All Things Ansys 067: Introducing an All-new Ansys Discovery

 

Published on: July 13th, 2020
With: Eric Miller & Justin Hendrickson
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Justin Hendrickson, the Director of Product Management for the Physics Business Unit at Ansys for a discussion on the new Discovery release and the live product release event taking place on Wednesday, July 29th at 11:00 am EDT.

Learn how Discovery will help you boost your ROI across your organization by decreasing costs associated with engineering labor, physical prototyping, and testing. With this tool you can answer critical design questions earlier on in your process without waiting for simulation results. Quickly prepare models, explore multiple design concepts, and refine insights with high-fidelity, all thanks to this brand new release from Ansys.

If you would like to register for the release event you can do so via this link: https://www.padtinc.com/discoveryr2

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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Sharing Nature While we Work From Home

The Winners From PADT’s 2020 Nature Photography Day Contest

Every month we try to do something fun at PADT, and before the evil spikey ball of death ruined everything we would usually do something food-related. Pi day = Pie. Chocolate Day = Chocolate. Anything = pizza. However, since most of us are working from home we could not all show up in the lunchroom at noon for team-building (chowing down).

So we have been looking at a few websites that list fun, often fake holidays, and found out that June 15th was “Nature Photography Day” After setting up some channels in Microsoft Teams we let everyone submit pictures. Then after a week of submitting, employees voted.

Some were downright stunning. All were beautiful.

We present the winners here for your viewing pleasure.

For those who are interested, here is how we did the contest:

  1. We created MS Teams channels for each of the categories we chose:
    Amature, Pro’ish, and Kids
  2. Within each category, there were five topics: Desert, Not Desert, Water, Plants & Flowers, Animals in Nature, Human Structures in Nature.
  3. Employees uploaded their images to the proper channel and shared a bit about each one.
  4. We used the emoticon capability in Teams to “vote” on each one. A heart was worth 3 points, a laughing face 2, and thumbs up 1.
  5. After the voting was done we added up the points for each category and that determined the winners.

Mechanical Updates in Ansys 2020 R2 – Webinar

From designers and occasional users looking for quick, easy and accurate results, to experts looking to model complex materials, large assemblies and nonlinear behavior, Ansys has you covered. The intuitive interface of Ansys Mechanical enables engineers of all levels to get answers fast and with confidence. Ansys structural analysis software is used across industries to help engineers optimize their product designs and reduce the costs of physical testing.

Ansys Mechanical is the flagship mechanical engineering software solution that uses finite element analysis (FEA) for structural analysis.It covers an enormous range of applications and comes complete with everything you need from geometry preparation to optimization and all the steps in between. With Mechanical Enterprise you can model advanced materials, complex environmental loadings and industry-specific requirements in areas such as offshore hydrodynamics and layered composite materials.

In this webinar, PADT’s Senior Mechanical Engineer & Lead Trainer, Joe Woodward will cover a few key components of this tool and what is newly available for them in Ansys 2020 R2. This includes updates for:

– Mechanical Core

– Mechanical Graphics/Post Processing

– Linear Dynamics

– SMART Fracture

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!

From visualization to simulation: Digital Anatomy Solutions for 3D Printing – Webinar

The Stratasys J750 Digital Anatomy printer truly brings the look and feel of medical models to life with unrivaled accuracy, realism and functionality. Whether used for surgeon training or to perform testing during device development, its models provide unmatched clinical versatility mimicking both the appearance and response of human tissue.

Bring medical models to life. The J750 Digital Anatomy Printer takes the J750 capabilities to the next level. Step up to the printer’s digital capabilities to create models with an incredible array of microstructures which not only look, but now feel and function like actual human tissue for true haptic feedback. All of this in a single print operation with minimal to no finishing steps like painting, sanding or assembly.

Join PADT’s 3D Printing & Support Application Engineer Pam Waterman for a discussion on the value of this innovative new technology, including:

– How it solves challenges facing medical device companies and hospitals

– More realistic, functional, and anatomically accurate modeling capabilities

– Quicker design and development, leading to reduced time-to-market

– 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!

