Being a CAE analyst for almost 20 years has been an interesting journey. Looking back to see the huge development in computer power and the development of the simulation software, in terms of supported physics, features and ease of use, is very pleasing. The turnaround time for a typical simulation has been reduced from months to days. However, there is one part of the process that has not changed, and not gotten the same speed up; namely reporting.

Communicating simulation results with project managers and stakeholders, as well as documenting the work in the company’s product development process, is very important in order to have traceability of decisions that are made and to be able to review the work. My experience, however, is that you in the rush of the project only deliver some preliminary result pictures and presentations to the project manager. The important, but boring and time consuming reporting, is left for later. This is obviously not an optimal way of working, since you may have forgotten important conclusions about the analysis when you find the time to write the report.

The problem with report writing is that it requires manual work making screen dumps, cropping and re-sizing the images, as well as reading out properties from the model, and typing in the report. Often you might start from an old report and replace just what has changed. Is this a quality assured way of working?

When a person is reviewing these kinds of reports there is absolutely no guaranty that the content of the report actually matches the FE-model. In order to review the analysis I have seen examples where they look at the APDL input file for the simulation to check the material properties and load cases etc. instead of reading the report. For a third party reviewer this is impossible to read unless they are also an expert of APDL syntax. Don’t get me started about custom macros or linked input files making this reviewing mission impossible!

Then there are examples of automatic report generators that can extract model information to a report that either contain too much or too little information. Usually you have to spend a lot of time editing and to transfer to your company specific template so if you make a model update the work is lost and has to be re-done.

To avoid spending time writing engineering reports you may either a) become a manager b) find a new solution. I took the latter and have developed a new app called “Report Generator” found on the ANSYS app store.

The core values this app will bring to your business can be summarized in these six points.

• Speed up 10X. Automatic picture export, report layout and formatting done in seconds. Free up time for the engineer to do more analyses and deliver reports instantly in order to take correct decisions faster.
• Quality Assurance. Report content matches the actual FE-model, makes for a valid third party reviewing. No copy/paste errors or wrong images.
• Traceability. Easy to document the development of a product and make new report revisions when there are new prerequisites. The documentation is automatically saved in the ANSYS project.
• Process integration. Use company specific report templates with standardized content. Easy to read and find content independent of who is writing the report.
• Simplicity. Easy to use ANSYS Mechanical GUI for creating text, figures and charts
• Flexibility. Custom report content from a simple result page to a full engineering report with appendix.

To read more about this app click on the link below where you will also find a sample report made with the app.
ANSYS ACT Application Store/Report Generator

The post How to Make Knowledge Capture an Integrated Part of the Simulation Process appeared first on ANSYS.

Over the past year and a half, our team has been creating a large number of technical ANSYS videos that focus on a variety of areas. From ANSYS Electronics to ANSYS CFX, ANSYS Fluent to ANSYS Mechanical, ANSYS SCADE, and even ANSYS Student tips for those just getting started in the art of engineering simulation.

Today, I’d like to share a few of the examples you’ll find before I send you off to explore on your own.

This first video I’m highlighting is featuring ANSYS AIM, with a demonstration of performing a stress analysis of an aircraft engine bracket. You get a look at how to import your model to begin your work. The demo is showing how you can study the effect of the stress on the pinholes location that will be used to bolt the bracket to the engine.

For users of ANSYS Fluent, you’ll find a two-part look at wrapping a generic combustor geometry, including using diagnostic tool driven gap closing, how to simplify and close the geometry, setting up scoped sizing controls, and defining a material point and periodicity.

Recently, we also released a six-part look at electro-thermal analyses of a printed circuit board. In part one, you’ll learn how to import the board from ODB++ format into SIwave and review the layout and schematic of the PCB. Part two demonstrates how to set up the board and define current and voltage sources for power integrity analyses. Part three will walk you through how to perform post processing in SIwave and Icepak after specifying natural convection as the thermal simulation type.. Part four shows you how to perform post processing in Icepak after specifying forced convection as the thermal simulation type in SIwave.

