Cell
Fig. 1 (c) Finite Array Domain Decomposition (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 (b) Assign the lattice pair boundary
Fig. 5 (c) After, right-click and choosing assign boundary > choose Lattice Pair
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:

  1. 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).
  2. Choose the top face and add PML boundary.
  3. Select each side of the airbox and create an object from that face (Figure 10).
  4. Select each side of the PML and create objects from those faces (Figure 10).
  5. 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).
  6. 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.

Reduce EMI with Good Signal Integrity Habits

Recently the ‘Signal Integrity Journal’ posted their ‘Top 10 Articles’ of 2019. All of the articles included were incredible, however, one stood out to me from the rest – ‘Seven Habits of Successful 2-Layer Board Designers’ by Dr. Eric Bogatin (https://www.signalintegrityjournal.com/blogs/12-fundamentals/post/1207-seven-habits-of-successful-2-layer-board-designers). In this work, Dr. Bogatin and his students were developing a 2-Layer printed circuit board (PCB), while trying to minimize signal and power Integrity issues as much as possible. As a result, they developed a board and described seven ‘golden habits’ for this board development. These are fantastic habits that I’m confident we can all agree with. In particular, there was one habit at which I wanted to take a deeper look:

“…Habit 4: When you need to route a cross-under on the bottom layer, make it short. When you can’t make it short, add a return strap over it..”

Generally speaking, this habit suggests to be very careful with the routing of signal traces over the gap on the ground plane. From the signal integrity point of view, Dr. Bogatin explained it perfectly – “..The signal traces routed above this gap will see a gap in the return path and generate cross talk to other signals also crossing the gap..”. On one hand, crosstalk won’t be a problem if there are no other nets around, so the layout might work just fine in that case. However, crosstalk is not the only risk. Fundamentally, crosstalk is an EMI problem. So, I wanted to explore what happens when this habit is ignored and there are no nearby nets to worry about.

To investigate, I created a simple 2-Layer board with the signal trace, connected to 5V voltage source, going over an air gap. Then I observed the near field and far field results using ANSYS SIwave solution. Here is what I found.

Near and Far Field Analysis

Typically, near and far fields are characterized by solved E and H fields around the model. This feature in ANSYS SIwave gives the engineer the ability to simulate both E and H fields for near field analysis, and E field for Far Field analysis.

First and foremost, we can see, as expected, that both near and far Field have resonances at the same frequencies. Additionally, we can observe from Figure 1 that both E and H fields for near field have the largest radiation spikes at 786.3 MHz and 2.349GHz resonant frequencies.

Figure 1. Plotted E and H fields for both Near and Far Field solutions

If we plot E and H fields for Near Field, we can see at which physical locations we have the maximum radiation.

Figure 2. Plotted E and H fields for Near field simulations

As expected, we see the maximum radiation occurring over the air gap, where there is no return path for the current. Since we know that current is directly related to electromagnetic fields, we can also compute AC current to better understand the flow of the current over the air gap.

Compute AC Currents (PSI)

This feature has a very simple setup interface. The user only needs to make sure that the excitation sources are read correctly and that the frequency range is properly indicated. A few minutes after setting up the simulation, we get frequency dependent results for current. We can review the current flow at any simulated frequency point or view the current flow dynamically by animating the plot.

Figure 3. Computed AC currents

As seen in Figure 3, we observe the current being transferred from the energy source, along the transmission line to the open end of the trace. On the ground layer, we see the return current directed back to the source. However at the location of the air gap there is no metal for the return current to flow, therefore, we can see the unwanted concentration of energy along the plane edges. This energy may cause electromagnetic radiation and potential problems with emission.

If we have a very complicated multi-layer board design, it won’t be easy to simulate current flow on near and far fields for the whole board. It is possible, but the engineer will have to have either extra computing time or extra computing power. To address this issue, SIwave has a feature called EMI Scanner, which helps identify problematic areas on the board without running full simulations.