Top Ten Additive Manufacturing Terms to Know

The world of additive manufacturing, or 3D printing, is constantly evolving. The technology was invented less than 35 years ago yet has come a long way. What began as a unique, though limited, way to develop low-end prototypes, has exploded into a critical component of the product development and manufacturing process with the ability to produce end-use parts for critical applications in markets such as industrial and aerospace and defense.

To help our customers and the larger technology community stay abreast of the changing world of additive manufacturing, we launched a glossary of the most important terms in the industry that you can bookmark here for easy access. To make it easier to digest, we’re also starting a blog series outlining ten terms to know in different sub-categories.

For our first post in the series, here are the top ten terms for Additive Manufacturing Processes that our experts think everyone should know:

Binder Jetting

Any additive manufacturing process that uses a binder to chemically bond powder where the binder is placed on the top layer of powder through small jets, usually using inkjet technology. One of the seven standard categories defined by ASTM International (www.ASTM.org) for additive manufacturing processes.

Digital Light Synthesis (DLS)

A type of vat photopolymerization additive manufacturing process where a projector under a transparent build plate shines ultraviolet light onto the build layer, which is against the transparent build plate. The part is then pulled upward so that a new layer of liquid fills between the build plate and the part, and the process is repeated. Digital light synthesis is a continuous build process that does not create distinct layers.

Direct Laser Melting (DLM) or Direct Metal Laser Sintering (DMLS)

A type of powder bed fusion additive manufacturing process where a laser beam is used to melt powder material. The beam is directed across the top layer of powder. The liquid material solidifies to create the desired part. A new layer of powder is placed on top, and the process is repeated. Also called laser powder bed fusion, metal powder bed fusion, or direct metal laser sintering.

Directed Energy Deposition (DED)

An additive manufacturing process where metal powder is jetted, or wire is extruded from a CNC controlled three or five-axis nozzle. The solid material is then melted by an energy source, usually a laser or electron beam, such that the liquid metal deposits onto the previous layers (or build plate) and then cools to a solid. One of the ASTM defined standard categories for additive manufacturing processes.

Fused Deposition Modeling (FDM)

A type of material extrusion additive manufacturing process where a continuous filament of thermoplastic material is fed into a heated extruder and deposited on the current build layer. It is the trademarked name used for systems manufactured by the process inventor, Stratasys. Fused filament fabrication is the generic term.

Laser Powder Bed Fusion (L-PBF)

A type of powder bed fusion additive manufacturing process where a laser is used to melt material on the top layer of a powder bed. Also called metal powder bed fusion or direct laser melting. Most often used to melt metal powder but is used with plastics as with selective laser sintering.

Laser Engineered Net Shaping (LENS)

A type of direct energy deposition additive manufacturing process where a powder is directed into a high-energy laser beam and melted before it is deposited on the build layer. Also called laser powder forming.

Material Jetting

Any additive manufacturing process where build or support material is jetted through multiple small nozzles whose position is computer controlled to lay down material to create a layer. One of the ASTM defined standard categories for additive manufacturing processes.

Stereolithography Apparatus (SLA)

A type of vat photopolymerization additive manufacturing where a laser is used to draw a path on the current layer, converting the liquid polymer into a solid. Stereolithography was the first commercially available additive manufacturing process.

Vat Polymerization

A class of additive manufacturing processes that utilizes the hardening of a photopolymer with ultraviolet light. A vat of liquid is filled with liquid photopolymer resin, and ultraviolet light is either traced on the build surface or projected on it. Stereolithography is the most common form of vat photopolymerization. The build layer can be on the top of the vat of liquid or the bottom. One of the ASTM defined standard categories for additive manufacturing processes.

We hope this new blog series will help to firm up your knowledge of the ever-evolving world of additive manufacturing. For a list of all of the key terms and definitions in the additive manufacturing world, please visit our new glossary page at https://www.3dprinting-glossary.com/. The glossary allows you to search by terms or download a PDF of the glossary in its entirety to use as a reference guide.

