ANSYS Discovery Live: A Focus on Topology Optimization

For those who are not already familiar with it, Discovery Live is a rapid design tool that shares the Discovery SpaceClaim environment. It is capable of near real-time simulation of basic structural, modal, fluid, electronic, and thermal problems. This is done through leveraging the computational power of a dedicated GPU, though because of the required speed it will necessarily have somewhat less fidelity than the corresponding full Ansys analyses. Even so, the ability to immediately see the effects of modifying, adding, or rearranging geometry through SpaceClaim’s operations provides a tremendous value to designers.

One of the most interesting features within Discovery Live is the ability to perform Topology Optimization for reducing the quantity of material in a design while maintaining optimal stiffness for a designated loading condition. This can be particularly appealing given the rapid adoption of 3D printing and other additive manufacturing techniques where reducing the total material used saves both time and material cost. These also allow the production of complex organic shapes that were not always feasible with more traditional techniques like milling.

With these things in mind, we have recently received requests to demonstrate Discovery Live’s capabilities and provide some training in its use, especially for topology optimization. Given that Discovery Live is amazingly straightforward in its application, this also seems like an ideal topic to expand on in blog form alongside our general Discovery Live workshops!

For this example, we have chosen to work with a generic “engine mount” geometry that was saved in .stp format. The overall dimensions are about 10 cm wide x 5 cm tall x 5 cm deep, and we assume it is made out of stainless steel (though this is not terribly important for this demonstration).

Figure 1: Starting engine mount geometry with fixed supports and a defined load.

The three bolt holes around the perimeter are fixed in position, as if they were firmly clamped to a surface, while a total load of 9069 N (-9000 N in X, 1000 N in Y, and 500 N in Z) is applied to the cylindrical surfaces on the front. From here, we simply tell Discovery Live that we would like to add a topology optimization calculation onto our structural analysis. This opens up the ability to specify a couple more options: the way we define how much material to remove and the amount of material around boundary conditions to preserve. For removing material, we can choose to either reduce the total volume by a percent of the original or to remove material until we reach a specific model volume. For the area around boundary conditions, this is an “inflation” length measured as a normal distance from these surfaces, easily visualizable when highlighting the condition on the solution tree.

Figure 2: Inflation zone shown around each fixed support and load surface.

Since I have already planned out what kind of comparisons I want to make in this analysis, I chose to set the final model volume to 30 cm3. After hitting the simulate button, we get to watch the optimization happen alongside a rough structural analysis. By default, we are provided with a result chart showing the model’s volume, which pretty quickly converges on our target volume. As with any analysis, the duration of this process is fairly sensitive to the fidelity specified, but with default settings this took all of 7 minutes and 50 seconds to complete on my desktop with a Quadro K4000.

Figure 3: Mid-optimization on the top, post-optimization on the bottom.

Once optimization is complete, there are several more operations that become available. In order to gain access to the optimized structure, we need to convert it into a model body. Both options for this result in faceted bodies with the click of a button located in the solution tree; the difference is just that the second has also had a smoothing operation applied to it. One or the other may be preferable, depending on your application.

Figure 4: Converting results to faceted geometry

Text Box: Figure 5: Faceted body post-optimization.

Figure 5: Faceted body post-optimization

Figure 6: Smoothed faceted body post-optimization

Though some rough stress calculations were made throughout the optimization process, the next step is typically a validation. Discovery Live makes this as a simple procedure as right-clicking on the optimized result in the solution tree and selecting the “Create Validation Solution” button. This essentially copies over the newly generated geometry into a new structural analysis while preserving the previously applied supports and loads. This allows for finer control over the fidelity of our validation, but still a very fast confirmation of our results. Using maximum fidelity on our faceted body, we find that the resulting maximum stress is about 360 MPa as compared to our unoptimized structure’s stress of 267 MPa, though of course our new material volume is less than half the original.

Figure 7: Optimized structure validation. Example surfaces that are untouched by optimization are boxed.