EMI Scanner

ANSYS EMI Scanner, which is based on geometric rule checks, identifies design issues that might result in electromagnetic interference problems during operation. So, I ran the EMI Scanner to quickly identify areas on the board which may create unwanted EMI effects. It is recommended for engineers, after finding all potentially problematic areas on the board using EMI Scanner, to run more detailed analyses on those areas using other SIwave features or HFSS.

Currently the EMI Scanner contains 17 rules, which are categorized as ‘Signal Reference’, ‘Wiring/Crosstalk’, ‘Decoupling’ and ‘Placement’. For this project, I focused on the ‘Signal Reference’ rules group, to find violations for ‘Net Crossing Split’ and ‘Net Near Edge of Reference’. I will discuss other EMI Scanner rules in more detail in a future blog (so be sure to check back for updates).

Figure 4. Selected rules in EMI Scanner (left) and predicted violations in the project (right)

As expected, the EMI Scanner properly identified 3 violations as highlighted in Figure 4. You can either review or export the report, or we can analyze violations with iQ-Harmony. With this feature, besides generating a user-friendly report with graphical explanations, we are also able to run ‘What-if’ scenarios to see possible results of the geometrical optimization.

Figure 5. Generated report in iQ-Harmony with ‘What-If’ scenario

Based on these results of quick EMI Scanner, the engineer would need to either redesign the board right away or to run more analysis using a more accurate approach.

Conclusion

In this blog, we were able to successfully run simulations using ANSYS SIwave solution to understand the effect of not following Dr.Bogatin’s advice on routing the signal trace over the gap on a 2-Layer board. We also were able to use 4 different features in SIwave, each of which delivered the correct, expected results.

Overall, it is not easy to think about all possible SI/PI/EMI issues while developing a complex board. In these modern times, engineers don’t need to manufacture a physical board to evaluate EMI problems. A lot of developmental steps can now be performed during simulations, and ANSYS SIwave tool in conjunction with HFSS Solver can help to deliver the right design on the first try.

If you would like more information or have any questions please reach out to us at info@padtinc.com.

Defining Antenna Array Excitations with Nested-If Statements in HFSS

HFSS offers various methods to define array excitations. For a large array, you may take advantage of an option “Load from File” to load the magnitude and phase of each port. However, in many situations you may have specific cases of array excitation. For example, changing amplitude tapering or the phase variations that happens due to frequency change. In this blog we will look at using the “Edit Sources” method to change the magnitude and phase of each excitation. There are cases that might not be easily automated using a parametric sweep. If the array is relatively small and there are not many individual cases to examine you may set up the cases using “array parameters” and “nested-if”.

In the following example, I used nested-if statements to parameterize the excitations of the pre-built example “planar_flare_dipole_array”, which can be found by choosing File->Open Examples->HFSS->Antennas (Fig. 1) so you can follow along. The file was then saved as “planar_flare_dipole_array_if”. Then one project was copied to create two examples (Phase Variations, Amplitude Variations).

Fig. 1. Planar_flare_dipole_array with 5 antenna elements (HFSS pre-built example).

Phase Variation for Selected Frequencies

In this example, I assumed there were three different frequencies that each had a set of coefficients for the phase shift. Therefore, three array parameters were created. Each array parameter has 5 elements, because the array has 5 excitations:

A1: [0, 0, 0, 0, 0]

A2: [0, 1, 2, 3, 4]

A3: [0, 2, 4, 6, 8]

Then 5 coefficients were created using a nested_if statement. “Freq” is one of built-in HFSS variables that refers to frequency. The simulation was setup for a discrete sweep of 3 frequencies (1.8, 1.9 and 2.0 GHz) (Fig. 2). The coefficients were defined as (Fig. 3):

E1: if(Freq==1.8GHz,A1[0],if(Freq==1.9GHz,A2[0],if(Freq==2.0GHz,A3[0],0)))

E2: if(Freq==1.8GHz,A1[1],if(Freq==1.9GHz,A2[1],if(Freq==2.0GHz,A3[1],0)))