We also know that there are a ton of experts in our community with knowledge to share. If you notice a term missing from our glossary or an inaccurate/incomplete description, please visit the suggestions page at https://www.3dprinting-glossary.com/suggest-a-correction-clarification-or-new-term/ and drop us a note.

Subscribe to the PADT blog or check back soon for the next installment in our series of “Top Ten Terms to Know in Additive Manufacturing.” We also welcome your feedback or questions. Just drop us a line at here.

All Things Ansys 066: Simulation Automation & Optimization management with Ansys optiSLang

 

Published on: June 29th, 2020
With: Eric Miller & Josh Stout
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s systems application & support engineer Josh Stout to look at the optimization tool optiSLang. This tool helps automate simulation and optimization activities across various solution areas, such as autonomy, electrification, digital twins, and more, as well as how it enables users to capitalize on the benefits of enterprise simulation management.

If you would like to learn more, you can view the product brochure here: https://www.ansys.com/-/media/ansys/corporate/resourcelibrary/brochure/optislang-brochure.pdf.

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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What I use Most from my Engineering Management Masters Degree

Even before finishing my undergraduate degree in Mechanical Engineering at the University of Colorado, Boulder in 2010, I had an interest in furthering my education. The decision I had at that point was whether the next step would be a graduate degree on the technical side or something more like an MBA. I would end up with the chance to study at the University of Denver (DU), focusing on Computational Fluid Dynamics (CFD), and if that field does not make it clear, my first stint in grad school was technical.

At DU, we sourced our Ansys simulation software from a company called, you guessed it, PADT. After finishing this degree, and while working at PADT, the desire to further my education cropped up again after seeing the need for a well-rounded understanding of the technical and business/management side of engineering work. After some research, I decided that a Master’s in Engineering Management program made more sense than an MBA, and I started the program back at my original alma mater, CU Boulder.

Throughout the program, I would find myself using the skills I was learning during lectures immediately in my work at PADT. It is difficult to boil down everything learned in a 10-course program to one skill that is used most often, and as I think about it, I think that what is used most frequently is the new perspective, the new lens through which I can now view situations. It’s taking a step back from the technical work and viewing a given project or situation from a perspective shaped by the curriculum as a whole with courses like EMEN 5020 – Finance and Accounting for Engineers, EMEN 5030/5032 – Fundamentals/Advanced Topics of Project Management, EMEN 5050 – Leading Oneself, EMEN 5080 – Ethical Decision Making, EMEN 5500 – Lean and Agile Management, and more. It is the creation of this new perspective that has been most valuable and influential to my work as an engineer and comes from the time spent completing the full program.

Okay okay, but what is the one thing that I use most often, besides this new engineering management perspective? If I had to boil it down to one skill, it would be the ‘pull’ method for feedback. During the course Leading Oneself, we read Thanks for the Feedback: The Science and Art of Receiving Feedback Well, Even When it is Off Base, Unfair, Poorly Delivered, and, Frankly, You’re Not In The Mood (Douglas Stone and Sheila Heen, 2014), where this method was introduced. By taking an active role in asking for feedback, it has been possible to head-off issues while they remain small, understand where I can do better in my current responsibilities, and grow to increase my value to my group and PADT as a whole.

Combining Simulation with Additive Manufacturing to Optimize Product Design – Webinar

Advatech Pacific, a Phoenix-based aerospace and defense contractor founded in 1995, works to change the way engineering is conducted for the better by incorporating innovative technologies into its customer’s workflow. Based on the success of previous projects, Advatech is a strong proponent of using high-end simulation software such as Ansys to identify and evaluate the fine details of massive multi-body mechanical systems, whether through simple static analyses or tightly-coupled multiphysics computations.

