Making Sense of DC IR Results in Ansys SIwave

In this article I will cover a Voltage Drop (DC IR) simulation in SIwave, applying realistic power delivery setup on a simple 4-layer PCB design. The main goal for this project is to understand what data we receive by running DC IR simulation, how to verify it, and what is the best way of using it.

And before I open my tools and start diving deep into this topic, I would like to thank Zachary Donathan for asking the right questions and having deep meaningful technical discussions with me on some related subjects. He may not have known, but he was helping me to shape up this article in my head!

Design Setup

There are many different power nets present on the board under test, however I will be focusing on two widely spread nets +1.2V and +3.3V. Both nets are being supplied through Voltage Regulator Module (VRM), which will be assigned as a Voltage Source in our analysis. After careful assessment of the board design, I identified the most critical components for the power delivery to include in the analysis as Current Sources (also known as ‘sinks’). Two DRAM small outline integrated circuit (SOIC) components D1 and D2 are supplied with +1.2V. While power net +3.3V provides voltage to two quad flat package (QFP) microcontrollers U20 and U21, mini PCIE connector, and hex Schmitt-Trigger inverter U1.

Fig. 1. Power Delivery Network setting for a DC IR analysis

Figure 1 shows the ‘floor plan’ of the DC IR analysis setup with 1.2V voltage path highlighted in yellow and 3.3V path highlighted in light blue.

Before we assign any Voltage and Current sources, we need to define pin groups for all nets +1.2V, +3.3V and GND for all PDN component mentioned above. Having pin groups will significantly simplify the reviewing process of the results. Also, it is generally a good practice to start the DC IR analysis from the ‘big picture’ to understand if certain component gets enough power from the VRM. If a given IC reports an acceptable level of voltage being delivered with a good margin, then we don’t need to dig deeper; we can instead focus on those which may not have good enough margins.

Once we have created all necessary pin groups, we can assign voltage and current sources. There are several ways of doing that (using wizard or manual), for this project we will use ‘Generate Circuit Element on Components’ feature to manually define all sources. Knowing all the components and having pin groups already created makes the assignment very straight-forward. All current sources draw different amount of current, as indicated in our setting, however all current sources have the same Parasitic Resistance (very large value) and all voltage source also have the same Parasitic Resistance (very small value). This is shown on Figure 2 and Figure 3.

Note: The type of the current source ‘Constant Voltage’ or ‘Distributed Current’ matters only if you are assigning a current source to a component with multiple pins on the same net, and since in this project we are working with pins groups, this setting doesn’t make difference in final results.

Fig. 2. Voltage and Current sources assigned
Fig. 3. Parasitic Resistance assignments for all voltage and current sources

For each power net we have created a voltage source on VRM and multiple current sources on ICs and the connector. All sources have a negative node on a GND net, so we have a good common return path. And in addition, we have assigned a negative node of both voltage sources (one for +1.2V and one for +3.3V) as our reference points for our analysis. So reported voltage values will be referenced to that that node as absolute 0V.

At this point, the DC IR setup is complete and ready for simulation.

Results overview and validation

When the DC IR simulation is finished, there is large amount of data being generated, therefore there are different ways of viewing results, all options are presented on Figure 4. In this article I will be primarily focusing on ‘Power Tree’ and ‘Element Data’. As an additional source if validation we may review the currents and voltages overlaying the design to help us to visualize the current flow and power distribution. Most of the time this helps to understand if our assumption of pin grouping is accurate.

Fig. 4. Options to view different aspects of DC IR simulated data

Power Tree

First let’s look at the Power Tree, presented on Figure 5. Two different power nets were simulated, +1.2V and +3.3V, each of which has specified Current Sources where the power gets delivered. Therefore, when we analyze DC IR results in the Power tree format, we see two ‘trees’, one for each power net. Since we don’t have any pins, which would get both 1.2V and 3.3V at the same time (not very physical example), we don’t have ‘common branches’ on these two ‘trees’.

Now, let’s dissect all the information present in this power tree (taking in consideration only one ‘branch’ for simplicity, although the logic is applicable for all ‘branches’):