E3: if(Freq==1.8GHz,A1[2],if(Freq==1.9GHz,A2[2],if(Freq==2.0GHz,A3[2],0)))

E4: if(Freq==1.8GHz,A1[3],if(Freq==1.9GHz,A2[3],if(Freq==2.0GHz,A3[3],0)))

E5: if(Freq==1.8GHz,A1[4],if(Freq==1.9GHz,A2[4],if(Freq==2.0GHz,A3[4],0)))

Please note that the last case is the default, so if frequency is none of the three frequencies that were given in the nested-if, the default phase coefficient is chosen (“0” in this case).

Fig. 2. Analysis Setup.

Fig. 3. Parameters definition for phase varaitioin case.

By selecting the menu item HFSS ->Fields->Edit Sources, I defined E1-E5 as coefficients for the phase shift. Note that phase_shift is a variable defined to control the phase, and E1-E5 are meant to be coefficients (Fig. 4):

Fig. 4. Edit sources using the defined variables.

The radiation pattern can now be plotted at each frequency for the phase shifts that were defined (A1 for 1.8 GHz, A2 for 1.9 GHz and A3 for 2.0 GHz) (Figs 5-6):

 Fig. 5. Settings for radiation pattern plots.

Fig. 6. Radiation patten for phi=90 degrees and different frequencies, the variation of phase shifts shows how the main beam has shifted for each frequency.

Amplitude Variation for Selected Cases

In the second example I created three cases that were controlled using the variable “CN”. CN is simply the case number with no units.

The variable definition was similar to the first case. I defined 3 array parameters and 5 coefficients. This time the coefficients were used for the Magnitude. The variable in the nested-if was CN. That means 3 cases and a default case were created. The default coefficient here was chosen as “1” (Figs. 7-8).

A1: [1, 1.5, 2, 1.5, 1]

A2: [1, 1, 1, 1, 1]

A3: [2, 1, 0, 1, 2]

E1: if(CN==1,A1[0],if(CN==2,A2[0],if(CN==3,A3[0],1)))*1W

E2: if(CN==1,A1[1],if(CN==2,A2[1],if(CN==3,A3[1],1)))*1W

E3: if(CN==1,A1[2],if(CN==2,A2[2],if(CN==3,A3[2],1)))*1W

E4: if(CN==1,A1[3],if(CN==2,A2[3],if(CN==3,A3[3],1)))*1W

E5: if(CN==1,A1[4],if(CN==2,A2[4],if(CN==3,A3[4],1)))*1W

Fig. 7. Parameters definition for amplitude varaitioin case.

Fig. 8. Exciation setting for amplitude variation case.

Notice that CN in the parametric definition has the value of “1”. To create the solution for all three cases I used a parametric sweep definition by selecting the menu item Optimetrics->Add->Parametric. In the Add/Edit Sweep I chose the variable “CN”, Start: 1, Stop:3, Step:1. Also, in the Options tab I chose to “Save Fields and Mesh” and “Copy geometrically equivalent meshes”, and “Solve with copied meshes only”. This selection helps not to redo the adaptive meshing as the geometry is not changed (Fig. 9). In plotting the patterns I could now choose the parameter CN and the results of plotting for CN=1, 2, and 3 is shown in Fig. 10. You can see how the tapering of amplitude has affected the side lobe level.

Fig. 9. Parameters definition for amplitude varaitioin case.

 Fig. 10. Radiation patten for phi=90 degrees and different cases of amplitude tapering, the variation of amplitude tapering has caused chagne in the beamwidth and side lobe levels.

Drawback

The drawback of this method is that array parameters are not post-processing variables. This means changing them will create the need to re-run the simulations. Therefore, it is needed that all the possible cases to be defined before running the simulation.

If you would like more information or have any questions please reach out to us at info@padtinc.com.

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.