In part five, we show how to use the same printed circuit board and link its electro-thermal analysis results to ANSYS Mechanical using ANSYS Workbench, including how to set up the board to perform structural analysis. And finally, part six demonstrates how to use ANSYS Workbench and ANSYS Mechanical to perform a structural analysis on a printed circuit board and generate graphs for stress, deformation, and strain.

So to close out this post…I hope I have piqued your curiosity to head over to the ANSYS How To Videos YouTube channel, poke around, and find topics of interest. While you’re there, don’t forget to subscribe to receive notifications when new videos are posted. Enjoy!

The post ANSYS Videos on the ANSYS How To Channel appeared first on ANSYS.

If you’re an engineer who has dealt with large simulation models, you know there’s often a trade-off between accuracy and solution time. Submodeling is a technique you can use to reduce solution time without sacrificing accuracy of results.

A common strategy you can use to look at the overall behavior of an assembly or complex part of a large model is to simplify the model during preparation by removing small details, like fillets and holes. Simplifying models in this way can have a significant impact on run times. This simplification, while not excessively affecting overall model stiffness, may result in lower resolution of localized stresses. What you need, then, is a mechanism that allows you to “zoom in” on these details to examine behavior around specific areas.

Submodeling is such a technique — it enables you to solve a locally refined model with all of the geometric details required to solve accurate stresses. The ANSYS submodeling method not only provides accuracy, it solves these regions in a fraction of the time it would take to solve the entire model.

With ANSYS 17.0, we added an enhancement for SpaceClaim Direct Modeler that makes it easy to extract a submodel from solid or surface geometry. The technique is highly automated while still enabling you to enter guidance and input. You can use Direct Modeler to extract the model with only a few mouse button clicks. The technique leverages a useful SpaceClaim viewing method called “Clip with Volume;” you simply designate a spherical region and Direct Modeler automatically extracts the appropriate solid geometry. Not only does it rapidly create the submodel but the cut planes (named selections) are automatically created as part of this process!

The example below shows stress concentration results for a full model gearbox. The geometry has a “sharp” corner — singularity.

Sharp corners resulting in high stress concentrations often lead to misleading or inaccurate results.

The stress results would be inaccurate — stresses are over 50 KSI.

High stresses along sharp corners.

In SpaceClaim, with a few mouse button clicks, you can extract the submodel using Clip with Volume and use the Pull tool to add a fillet radius.

Submodel extraction and rapid geometry modification in SpaceClaim.

This automatically creates a Named Selection at the cut boundaries of the submodel. The next step is to transfer the model into ANSYS Mechanical and apply a fine mesh to specific areas.

Meshing a submodel with a fine resolution.

The Named Selections created from SpaceClaim are used to import displacements  from the full model.

Transferring displacements using the SpaceClaim generated Named Selections

This gear box submodel is solved with 3 easy steps as shown below and with very accurate stresses of only 20 KSI, as compared to the 50 KSI stress concentration.

Realistic and accurate stress concentrations now that geometries have been altered and meshes refined.

To solve the full model with all the detail will require a significant increase in the solve time to get the accuracy of the submodel.

The post Submodeling: Simple Solutions for Large-Scale Problems appeared first on ANSYS.

We just held our eighth ANSYS in ACTION session. This one featured HPC on the Cloud and showed the audience how engineers without local HPC resources can access ANSYS and HPC in the cloud.

For those of you new to the series, ANSYS in ACTION is a webinar series where we show you how easy it is to solve common applications and address common challenges engineers face using ANSYS software in just 20 minutes on Thursdays at 1 pm ET. We skip the marketing and the background information and get right to the demo.

We suspected that a series like this would resonate with engineers that often prefer to watch software in action instead of seeing PowerPoint slides. At this point, I declare ANSYS in ACTION an early success with more engineers signing up for upcoming sessions and downloading recordings for past session every day.

Wouldn’t you like to join ANSYS in ACTION and see how ANSYS can help you over a coffee or a beverage of your choice on an upcoming Thursday in January at 1 pm ET or anytime by downloading a recording? Some suggested sessions are below.

Pressure Drop in a Valve: Pressure drop through a valve is a function of system demand and increases with increasing flowrate. The right valve must be selected for each application or new set of conditions. View this recording to see how simulation from ANSYS can make simulations for valve selection easy and straightforward in just 20 min.