It may be that our final stress value is higher than what we find acceptable. At this point, it is important to note one of the limitations in version 2019R3: Discovery Live can only remove material from the original geometry, it does not add. What this means is that any surfaces remaining unchanged throughout the process are important in maintaining structural integrity for the specified load. So, if we really want to optimize our structure, we should start with additional material in these regions to allow for more optimization flexibility.

In this case, we can go back to our original engine mount model in Discovery Live and use the integrated SpaceClaim tools to thicken our backplate and expand the fillets around the load surfaces.

Figure 8: Modified engine mount geometry with a thicker backplate and larger fillets.

We can then run back through the same analysis, specifying the same target volume, to improve the performance of our final component. Indeed, we find that after optimizing back down to a material volume of 30 cm3, our new maximum stress has been decreased to 256 MPa. Keep in mind that this is very doable within Discovery Live, as the entire modification and simulation process can be done in <10 minutes for this model.

Figure 9: Validated results from the modified geometry post-optimization.

Of course, once a promising solution has been attained in Discovery Live, we should then export the model to run a more thorough analysis of in Ansys Mechanical, but hopefully, this provides a useful example of how to leverage this amazing tool!

One final comment is that while this example was performed in the 2019R3 version, 2020R1 has expanded Discovery Live’s optimization capability somewhat. Instead of only being allowed to specify a target volume or percent reduction, you can choose to allow a specified increase in structure compliance while minimizing the volume. In addition to this, there are a couple more knobs to turn for better control over the manufacturability of the result, such as specifying the maximum thickness of any region and preventing any internal overhangs in a specified direction. It is now also possible to link topology optimization to a general-purpose modal analysis, either on its own or coupled to a structural analysis. These continued improvements are great news for users, and we hope that even more features continue to roll out.

All Things Ansys 058: Combining Mechanical Simulation with Additive Manufacturing

 

Published on: March 9th, 2020
With: Eric Miller, Matt Humrick & Pam Waterman
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by 3D Printing Applications Engineer Pamela Waterman and Advatech Pacific’s Engineering Manager Matt Humrick for a discussion on real world applications for topology optimization, and it’s value when it comes to creating parts though additive manufacturing.

If you would like to learn more about this topic and what Advatech Pacific is doing, you can download our case study covering these topics here: https://bit.ly/38Bqu2b

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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Icepak in Ansys Electronic Desktop – Why should you know about it?

The role of Ansys Electronics Desktop Icepak (hereafter referred to as Icepak, not to be confused with Classic Icepak) is in an interesting place. On the back end, it is a tremendously capable CFD solver through the use of the Ansys Fluent code. On the front end, it is an all-in-one pre and post processor that is streamlined for electronics thermal management, including the explicit simulation and effects of fluid convection. In this regard, Icepak can be thought of as a system level Multiphysics simulation tool.

One of the advantages of Icepak is in its interface consistency with the rest of the Electronic Desktop (EDT) products. This not only results in a slick modern appearance but also provides a very familiar environment for the electrical engineers and designers who typically use the other EDT tools. While they may not already be intimately familiar with the physics and setup process for CFD/thermal simulations, being able to follow a very similar workflow certainly lowers the barrier to entry for accessing useful results. Even if complete adoption by these users is not practical, this same environment can serve as a happy medium for collaboration with thermal and fluids experts.

Figure 1: AEDT Icepak interface. The same ribbon menus, project manager, history tree, and display window as other EDT products.

So, beyond these generalities, what does Icepak actually offer for an optimized user experience over other tools, and what kinds of problems/applications are best suited for it?

The first thing that comes to mind for both of these questions is a PCB with attached components. In a real-world environment, anyone that has looked at the inside of a computer is likely familiar with motherboards covered with all kinds of little chips and capacitors and often dominated by a CPU mounted with a heatsink and fan. In most cases, this motherboard is enclosed within some kind of box (a computer case) with vents/filters/fans on at least some of the sides to facilitate controlled airflow. This is an ideal scenario for Icepak. The geometries of the board and its components are typically well represented by rectangular prisms and cylinders, and the thermal management of the system is strongly related to the physics of conjugate heat transfer. For the case geometry, it may be more convenient to import this from a more comprehensive modeler like SpaceClaim and then take advantage of the tools built into Icepak to quickly process the important features.