All Things ANSYS 045: Using Simulation to Disrupt the RF Antenna Industry

 

Published on: August 26th, 2019
With: Eric Miller & Stefan O’Dougherty
Description:  

In this episode, your host and Co-Founder of PADT, Eric Miller is joined by Stefan O’Dougherty of FreeFall Moving Data to discuss the use of ANSYS simulation tools to drive the design of their unique RF antenna concept.

To learn more about FreeFall and see their product in action, click the link below and view the Wired article discussed in the interview portion of today’s episode: https://www.wired.com/story/new-space-telescopes-could-look-like-giant-beach-balls/

If you would like to learn more about what’s available in the latest release of ANSYS HFSS check out PADT’s webinar on the subject here: https://www.brighttalk.com/webcast/15747/361278

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!

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High Frequency Electromagnetic Updates in ANSYS 2019 R2 – Webinar

HFSS (High Frequency Structure Simulator) employs versatile solvers and an intuitive GUI to provide unparalleled performance, as well as deep insight, into a wide variety of 3D electromagnetic (EM) problems. ANSYS HFSS is the premier EM tool for R&D and virtual design prototyping. It reduces design cycle time and boosts your product’s reliability and performance. 

The ANSYS HFSS simulation suite consists of a comprehensive set of solvers to address diverse electromagnetic problems, ranging in detail and scale from passive IC components to extremely large-scale EM analyses. Its reliable automatic adaptive mesh refinement allows users to focus on the design instead of spending time determining and creating the best mesh.

Join PADT’s Lead Electromagnetics Engineer Michael Griesi for a look at what new capabilities are available for HFSS users in ANSYS 2019 R2.

This presentation will include updates for the following topics:

  • Solve speed
  • Electronics Desktop
  • ANSYS Cloud
  • Post processing
  • And much more

Register Here

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“Equation Based Surface” for Conformal and Non-Planar Antenna Design

ANYSY HFSS provides many options for creating non-planar and conformal shapes. In MCAD you may use shapes such as cylinders or spheres, and with some steps, you can design you antennas on various surfaces. In some applications, it is necessary to study the effect of curvatures and shapes on the antenna performance. For example for wearable antennas it is important to study the effect of bending, crumpling and air-gap between antenna and human body.

Equation Based Surface

One of the tools that HFSS offers and can be used to do parametric sweep or optimization, is “Draw equation based surface”. This can be accessed under “Draw” “Equation Based Surface” or by using “Draw” tab and choosing it from the banner (Fig. 1)

Fig. 1. (a) Select Draw -> Equation Based Surface
Fig. 1. (b) click on the icon that is highlighted

Once this is selected the Equation Based Surface window that opens gives you options to enter the equation with the two variables (_u, _v_) to define a surface. Each point of the surface can be a function of (_u,_v). The range of (_u, _v) will also be determined in this window. The types of functions that are available can be seen in “Edit Equation” window, by clicking on “…” next to X, Y or Z (Fig. 2). Alternatively, the equation can be typed inside this window. Project or Design Variables can also be used or introduced here.

Fig. 2. (a) Equation Based Surface window
Fig. 2. (b) Clikc on the “…” next to X and see the “Edit Equation: window to build the equation for X

For example an elliptical cylinder along y axis can be represented by:

This equation can be entered as shown in Fig. 3.

Fig. 3. Elliptical surface equation

Variation of this equation can be obtained by changing variables R1, R2, L and beta. Two examples are shown in Fig. 4.

Fig. 4. Elliptical surface equation

Application of Equation Based Surface in Conformal and Non-Planar Antennas

To make use of this function to transfer a planar design to a non-planar design of interest, the following steps can be taken:

  • Start with a planar design. Keep in mind that changing the surface shape can change the characteristics of the antenna. It is a good idea to use a parameterized model, to be able to change and optimize the dimensions after transferring the design on a non-planar surface. As an example we started with a planar meandered line antenna that works around 700MHz, as shown in Fig. 5. The model is excited by a wave port. Since the cylindrical surface will be built around y-axis, the model is transferred to a height to allow the substrate surface to be made (Fig 5. b)
Fig. 5. Planar meandered antenna (a) on xy plane, (b) moved to a height of 5cm
  • Next, using equation based surface, create the desired shape and with the same length as the planar substrate. Make sure that the original deisgn is at a higher location. Select the non-planar surface. Use Modeler->Surface->Thicken Sheet … and thicken the surface with the substrate thickenss. Alternatively, by choosing “Draw” tab, one can expand the Sheet dropdown menu and choose Thicken Sheet. Now select the sheet, change the material to the substrate material.
Fig. 6. Thicken the equation based surface to generate the substrate
  • At this point you are ready to transfer the antenna design to the curved surface. Select both traces of the antenna and the curved substrate (as shown in Fig. 7). Then use Modeler->Surface->Project Sheet…, this will transfer the traces to the curved surface. Please note that the original substrate is still remaining. You need not delete it.
Fig. 7. Steps for transferring the design to the curved surface (a)

Fig. 7. Steps for transferring the design to the curved surface (b)

Fig. 7. Steps for transferring the design to the curved surface (c)
  • Next step is to generate the ground plane and move the wave port. In our example design we have a partial ground plane. For ground plane surface we use the same method to generate an equation based surface. Please keep in mind that the Z coordinate of this surface should be the same as substrate minus the thickness of the substrate. (If you thickened the substrate surface to both sides, this should be the height of substrate minus half of the substrate thickness). Once this sheet is generate assign a Perfect E or Finite Conductivity Boundary (by selecting the surface, right click and Assign Boundary). Delete the old planar ground plane.
Fig. 8. Non-planar meandered antenna with non-planar ground

Wave Port Placement using Equation Based Curve

A new wave port can be defined by the following steps:

  • Delete the old port.
  • Use Draw->Equation Based Curve. Mimicking the equation used for ground plane (Fig. 9).
Fig. 9. Use Equation Based Curve to start a new wave port (a) Equation Based Curve definition window (b) wave pot terminal created using equation based curve and sweep along vector
  • Select the line from the Model tree, select Draw->Sweep->Along Vector. Draw a vector in the direction of port height. Then by selecting the SweepAlongVector from Model tree and double clicking, the window allows you to set the correct size of port height and vector start point (Fig. 10).
  • Assign wave port to this new surface.
Fig. 10. Sweep along vector to create the new wave port location

Similar method can be used to generate (sin)^n or (cos)^n surfaces. Some examples are shown in Fig. 11. Fig. 11 (a) shows how the surface was defined.

Fig. 11. (a) Equation based surface definition using “cos” function, (b), (c), & (d) three different surfaces generated by this equation based surface.

Effect of Curvature on Antenna Matching

Bending a substrate can change the transmission line and antenna impedance. By using equation based port the change in transmission line impedance effect is removed. However, the overall radiation surface is also changed that will have effects on S11. The results of S11 for the planar design, cylindrical design (Fig. 8), cos (Fig. 11 b), and cos^3 (Fig. 11 c) designs are shown in Fig. 12. If it is of interest to include the change in the transmission line impedance, the port should be kept in a rectangular shape.

Fig. 12. Effect of curvature on the resonance frequency.

Equation based curves and surfaces can take a bit of time to get used to but with a little practice these methods can really open the door to some sophisticated geometry. It is also interesting to see how much the geometry can impact a simple antenna design, especially with today’s growing popularity in flex circuitry. Be sure to check out this related webinar  that touches on the impact of packaging antennas as well. If you would like more information on how these tools may be able to help you and your design, please let us know at info@padtinc.com.

You can also click here to download a copy of this example.

Introducing ANSYS 2019 R1

PADT is excited to announce the release of ANSYS 2019 R1, the first group of updates for the suite of ANSYS simulation software this year. The release features updates for a wide variety of applications, including simulation for fluids, structures, electronics, 3D design, and much more.

We will be hosting a series of live webinars over the course of 2019 that will allow you to learn about what’s new in this release, from PADT’s team of expert support engineers.