Evaluating Bolted Connections and Tightening Sequences: Simulating bolted connections enables you to accurately determine the structural performance and the optimal tightening sequences for your bolted connection designs before you invest time and money in physical prototypes. Watch this ANSYS in ACTION session to learn how you can use simulation to quickly and easily evaluate bolted connections and tightening sequences.

Evaluating Fatigue on a Bicycle Frame: Bicycle designers have a multitude of options for bike frame configuration, material type and material thickness to choose from to design bicycles that best address both consumer and business needs. Watch this ANSYS in ACTION recording to see how simulation from ANSYS can make evaluating these options virtually fast and easy.

Determining the temperature of the LED itself and the enclosure requires simulating the current passing through the conductors, heat transfer to the air, the temperature of the LED and fixture, and the resulting mechanical stresses. Register for this upcoming, January 5, ANSYS in ACTION session, to see how simulation from ANSYS makes the simulation of all these forces impacting thermal management in LEDs easy and intuitive.

We hope to see you at an ANSYS in ACTION session soon.

The post ANSYS in ACTION – Let Us Show You How appeared first on ANSYS.

For over 40 years, ANSYS training has been a reliable partner for engineers to increase their productive use of ANSYS software. With tight deadlines and demanding product design requirements moving CAE engineers into the spotlight, engineers are feeling the pressure to deliver accurate predictions of product performance in a timely manner, often times before a product is even built.

Project and product success ultimately hinges on the preparedness of the engineering team to perform the simulations necessary to support key engineering decisions. In an environment of evolving demands it is becoming a high priority for engineers to keep their skills current. Successful engineers therefore focus on learning more in order to stay on top and to move ahead.

Our users typically report one of three reasons for attending training:

• Getting started — learn the basics of one of the flagship solvers and related workflows
• Stay current — get a refresh, keep up-to-date with the latest release
• Expand — add more physics to improve best practices or solve a new class of engineering problems

Traditionally, we have delivered training in a live classroom setting with a live instructor and face-to-face discussion. Over the last years, we introduced virtual classrooms led by a live instructor that users could attend from their desk, thus eliminating any travel requirement.

With the start of the new year we are rolling out a new on-demand learning platform called ANSYS Learning Hub. As an on-demand platform the ANSYS Learning Hub becomes the one-stop-shop for all learning needs for ANSYS software. The ANSYS Learning Hub provides our users with easy access to self-paced learning, tools to manage learning goals, and instructor moderated ‘Learning Rooms’ with discussion boards and additional learnings outside of the standard curriculum.

Self-paced courses consist of independent core and optional modules, each including lectures in video format, hands-on exercises and quizzes. Self-paced courses are delivered in video format including theory, usage and practical demonstration. The videos are augmented by closed-caption text of the material presented. Closed captions are searchable, such that locating a specific section in a course module is very convenient.

The modular nature of the course material enables easy mix-and-match as required by the learner’s application. It’s easy to go back to review previously completed modules. Available at any time, at any location, self-paced courses offer the most flexibility.

Over 100 learning modules, organized in 10 self-paced courses, are currently available in the ANSYS Learning Hub along with the live and virtual classroom schedule. The ‘Learning Catalog’ enables easy search for courses dates and course materials. It takes only a few clicks to enroll into a class. Users build their learning plans, get a clear view on the learning history and view certificates for course completion.

Similar to a live classroom the Learning Rooms in the ANSYS Learning Hub facilitate interactions between the instructor and class participants. Learners ask questions and reflect upon the concepts learned. Instructors bring in their own experience, assist with the practical work or pose extra challenge problems to ensure that learners can independently solve an unscripted problem. In addition, Learning Rooms provide examples, tips & tricks, FAQs and a program of extra webinars on topics of interest. Learning Rooms are available to support virtual classrooms and self-paced learning.

The ANSYS Learning Hub is THE learning platform for the ANSYS software portfolio. Learners can choose when to learn and what to learn with easy 24/7 access. Annual ANSYS Learning Hub subscriptions are available now.

In the end, we hope the ANSYS Learning Hub helps our customers use simulation to design better products more efficiently. I’d like to thank all the customers who have tested the ANSYS Learning Hub and provided requirements and feedback, as well as the many experts from ANSYS who have configured and contributed to the system.