Figure 2: A computer case with motherboard imported from SpaceClaim. The front and back have vents/fans while the side has a rectangular patterned grille.

For a CAD model like the one above, we may want to include some additional items like heatsinks, fan models, or simple PCB components. Icepak’s geometry tools include some very convenient parameterized functions for quickly constructing and positioning fans and heatsinks, in addition to the basic ability to create and manipulate simple volumes. There are also routines for extracting openings on surface, such as the rectangular vent arrays on the front and back as well as the patterned grille on the side. So, not only can you import detailed CAD from external sources, you can mix, match, and simplify it with Icepak’s geometry, which streamlines the entire design and setup process. For an experienced user, the above model can be prepared for a basic simulation within just a matter of minutes. The resulting configuration with an added heatsink, some RAM, and boundary conditions, could look something like this:

Figure 3: The model from Figure 2 after Icepak processing. Boundary conditions for the fans, vents, and grille have been defined. Icepak primitives have also been added in the form of a heatsink and RAM modules.

Monitor points can then assigned to surfaces or bodies as desired; chances are that for a simulation like this, temperature within the CPU is the most important. Additional temperature points for each RAM module or flow measurements for the fans and openings can also be defined. These points can all be tracked as the simulation proceeds to ensure that convergence is actually attained.

Figure 4: Monitoring chosen solution variables to ensure convergence.

For this simple system containing a 20 W CPU and 8 RAM modules at 2 W each, quite a few of our components are toasty and potentially problematic from a thermal standpoint.

Figure 5: Post-processing with Icepak. Temperature contours are overlaid with flow velocities to better understand the behavior of the system.

With the power of a simulation environment in Icepak at our fingertips, we can now play around with our design parameters to improve the thermal management of this system! Want to see what happens when you block the outlet vents? Easy, select and delete them! Want to use a more powerful fan or try a new material for the motherboard or heatsink? Just edit their properties in the history tree. Want to spin around the board or try changing the number of fins on the heatsink? Also straightforward, although you will have to remesh the model. While these are the kinds of things that are certainly possible in other tools, they are exceptionally easy to do within an all-in-one interface like Icepak.

The physics involved in this example are pretty standard: solid body conduction with conjugate heat transfer to a turbulent K-Omega fluid model. Where Icepak really shines is its ability to integrate with the other tools in the EDT environment. While we assumed that the motherboard was nothing more than a solid chunk of FR-4, this board could have been designed and simulated in detail with another tool like HFSS. The board, along with all of the power losses calculated during the HFSS analysis, could have then been directly imported into the Icepak project. This would allow for each layer to be modeled with its own spatially varying thermal properties according to trace locations as well as a very accurate spatial mapping of heat generation.

This is not at all to say that Icepak is limited to these kinds of PCB and CCA examples. These just often tend to be convenient to think about and relatively easy to geometrically represent. Using Fluent as the solver provides a lot of flexibility, and there are many more classes of problems that could be benefit from Icepak. On the low frequency side, electric motors are a good example of a problem where electronic and thermal behavior are intertwined. As voltage is applied to the windings, currents are induced and heat is generated. For larger motors, these currents, and consequently the associated thermal losses, can be significant. Maxwell is used to model the electronic side for these types of problems, where the results can then be easily brought into an Icepak simulation. I have gone through just such an example rotor/stator/winding motor assembly model in Maxwell, where I then copied everything into an Iecpak project to simulate the resulting steady temperature profile in a box of naturally convecting air.

Figure 6: An example half-motor that was solved in Maxwell as a magnetostatic problem and then copied over to Icepak for thermal analysis.

If it is found that better thermal management is needed, then extra features could then be added on the Icepak side as desired, such as a dedicated heatsink or external fan. Only the components with loads mapped over from Maxwell need to remain unmodified.