Implementing additive manufacturing as an additional way to improve system design presented opportunities to cut back on tooling costs and reduce lead time for several candidate turbine-engine parts. Doing so would also alleviate the challenge of reproducing complex castings, a problem made increasingly difficult by the fact that many of the original casting providers are no longer in business.

Join PADT’s Lead Mechanical Engineer Doug Oatis, and Advatech Pacific’s Engineering Manager Matt Humrick for a discussion on Ansys tools with regards to additive manufacturing & topology optimization, and how Advatech Pacific was able to use them to drastically improve the efficiency of their design and manufacturing process.

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!

A Simple Adjustment to Fix a Contact Convergence Problem in Ansys Mechanical

As I write this from home during the Covid-19 crisis, I want to assure you that PADT is conscious of many others working from home while using Ansys software as well.  We’re trying to help with those who may be struggling with certain types of models.  In this posting, I’ll talk about a contact convergence problem in Ansys Mechanical.  I’ll discuss steps we can take to identify the problem and overcome it, as well as a simple setting to make which dramatically helped in this case. 

The geometry in use here is a fairly simple assembly from an old training class.  It’s a wheel or roller held by a housing, which is in turn bolted to something to hold it in place.

A close up of a device

Description automatically generated

The materials used are linear properties for structural steel.  The loading consists of a bearing load representing a downward force applied by some kind of strap or belt looped over the wheel, along with displacement constraints on the back surfaces and around the bolt holes, as shown in the image below.  The flat faces on the back side have a frictionless support applied (allows in plane sliding only), while the circular faces where bolt heads and washers would be are fully constrained with fixed supports.

A close up of a logo

Description automatically generated

As is always the case in Ansys Mechanical, contact pairs are created wherever touching surfaces in the assembly are detected.  The default behavior for those contact pairs is bonded, meaning the touching surfaces can neither slide nor separate.  We will make a change to the default for the wheel on its shaft, though, changing the contact behavior from bonded to frictional.  The friction coefficient defined was 0.2.  This represents some resistance to sliding.  Unlike bonded contact in which the status of the contact pair cannot change during the analysis, frictional contact is truly nonlinear behavior, as the stiffness of the contact pair can change as deflection changes. 

This shows the basic contact settings for the frictional contact pair:

A screenshot of a cell phone

Description automatically generated

At this point, we attempt a solve.  After a while, we get an error message stating, “An internal solution magnitude limit was exceeded,” as shown below.  What this means is that our contact elements are not working as expected, and part of our structure is trying to fly off into space.  Keep in mind in a static analysis there are no inertia effects, so an unconstrained body is truly unconstrained.

At this point, the user may be tempted to start turning multiple knobs to take care of the situation.  Typical things to adjust for contact convergence problems are adding more substeps, reducing contact stiffness, and possibly switching to the unsymmetric solver option when frictional contact is involved.  In this case, a simple adjustment is all it takes to get the solution to easily converge. 

Another thing we might do to help us is to insert a Contact Tool in the Connections branch and interrogate the initial contact status:

This shows us that our frictional contact region is actually not in initial contact but has a gap.  There are multiple techniques available for handling this situation, such as adding weak springs, running a transient solution (computationally expensive), starting with a displacement as a load and then switching to a force load, etc.  However, if we are confident that these parts actually SHOULD be initially touching but are not due to some slop in the CAD geometry, there is a very easy adjustment to handle this.

The Simple Adjustment That Gets This Model to Solve Successfully

Knowing that the parts should be initially in contact, one simple adjustment is all that is needed to close the initial gap and allow the simulation to successfully solve.  The adjustment is to set the Interface Treatment in the Contact Details for the contact region in question to Adjust to Touch:

This change automatically closes the initial gap and, in this case, allows the solution to successfully solve very quickly. 

For your models, if you are confident that parts should be in initial contact, you may also find that this adjustment is a great aid in closing gaps due to small problems in the CAD geometry.  We encourage you to test it out.