  • We were treating both power nets +1.2V and +3.3V as separate voltage loops, so we have assigned negative nodes of each Voltage Source as a reference point. Therefore, we see the ‘GND’ symbol ((1) and (2)) for each voltage source. Now all voltage calculations will be referenced to that node as 0V for its specific tree.
  • Then we see the path from Voltage Source to Current Source, the value ΔV shows the Voltage Drop in that path (3). Ultimately, this is the main value power engineers usually are interested in during this type of analysis. If we subtract ΔV from Vout we will get the ‘Actual Voltage’ delivered to the specific current source positive pin (1.2V – 0.22246V = 0.977V). That value reported in the box for the Current Source (4). Technically, the same voltage drop value is reported in the column ‘IR Drop’, but in this column we get more details – we see what the percentage of the Vout is being dropped. Engineers usually specify the margin value of the acceptable voltage drop as a percentage of Vout, and in our experiment we have specified 15%, as reported in column ‘Specification’. And we see that 18.5% is greater than 15%, therefore we get ‘Fail_I_&_V’ results (6) for that Current Source.
  • Regarding the current – we have manually specified the current value for each Current Source. Current values in Figure 2 are the same as in Figure 5. Also, we can specify the margin for the current to report pass or fail. In our example we assigned 108A as a current at the Current Source (5), while 100A is our current limit (4). Therefore, we also got failed results for the current as well.
  • As mentioned earlier, we assigned current values for each Current Source, but we didn’t set any current values for the Voltage Source. This is because the tool calculates how much current needs to be assigned for the Voltage Source, based on the value at the Current Sources. In our case we have 3 Current Sources 108A, 63A, 63A (5). The sum of these three values is 234A, which is reported as a current at the Voltage Source (7). Later we will see that this value is being used to calculate output power at the Voltage Source.  
Fig. 5. DC IR simulated data viewed as a ‘Power Tree’

Element Data

This option shows us results in the tabular representation. It lists many important calculated data points for specific objects, such as bondwire, current sources, all vias associated with the power distribution network, voltage probes, voltage sources.

Let’s continue reviewing the same power net +1.2V and the power distribution to CPU1 component as we have done for Power Tree (Figure 5). The same way we will be going over the details in point-by-point approach:

  • First and foremost, when we look at the information for Current Sources, we see a ‘Voltage’ value, which may be confusing. The value reported in this table is 0.7247V (8), which is different from the reported value of 0.977V in Power Tree on Figure 5 (4). The reason for the difference is that reported voltage value were calculated at different locations. As mentioned earlier, the reported voltage in the Power Tree is the voltage at the positive pin of the Current Source. The voltage reported in Element Data is the voltage at the negative pin of the Current Source, which doesn’t include the voltage drop across the ground plane of the return path.

To verify the reported voltage values, we can place Voltage Probes (under circuit elements). Once we do that, we will need to rerun the simulation in order to get the results for the probes:

  1. Two terminals of the ‘VPROBE_1’ attached at the positive pin of Voltage Source and at the positive pin of the Current Source. This probe should show us the voltage difference between VRM and IC, which also the same as reported Voltage Drop ΔV. And as we can see ‘VPROBE_1’ = 222.4637mV (13), when ΔV = 222.464mV (3). Correlated perfectly!
  2. Two terminals of the ‘VPROBE_GND’ attached to the negative pin of the Current Source and negative pin of the Voltage Source. The voltage shown by this probe is the voltage drop across the ground plane.

If we have 1.2V at the positive pin of VRM, then voltage drops 222.464mV across the power plane, so the positive pin of IC gets supplied with 0.977V. Then the voltage at the Current Source 0.724827V (8) being drawn, leaving us with (1.2V – 0.222464V – 0.724827V) = 0.252709V at the negative pin of the Current Source. On the return path the voltage drops again across the ground plane 252.4749mV (14) delivering back at the negative pin of VRM (0.252709V – 0.252475V) = 234uV. This is the internal voltage drop in the Voltage Source, as calculated as output current at VRM 234A (7) multiplied by Parasitic Resistance 1E-6Ohm (Figure 3) at VRM. This is Series R Voltage (11)

  • Parallel R Current of the Current source is calculated as Voltage 724.82mV (8) divided by Parasitic Resistance of the Current Source (Figure 3) 5E+7 Ohm = 1.44965E-8 (9)
  • Current of the Voltage Source report in the Element Data 234A (10) is the same value as reported in the Power Tree (sum of all currents of Current Sources for the +1.2V power net) = 234A (7). Knowing this value of the current we can multiple it by Parasitic Resistance of the Voltage Source (Figure 3) 1E-6 Ohm = (234A * 1E-6Ohm) = 234E-6V, which is equal to reported Series R Voltage (11). And considering that the 234A is the output current of the Voltage Source, we can multiple it by output voltage Vout = 1.2V to get a Power Output = (234A * 1.2V) = 280.85W (12)
Fig. 6. DC IR simulated data viewed in the table format as ‘Element Data’

In addition to all provided above calculations and explanations, the video below in Figure 7 highlights all the key points of this article.