On the high frequency side, you may care about the performance of an antenna. HFSS can be used for the electromagnetic side, while Icepak can once again be brought in to analyze the thermal behavior. For high powered antenna, some components could very easily get hot enough for the material properties to appreciably change and for thermal radiation to become a dominant mode of heat transport. A 2-way automatic Icepak coupling is an excellent way to model this. Thermal modifiers may be defined for material properties in HFSS, and radiation is a supported physics model in Icepak. HFSS and Icepak can then be set up to alternately solve and automatically feed each other new loads and boundary conditions until a converged result is attained.

What all of this really comes down to is the question: how easy is it for the user to set up a model that will produce the information they need? For these kinds of electronics questions, I believe the answer for Icepak is “extraordinarily easy”. While functional on its own merit, Icepak really shines when it comes to the ease of coupling thermal management analysis with the EM family of tools.

3D Design Updates in ANSYS 2020 R1 – Webinar

The ANSYS Discovery 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. With ANSYS you can explore ideas, iterate, and innovate with unprecedented speed early in your design process. Delve deeper into design details, refine concepts and perform multiple physics simulations — backed by ANSYS solvers — to better account for real-world behaviors.

Capabilities in this tool-set allow engineers to increase speed and reduce costs from the start of the design cycle, all the way to product launch. Improve engineering productivity and accelerate development time, create higher-quality products while reducing development & manufacturing costs, and respond quickly to changing customer demands while bringing new products to market faster than the competition.

Join PADT’s Training & Support Application Engineer, Robert McCathren for a look at whats new & improved when it comes to these tools in ANSYS 2020 R1. This update includes new releases for ANSYS Discovery Live, AIM, and SpaceClaim, focusing on areas including:

  • Simulation of Thin Parts
  • Topology Optimization in Discovery Live
  • Structural Material Properties
  • Physics Aware Meshing
  • Beam and Shell Modeling
  • And much more

Register Here

All Things ANSYS 057: Simulation for Additive Manufacturing in ANSYS 2020 R1

 

Published on: February 24th, 2020
With: Eric Miller & Doug Oatis
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Lead Mechanical Engineer Doug Oatis for a discussion on the latest advancements in simulation for additive manufacturing and topology optimization in ANSYS 2020 R1.

If you would like to learn more about what this release is capable of, check out our webinar on the topic here:

https://www.brighttalk.com/webcast/15747/384528

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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Additive Manufacturing & Topology Optimization in ANSYS 2020 R1 – Webinar

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

ANSYS additive solutions continue to evolve at a rapid pace. A variety of new enhancements and features come as part of ANSYS 2020 R1, including the ability to work with EOS printers, using the inherent strain approach in ANSYS Workbench Additive, and new materials in ANSYS Additive Print and Science.

Join PADT’s Lead Mechanical Engineer Doug Oatis for an exploration of the ANSYS tools that help to optimize additive manufacturing, and what new capabilities are available for them when upgrading to ANSYS 2020 R1. This presentation includes updates regarding:

  • Level-set topology optimization
  • Density based topology optimization
  • Inherent strain method in workbench Additive
  • Improved supports in Additive Prep
  • Additive Wizard update
  • And much more

Register Here

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

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

All Things ANSYS 056: A Unique Perspective on a Unique Solution – PADT Sales Talks ANSYS Applications

 

Published on: February 10th, 2020
With: Eric Miller, Bob Calvin, Dan Christensen, Brian Benbow, Heather Dean, Ian Scott & Will Kruspe
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Bob Calvin, Dan Christensen, Brian Benbow, Heather Dean, Ian Scott, and Will Kruspe from PADT’s ANSYS sales team to discuss the benefits they see in ANSYS as a solution for their unique customer bases, as well as for manufacturers and engineers as a whole. With a combination of technical know-how and knowledge of positioning within different industries, the PADT sales team shares a unique perspective on the value of the various tools that make up the ANSYS suite and how users can best take advantage of them in order to help them succeed.