Take a look at the following upcoming product update webinars for 2019 R1 and register by clicking the links below.

There is more to come, so stay tuned


Fluent Updates in ANSYS 2019 R1
Wednesday, February 13th – 11:00 am – 12:00 pm MST AZ

Computational Fluid Dynamics (CFD) is a tool with amazing flexibility, accuracy and breadth of application. Serious CFD, the kind that provides insights to help you optimize your designs, could be out of reach unless you choose your software carefully. Experienced engineers need to go further and faster with well-validated CFD results across a wide range of applications, and with ANSYS Fluent users are able to do just that; delivering reliable and accurate results.

Join Padt’s CFD Team Lead Engineer, Clinton Smith for a look at what new capabilities are available for the latest version of Fluent, in ANSYS 2019 R1.

Register Here


Mechanical Updates in ANSYS 2019 R1
Wednesday, March 13th – 11:00 am – 12:00 pm MST AZ

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 Mechanical enables engineers of all levels to get answers fast and with confidence. With applications for everything form strength analysis to topology optimization, it’s no wonder this comprehensive suite of tools continues to serve as the flagship mechanical engineering software solution.

Join PADT’s Simulation Support Manager, Ted Harris for a look at what new capabilities are available for ANSYS Mechanical, in the latest version; 2019 R1.

Register Here


High Frequency Electromagnetics Updates in ANSYS 2019 R1
Wednesday, April 10th – 11:00 am – 12:00 pm MST AZ

In today’s world of high performance electronics and advanced electrification systems, the effects of electromagnetic fields on circuits and systems cannot be ignored. ANSYS software can uniquely simulate electromagnetic performance across component, circuit and system design, evaluating temperature, vibration and other critical mechanical effects.

Join PADT’s Electrical Engineer, Michael Griesi for a look at what new capabilities are available with regards to High Frequency Electromagnetics, in the latest version of ANSYS; 2019 R1

Register Here


Discovery Updates in ANSYS 2019 R1
Wednesday, May 8th – 11:00 am – 12:00 pm MST AZ

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.

Join PADT’s Simulation Support Manager, Ted Harris for a look at what exciting new features are available for design engineers in both Discovery Live and AIM, in ANSYS 2019 R1.

Register Here


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All Things ANSYS 024 – An Interview with TriLumina on using ANSYS to help develop LiDAR components

 

Published on: November 5th, 2018
With: Eric Miller & Jeff Earls
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Jeff Earls of TriLumina for a discussion on how they use ANSYS simulation tools on their disruptive new design for laser arrays used in LiDAR applications, such as autonomous vehicles. All that, followed by an update on news and events in the respective worlds of ANSYS and PADT.

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!

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Press Release: On-Demand High Performance Cloud Computing for Today’s Most Demanding Workflows Offered by PADT in Partnership with Nimbix

Sometimes if you look hard enough, you find what you need.  Ever since people started talking about running large simulation models in the “cloud” our engineers here at PADT have been looking for a good solution. We even offered our own service, and although it was fast and easy to use, we could not make it big enough. Then along comes Nimbix. And they nailed it.

First we asked, are they running on real HPC hardware, not souped up web servers with virtual machines on them? Yes. We found bare metal hardware striped down to fighting weight that is optimized for running simulation.

Then we asked, Is it easy to use or do I have to jump through hoops to just get a job running?  Indeed it was easy and powerful. We just wanted to submit jobs and found that the JARVICE™ interface does so much more to configure, setup, submit, and monitor even the most complex parallel solves.

OK, this looked good, but the real test is do we have access to whole machines or is this just a timesharing system with a great interface?  Turns out it is not.  It is true cloud-based high-performance computing.

We think we found what we were looking for, but we have been burned before.  So we did what any simulation engineer would do, we pounded on it with massive ANSYS FLUENT, Mechanical, and HFSS runs.  We knew it was real when we were monitoring a CFD run on a mobile phone from a coffee shop in-between customer visits.