Fig. 7. Difference between reporting Voltage values in Power Tree and Element Data

Conclusion

By carefully reviewing the Power Tree and Element Data reporting options, we can determine many important decisions about the power delivery network quality, such as how much voltage gets delivered to the Current Source; how much voltage drop is on the power net and on the ground net, etc. More valuable information can be extracted from other DC IR results options, such as ‘Loop Resistance’, ‘Path Resistance’, ‘RL table’, ‘Spice Netlist’, full ‘Report’. However, all these features deserve a separate topic.

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

Bring Your Simulation Home with Ansys Cloud Solutions – Webinar

Engineering simulation has long been constrained by fixed computing resources available on a desktop or cluster. Today, however, cloud computing can deliver the on-demand, high performance computing (HPC) capacity required for faster high-fidelity results offering greater performance insight, all from the comfort of your home.

Ansys Cloud delivers the speed, power and compute capacity of cloud computing directly to your desktop — when and where you need it. You can run larger, more complex and more accurate simulations to gain more insight into your product — or you can evaluate more design variations to find the optimal design without long hardware/software procurement and deployment delays.

Join PADT’s Senior Application & Simulation Support Engineer Sina Ghods for a look at how Ansys is working to drive adoption by providing users a ready to use cloud service that provides:

  • Higher Fidelity Models
  • Faster Turnaround Time
  • Improved Productivity
  • Flexible Licensing
  • Multiple Supported Solvers
  • 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 060: Tips For Making Working From Home More Productive

 

Published on: April 6th, 2020
With: Eric Miller & Matt Sutton
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s seasoned expert at working with Ansys from home Matt Sutton for a quick discussion on tips and best practices that make working from home more productive and effective.

If you would like to learn more about how PADT and Ansys can help you to better run your simulation from your home office, check out our webinar on the topic here: https://bit.ly/3dSa8WN

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

Listen:
Subscribe:

@ANSYS #ANSYS

Test, Design & Analyze From Home With Ansys Simulation Software – Webinar

As companies are closing their doors in order to help ensure the health and safety of their employees and customers, those that can are pivoting to working form home. Tadam black stock is one of the best guide.

But what about those working on product design, testing, and analysis that require a physical presence?

Here at PADT we know that the show must go on, and companies working across various technical professions are needed to keep the world moving forward, especially in these trying times. Thus we would like to introduce a solution: Ansys Engineering Simulation Software. You can also visit cherryscustomframing to know more.

Join The PADT team for a panel discussion on how you can use simulation to move your in-person workflow to a digital environment, as well as what specific Ansys tools can be used to access your work from home.

All of this will be followed by a live Q&A in which our expert staff will take any questions regarding your specific concerns with transitioning your workflow and all other things related to working from home.

Register Here

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

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

Ansys Sherlock: A Comprehensive Electronics Reliability Tool

As systems become more complex, the introduction and adoption of detailed Multiphysics / Multidomain tools is becoming more commonplace. Oftentimes, these tools serve as preprocessors and specialized interfaces for linking together other base level tools or models in a meaningful way. This is what Ansys Sherlock does for Circuit Card Assemblies (CCAs), with a heavy emphasis on product reliability through detailed life cycle definitions.

In an ideal scenario, the user will have already compiled a detailed ODB++ archive containing all the relevant model information. For Sherlock, this includes .odb files for each PCB layer, the silkscreens, component lists, component locations separated by top/bottom surface, drilled locations, solder mask maps, mounting points, and test points. This would provide the most streamlined experience from a CCA design through reliability analysis, though any of these components can be imported individually.

These definitions, in combination with an extensive library of package geometries, allow Sherlock to generate a 3D model consisting of components that can be checked against accepted parts lists and material properties. The inclusion of solder mask and silkscreen layers also makes for convenient spot-checking of component location and orientation. If any of these things deviate from the expected or if basic design variation and optimization studies need to be conducted, new components can be added and existing components can be removed, exchanged, or edited entirely within Sherlock.

Figure 1: Sherlock’s 2D layer viewer and editor. Each layer can be toggled on/off, and components can be rearranged.

While a few of the available analyses depend on just the component definitions and geometries (Part Validation, DFMEA, and CAF Failure), the rest are in some way connected to the concept of life cycle definitions. The overall life cycle can be organized into life phases, e.g. an operating phase, packaging phase, transport phase, or idle phase, which can then contain any number of unique event definitions. Sherlock provides support for vibration events (random and harmonic), mechanical shock events, and thermal events. At each level, these phases and events can be prescribed a total duration, cycle count, or duty cycle relative to their parent definition. On the Life Cycle definition itself, the total lifespan and accepted failure probability within that lifespan are defined for the generation of final reliability metrics.  Figure 1 demonstrates an example layout for a CCA that may be part of a vehicle system containing both high cycle fatigue thermal and vibration events, and low cycle fatigue shock events.