An Ansys optiSLang Overview and Optimization Example with Ansys Icepak

Ansys optiSLang is one of the newer pieces of software in the Ansys toolkit that was recently acquired along with the company Dynardo. Functionally, optiSLang provides a flexible top-level platform for all kinds of optimization. It is solver agnostic, in that as long as you can run a solver through batch files and produce text readable result files, you can use said solver with optiSLang. There are also some very convenient integrations with many of the Ansys toolkit solvers in addition to other popular programs. This includes AEDT, Workbench, LS-DYNA, Python, MATLAB, and Excel, among many others.

While the ultimate objective is often to simply minimize or maximize a system output according to a set of inputs, the complexity of the problem can increase dramatically by introducing constraints and multiple optimization goals. And of course, the more complicated the relationships between variables are, the harder it gets to adequately describe them for optimization purposes.

Much of what optiSLang can do is a result of fitting the input data to a Metamodel of Optimal Prognosis (MOP) which is a categorical description for the specific metamodels that optiSLang uses. A user can choose one of the included models (Polynomial, Moving Least Squares, and Ordinary Kriging), define their own model, and/or allow optiSLang to compare the resulting Coefficients of Prognosis (COP) from each model to choose the most appropriate approach.

The COP is calculated in a similar manner as the more common COD or R2 values, except that it is calculated through a cross-validation process where the data is partitioned into subsets that are each used only for the MOP calculation or the COP calculation, not both. For this reason, it is preferred as a measure for how effective the model is at predicting unknown data points, which is particularly valuable in this kind of MOP application.

This whole process really shows where optiSLang’s functionality shines: workflow automation. Not only does optiSLang intelligently select the metamodel based on its applicability to the data, but it can also apply an adaptive method for the improvement of the MOP. It will suggest an automatic sampling method based on the number of problem variables involved, which can then be applied towards refining either the global COP or the minimum local COP. The automation of this process means that once the user has linked optiSLang to a solver with appropriate inputs/outputs and defined the necessary run methodology for optimization, all that is left is to click a button and wait.

As an example of this, we will run through a test case that utilizes the ability to interface with Ansys EDT Icepak.

Figure 1: The EDT Icepak project model.

For our setup, we have a simple board mounted with bodies representative of 3 x 2 watt RAM modules, and 2 x 10 watt CPUs with attached heatsinks. The entire board is contained within an air enclosure, where boundary conditions are defined as walls with two parametrically positioned circular inlets/outlets. The inlet is a fixed mass flow rate surface and the outlet is a zero-pressure boundary. In our design, we permit the y and z coordinates for the inlet and outlet to vary, and we will be searching for the configuration that minimizes the resulting CPU and RAM temperatures.

The optiSLang process generally follows a series of drag-and-drop wizards. We start with the Solver Wizard which guides us through the options for which kind of solver is being used: text-based, direct integrations, or interfaces. In this case, the Icepak project is part of the AEDT interface, so optiSLang will identify any of the parameters defined within EDT as well as the resulting report definitions.  The Parametric Solver System created through the solver wizard then provides the interfacing required to adjust inputs while reading outputs as designs are tested and an MOP is generated.

Figure 2: Resulting block from the Solver wizard with parameters read in from EDT.

Once the parametric solver is defined, we drag and drop in a sensitivity wizard, which starts the AMOP study.  We will start with a total of 100 samples; 40 will be initial designs, and 60 will be across 3 stages of COP refinement with all parameter sets sampled according to the Advanced Latin Hypercube Sampling method.

Figure 3: Resulting block from the Sensitivity wizard with Advanced Latin Hypercube Sampling.

The results of individual runs are tabulated and viewable as the study is conducted, and at the conclusion, a description of the AMOP is provided with response surfaces, residual plots, and variable sensitivities. For instance, we can see that by using these first 100 samples, a decent metamodel with a COP of 90% is generated for the CPU temperature near the inlet. We also note that optiSLang has determined that none of the responses are sensitive to the ‘y’ position of the outlet, so this variable is automatically freed from further analysis.

Figure 4: MOP surface for the temperature of Chip1, resulting from the first round of sampling.