We liked it so much that we asked if we could partner with them to offer it to our customers. And that is where we are today with this press release.

PADT is proud to announce that we are able to provide our customers with on-demand HPC resources that are fast, easy to use, and cost-effective.  If you are a simulation user who needs to run large jobs, access surge capacity, or just solve faster, Nimbix is the answer. And PADT is here to help determine the right configuration and guide you through the process.

Please read the press release below for more details on what we are doing.

This video explains Nimbix well:

You can also learn more about our experience by watching a free webinar we recorded where we shared our experience using this service: www.brighttalk.com/webcast/15747/330189

Please find the official press release on this new partnership below and here in PDF and HTML

If you have any questions about high-performance computing for simulation, either with local hardware or compute resources in the cloud, reach out to info@padtinc.com or call 480.813.4884.

Press Release:

On-Demand High Performance Cloud Computing for Today’s
Most Demanding Workflows Offered by PADT in Partnership with Nimbix

Nimbix Cloud Provides Access to ANSYS Applications Through Cloud Supercomputing, Removing the Requirement of Expensive Infrastructure and Hardware

TEMPE, Ariz., July 26, 2018 ─ Understanding the need for easy, affordable and reliable access to supercomputing capabilities, PADT has partnered with Nimbix to provide High-Performance Computing (HPC) in the cloud. The Nimbix Cloud is the industry’s first true Software-as-a-Service (SaaS) supercomputing cloud and will provide PADT’s customers with on-demand, mobile access to more than 15 ANSYS applications without needing expensive hardware and complex infrastructure to support it. Current ANSYS applications available with Nimbix are listed here.

“As long-time simulation users, PADT has always wanted to leverage the cloud for running ANSYS models, but no one could support it before Nimbix,” said Bob Calvin, manager, Simulation Solutions, PADT. “The Nimbix Cloud gives our customers a flexible way to run large models from anywhere and accommodate surge capacity when it is needed.”

The Nimbix Cloud is powered by the JARVICE™ platform, which Nimbix purpose-built from the ground-up to accommodate the most demanding workflows by providing superior performance, capabilities, and ease of use. PADT customers can manage start-up, execution, completion and notifications on full-featured ANSYS applications in the cloud, and send their data from any device including desktop, tablet and smartphones.

“With its global recognition as a provider of numerical simulation and product development, PADT is the ideal partner to leverage the JARVICE platform internally and as an offering to its customers,” said Chuck Kelly, senior vice president, Sales, Nimbix. “Nimbix high-performance computing in the cloud provides a competitive advantage by allowing users to more easily solve complex design problems and then send the data anywhere to turn results into actionable insights.”

“We use Nimbix frequently at PADT because of its reliability and performance when running ANSYS software in the cloud,” said Manoj Mahendran, lead application engineer, PADT. “The platform has allowed us to easily check and submit simulation test results off-site and on-the-go, providing more flexibility to our simulation teams.”

Nimbix Cloud is available today, and PADT invites customers to learn more about the challenges, tools, and mindset needed to run simulation in the cloud, and how the Nimbix platform can be an effective solution, in a webinar on August 8, 2018. To register for the webinar, please visit https://www.brighttalk.com/webcast/15747/330189.

About Phoenix Analysis and Design Technologies

Phoenix Analysis and Design Technologies, Inc. (PADT) is an engineering product and services company that focuses on helping customers who develop physical products by providing Numerical Simulation, Product Development, and 3D Printing solutions. PADT’s worldwide reputation for technical excellence and experienced staff is based on its proven record of building long-term win-win partnerships with vendors and customers. Since its establishment in 1994, companies have relied on PADT because “We Make Innovation Work.” With over 80 employees, PADT services customers from its headquarters at the Arizona State University Research Park in Tempe, Arizona, and from offices in Torrance, California, Littleton, Colorado, Albuquerque, New Mexico, Austin, Texas, and Murray, Utah, as well as through staff members located around the country. More information on PADT can be found at www.PADTINC.com.