Figure 2: Product life cycles are broken down into life phases that contain life events. Each event is customizable through its duration, frequency, and profile.

The remaining analysis types can be divided into two categories: FEA and part specification-based. The FEA based tests function by generating a 3D model with detail and mesh criteria determined within Sherlock, which is then passed over to an Ansys Mechanical session for analysis. Sherlock provides quite a lot of customization on the pre-processing level; the menu options include different methods and resolutions for the PCB, explicit modeling of traces, and inclusion or exclusion of part leads, mechanical parts, and potting regions, among others.

Figure 3: Left shows the 3D model options, the middle shows part leads modeled, and right shows a populated board.

Each of the FEA tests, Random Vibration, Harmonic Vibration, Mechanical Shock, and Natural Frequency, correspond to an analysis block within Ansys Workbench. Once these simulations are completed, the results file is read back into Sherlock, and strain values for each component are extracted and applied to either Basquin or Coffin—Manson fatigue models as appropriate for each included life cycle event.

Part specification tests include Component Failure Analysis for electrolytic and ceramic capacitors, Semiconductor Wearout for semiconductor devices, and CTE mismatch issues for Plated Through-Hole and solder fatigue. These analyses are much more component-specific in the sense that an electrolytic capacitor has some completely different failure modes than a semiconductor device and including them allows for a broad range of physics to be accounted for across the CCA.

The result from each type of analysis is ultimately a life prediction for each component in terms of a failure probability curve alongside a time to failure estimate. The curves for every component are then combined into a life prediction for the entire CCA under one failure analysis.

Figure 4: Analysis results for Solder Fatigue including an overview for quantity of parts in each score range along with a detailed breakdown of score for each board component.

Taking it one step further, the results from each analysis are then combined into an overall life prediction for the CCA that encompasses all the defined life events. From Figure 5, we can see that the life prediction for this CCA does not quite meet its 5-year requirement, and that the most troublesome analyses are Solder Fatigue and PTH Fatigue. Since Sherlock makes it easy to identify these as problem areas, we could then iterate on this design by reexamining the severity or frequency of applied thermal cycles or adjusting some of the board material choices to minimize CTE mismatch.

Figure 5: Combined life predictions for all failure analyses and life events.

Sherlock’s convenience for defining life cycle phases and events, alongside the wide variety of component definitions and failure analyses available, really cement Sherlock’s role as a comprehensive electronics reliability tool. As in most analyses, the quality of the results is still dependent on the quality of the input, but all the checks and cross-validations for components vs life events that come along with Sherlock’s preprocessing toolset really assist with this, too.

All Things Ansys 059: Elements, Contact & Solver Updates in Ansys MAPDL 2020 R1

 

Published on: March 23rd, 2020
With: Eric Miller, Ted Harris, Alex Grishin & Joe Woodward
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Ted Harris, Alex Grishin, and Joe Woodward to discuss their favorite features in the MAPDL Updates in Ansys 2020 R1.

If you would like to learn more about this topic, you can view PADT’s webinar covering these updates here: https://bit.ly/2WD88vt

Additionally, if you would like to take part in the survey mentioned at the start of the episode click the link here: https://bit.ly/3biWkCp

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

Listen:
Subscribe:

@ANSYS #ANSYS

MAPDL – Elements, Contact & Solver Updates in Ansys 2020 R1 – Webinar

The ANSYS finite element solvers enable a breadth and depth of capabilities unmatched by anyone in the world of computer-aided simulation. Thermal, Structural, Acoustic, Piezoelectric, Electrostatic and Circuit Coupled Electromagnetics are just an example of what can be simulated. Regardless of the type of simulation, each model is represented by a powerful scripting language, the ANSYS Parametric Design Language (APDL).

APDL is the foundation for all sophisticated features, many of which are not exposed in the Workbench Mechanical user interface. It also offers many conveniences such as parameterization, macros, branching and looping, and complex math operations. All these benefits are accessible within the ANSYS Mechanical APDL user interface.

Join PADT’s Principle & Co-Owner Eric Miller for a look at what’s new for MAPDL in ANSYS 2020 R1, regarding:

  • Linear Dynamics
  • Elements
  • Contacts
  • Post Processing
  • Solver Components
  • 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!

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!

Listen:
Subscribe:

@ANSYS #ANSYS

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!

Listen:
Subscribe:

@ANSYS #ANSYS

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!

Listen:
Subscribe:

@ANSYS #ANSYS

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!

Listen:
Subscribe:

@ANSYS #ANSYS