 If we decide that this CoP, or that from any of our other outputs, is not good enough for our purposes, optiSLang makes it very easy to add on to our study. All that is required is dragging and dropping a new sensitivity wizard onto our previous study, which will automatically load the previous results in as starting values. This makes a copy of and visually connects an output from the previous solver block to a new sensitivity analysis on the diagram, which we can then be adjusted independently.

For simplicity and demonstration’s sake, we will add on two more global refinement iterations of 50 samples each. By doing this and then excluding 8 of our 200 total samples that appear as outliers, our “Chip1” CoP can be improved to 97%.

Figure 5: A refined MOP generated by including a new Sensitivity wizard.

Now that we have an MOP of suitable predictive power for our outputs of interest, we can perform some fast optimization. By initially building an MOP based on the overall system behavior, we are now afforded some flexibility in our optimization criteria. As in the previous steps, all that is needed at this point is to drag and drop an optimization wizard onto our “AMOP Addition” system, and optiSLang will guide us through the options with recommendations based on the number of criteria and initial conditions.

In this case, we will define three optimization criteria for thoroughness: a sum of both chip temperatures, a sum of all RAM temperatures, and an average temperature rise from ambient for all components with double weighting applied to the chips. Following the default optimization settings, we end up with an evolutionary algorithm that iterates through 9300 samples in about 14 minutes – far and away faster than directly optimizing the Icepak project. What’s more, if we decide to adjust the optimization criteria, we’ll only need to rerun this ~14 minute evolutionary algorithm.

What we are most interested in for this example are the resulting Pareto fronts which give us a clear view of the tradeoffs between each of our optimization criteria. Each of the designs on this front can easily be selected through the interface, and their corresponding input parameters can be accessed.

Figure 6: Pareto front of the “Chipsum” and “TotalAve” optimization criteria.

Scanning through some of these designs also provides a very convenient way to identify which of our parameters are limiting the design criteria. Two distinct regions can be identified here: the left region is limited by how close we are allowing the inlet fan to be to the board, and the right region is limited by how close to the +xz corner of our domain the outlet vent can be placed. In a situation where these parameters were not physically constrained by geometry, this would be a good opportunity to consider relaxing parameter constraints to further improve our optimization criteria. 

As it is, we can now choose a design based on this Pareto front to verify with the full solver. After choosing a point in the middle of the “Limited by outlet ‘z’” zone, we find that our actual “ChipSum” is 73.33 vs. the predicted 72.78 and the actual “TotalAve” is 17.82 vs. the predicted 17.42. For this demonstration, we consider this small error as satisfactory, and a snapshot of the corresponding Icepak solution is shown below.

Figure 7: The Icepak solution of the final design. The inlet vent is aligned with the outlet side’s heatsink, and the outlet vent is in the corner nearest the heatsink. Primary flow through the far heatsink is maximized, while a strong recirculating flow is produced around the front heatsink.

The accuracy of these results is of course dependent not only on how thoroughly we constructed the MOP, but also the accuracy of the 3D solution; creating mesh definitions that remain consistently accurate through parameterized geometry changes can be particularly tricky. Though, with all of this considered, optiSLang provides a great environment for not only managing optimization studies, but displaying the results in such a way that you can gain an improved understanding of the interaction between input/output variables and their optimization criteria.

All Things Ansys 064: The Largest Virtual Engineering Simulation Trade Show Ever – Ansys Simulation World

 

Published on: June 1st, 2020
With: Eric Miller & Lynn Ledwith
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Ansys CMO Lynn Ledwith, for a look at their digital trade show, Simulation world taking place Wednesday June 10th through Thursday June 11th.

The largest engineering simulation virtual event in the world, this event is a free online conference designed to inspire and educate executives, engineers, R&D, and manufacturing professionals about the transformative powers of engineering simulation and Ansys.

If you would like to learn more, visit simulation-world.com or register for free via https://bit.ly/2XjxrCN

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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