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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All Things ANSYS 055: Introducing ANSYS 2020

 

Published on: February 3rd, 2020
With: Eric Miller, Josh Stout, Sina Gohds, Ted Harris & Tom Chadwick
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Josh Stout, Sina Gohds, Ted Harris, and Tom Chadwick from the simulation support team to discuss their thoughts on ANSYS 2020 R1, and what specific capabilities they are excited about exploring after attending the annual ANSYS sales kickoff in Florida.

This new release covers updates for the entirety of the ANSYS suite of tools, so there is a lot to talk about.

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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Fluent Updates in ANSYS 2020 R1 – Webinar

Computational fluid dynamics (CFD) can be challenging for a multitude of reasons, but not with ANSYS Fluent. Anyone can get great CFD simulation results with ANSYS solutions. Fluent software contains the broad, physical modeling capabilities needed to model flow, turbulence, heat transfer and reactions for industrial applications. These range from air flow over an aircraft wing to combustion in a furnace, from bubble columns to oil platforms, from blood flow to semiconductor manufacturing and from clean room design to wastewater treatment plants.

Fluent spans an expansive range, including special models, with capabilities to model in-cylinder combustion, aero-acoustics, turbomachinery and multiphase systems. The latest innovations and updates simplify and speed setup and meshing while adding even more accurate physical models. The outcome: great results, without compromise.

Join PADT’s Senior CFD & FEA Application Engineer, Sina Ghods, for a look at what’s new and improved in this latest version of ANSYS Fluent, including:

  • User Interface/Graphics
  • Meshing Workflows
  • Multi-phase Robustness
  • Solver Enhancements
  • And much more

Register Here

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

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

Mechanical Updates in ANSYS 2020 R1 – Webinar

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

Once again, ANSYS is able to cement their role as industry leaders when it comes to usability, productivity, and reliability; adding innovative functionality to an already groundbreaking product offering. ANSYS Mechanical continues to be used throughout the industry, and for good reason as it enables engineers to optimize their product design and reduce the costs of physical testing.

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

  • External Modeling
  • Graphics
  • Composites
  • Linear Dynamics
  • And much more

Register Here

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

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

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.

All Thing ANSYS 054: Talking CFD – Discussion on the Current State of Computational Fluid Dynamics with Robin Knowles

 

Published on: January 13th, 2020
With: Eric Miller & Robin Knowles
Description:  

In this episode we are excited to share an interview done with host and Co-Founder of PADT, Eric Miller and host of the Talking CFD podcast Robin Knowles, regarding the history of PADT’s use of simulation technology as a whole, and the current state of all things CFD.

If you would like to hear more of Robin’s interviews with various other CFD based companies both small and large, you can listen at https://www.cfdengine.com/podcast/.

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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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.

Getting Bulk Properties for Repeated Structures in ANSYS Mechanical with Material Designer

Using Material Designer To Perform Homogenization Studies

Editor’s Note:

3D Printing and other advanced manufacturing methods are driving the increased use of lattice-type structures in structural designs. This is great for reducing mass and increasing the stiffness of components but can be a real pain for those of us doing simulation. Modeling all of those tiny features across a part is difficult to mesh and takes forever to solve.

PADT has been doing a bit of R&D in this area recently, including a recent PHASE II NASA STTR with ASU and KSU. We see a lot of potential in combining generative design and 3D Printing to drive better structures. The key to this effort is efficient and accurate simulation.

The good news is that we do not have to model every unit cell. Instead, we can do some simulation on a single representative chunk and use the ANSYS Material Designer feature to create an approximate material property that we can use to represent the lattice volume as a homogeneous material.

In the post below, PADT’s Alex Grishin explains it all with theory, examples, and a clear step-by-step process that you can use for your lattice filled geometry.

PADT-ANSYS-Lattice-Material_Homogenization

All Things ANSYS 053: 2019 Wrap-up & Predictions for ANSYS in the New Year

 

Published on: December 20th, 2019
With: Eric Miller, Tom Chadwick, Ted Harris, Sina Ghods & Ahmed Fayad
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Simulation Support Team, including Tom Chadwick, Ted Harris, Sina Ghods, and Ahmed Fayad for a round-table discussion of their favorite ANSYS features released in 2019, along with predictions on what has yet to come.

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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