# # #

Media Contact
Alec Robertson
TechTHiNQ on behalf of PADT
585-281-6399
alec.robertson@techthinq.com
PADT Contact
Eric Miller
PADT, Inc.
Principal & Co-Owner
480.813.4884
eric.miller@padtinc.com

 

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IEEE Day 2017: Smart Antennas for IoT and 5G

IEEE Day celebrates the first time in history when engineers worldwide and IEEE members gathered to share their technical ideas in 1884. Events were held around the world by 846 IEEE Chapters this year. So, to celebrate, I attended a joint chapter meeting in at The Museum of Flight in Seattle with technical presentations focused on “Smart Antennas for IoT and 5G”. There were approximately 60 in attendance, so assuming this was the average attendance globally results in over 50,000 engineers celebrating IEEE Day worldwide!

The Seattle seminar featured three speakers that spanned theory, design, test, integration, and application of smart antennas. There was much discussion about the complexity and challenges of meeting the ambitious goals of 5G, which extend beyond mobile broadband data access. Some key objectives of 5G are to increase capacity, increase data rates, reduce latency, increase availability, and improve spectral and energy efficiency by 2020. A critical technology behind achieving these goals is beamforming antenna arrays, which were at the forefront of each presentation.

Anil Kumar from Boeing focused on the application of mmWave technology on aircraft. Test data was used to analyze EM radiation leakage through coated and uncoated aircraft windows. However, since existing regulations don’t consider the increased path loss associated with such high frequencies, the integration of 5G wireless applications may be restricted or delayed. Beyond this regulatory challenge, Anil discussed how multipath reflectors and absorbers will present significant challenges to successful integration inside the cabin. Although testing is always required for validation, designing the layout of the onboard transceivers may be impractical to optimize without an asymptotic EM simulation tool that can account for creeping waves, diffraction, and multi-bounce.

Considering the test and measurement perspective, Jari Vikstedt from ETS-Lindgren focused on the challenges of testing smart antenna systems. Smart or adaptive antenna systems will not likely perform the same in an anechoic chamber test as they would in real systems. Of particular difficulty, radiation null placement is just as critical as beam placement. This poses a difficult challenge to the number and location of probes in a test environment. Not only would a large number of probes become impractical, there is significant shadowing at mmWave frequencies which can negatively impact the measurement. Furthermore, compact ranges can significantly impact testing and line of sight measurements become particularly challenging. While not a purely test-oriented observation, this lead to considering the challenge of tower hand off. If a handset and tower use beamforming to maintain a link, if is difficult for an approaching tower to even sense the handset to negotiate the hand-off.

In contrast, if the handset was continuously scanning, the approaching tower could be sensed to negotiate the hand-off before the link is jeopardized.

The keynote speaker, who also traveled from Phoenix to Seattle, was ASU Professor Dr. Constantine Balanis. Dr. Balanis opened his presentation by making a distinction between conventional “dumb antennas” and “smart antennas”. In reality, there are no smart antennas, but instead smart antenna systems. This is a critical point from an engineering perspective since it highlights the complexity and challenge of designing modern communication systems. The focus of his presentation was using an adaptive system to steer null points in addition to the beam in an antenna array using a least mean square (LMS) algorithm. He began with a simple linear patch array with fixed uniform amplitude weights, since an analytic solution was practical and could be used to validate a simulation setup. However, once the simulation results were verified for confidence, designing a more complex array with weighted amplitudes accompanying the element phase shift was only practical through simulation. While beam steering will create a device centric system by targeting individual users on massive multiple input multiple output (MIMO) networks, null steering can improve efficiency by minimizing interference to other devices.

Whether spatial processing is truly the “last frontier in the battle for cellular system capacity”, 5G technology will most certainly usher in a new era of high capacity, high speed, efficient, and ubiquitous means of communication. If you would like to learn more about how PADT approaches antenna simulation, you can read about it here and contact us directly at info@padtinc.com.