The post Get Up to Speed with ANSYS Training Options to #LearnANSYS appeared first on ANSYS.

ANSYS Fluent 18 has advanced erosion fluid dynamics modeling by adding three industry-standard models to the previous default model.

Erosion wear is the loss of material due to repeated impact of solid particles on a surface and causes major economic losses across diverse industries such as oil and gas, hydraulic transportation, and chemical processes. Erosion severely damages flow passages, valves and pipe fittings, leading to higher replacement costs as well as the loss of valuable production time. For example, some oil and gas fittings can fail after just 30 minutes of operation due to high erosion rates! Engineers need to quickly evaluate the erosion on dozens of design variations to find ways of stretching the part’s lifespan in order to reduce costs and maximize process up-time.

Erosion is a mechanical process that happens due to the repeated impact of solid particles on pipe surface. If the surface material is ductile, repeated particle impacts will result in the formation of craters and platelets; craters will grow with subsequent particle impact and eventually platelets are easily removed into the flow, Figure 1-a.  Brittle material on the other hand, will grow lateral and radial cracks under sand particle impact, which will grow and eventually form small pieces that are removed by continuous solid particle impingement, Figure 1-b.

Erosion is a complex phenomenon that depends on many parameters. Particle parameters can include the following:

Flow parameters, on the other hand, have a stronger effect on erosion as it determines particle concentration, particle impact angle, and impact velocity. Other parameters affecting erosion are properties of target surface, i.e. surface hardness  and multiphase effects[1].

Progress in understanding the erosion due to solid particles has been achieved by the use of computational fluid dynamics (CFD). CFD allows the accurate modeling of fluid flow and particle trajectory through pipelines and bends. Once the impact velocity and angle of the particles colliding against the surface are calculated, empirical correlations to quantify the erosion rate can be implemented.

Many empirical erosion correlations have been published in the literature. All include the impingement angle, impact velocity, particle diameter, particle mass, and collision frequency plugged into it. A typical erosion model has the following general form (default erosion fluid dynamics model in ANSYS Fluent)

The particle impact angle, impact velocity and mass flow rate are calculated directly using CFD. Nevertheless, the impact angle function, particle diameter function, and velocity exponent have to be supplemented as input to the solver.

Better Particle Erosion Fluid Dynamics Modeling

In addition to the default, three industry accepted erosion correlations have been added to ANSYS Fluent in ANSYS 18 to give you more flexibility: Finnie [2], Oka [3], and McLaury[4] Figure 2.

• The Finnie erosion model is more suited for ductile materials, where the erosion varies with the impact angle and velocity.
• The Oka model provides a more realist correlation by including the effect of wall material hardness.
• The McLaury erosion model was developed to predict the erosion rate of solid particles in water; it has been primarily used in slurry flows.

Erosion Models Available in ANSYS Fluent

Just like turbulence modeling, there is no one size fits all erosion model. Each erosion model has been empirically calibrated for a certain flow scenario, so one should consider the relevant flow conditions for each model before using any erosion model.

How to Perform Erosion Fluid Dynamics Modeling in ANSYS Fluent

Erosion rate is usually calculated after the flow field has been established in the domain. Actually, you can think of it as a post-processing step for CFD analysis.  So, erosion can be predicted in low particle loading scenarios, particle volume loading <10%, using the following steps

1. Solve and converge your CFD model, save case and data
2. Enable “Discrete Particle Model” from the model tree, and activate the Erosion/Accretion model from the “Physical Models” tab, Figure 3.

Discrete Particulate Model (DPM) Panel in ANSYS Fluent

3. Define particle injections in the model; set the particle diameter, injection speed and flow rate.
4. Setup the particle normal and tangential reflection coefficients in the wall B.C. panel for the walls of interest, Figure 4.

(a)

(b)

Particle Reflection Coefficients: a) Normal; b) Tangential

5. Select the appropriate erosion model in the wall B.C. panel, under the DPM tab. Four models are available: Generic Fluent , Finnie, McLaury, and Oka. Model parameters can be adjusted or parameterized if needed.

6. Run the flow for one iteration, this will be enough to release particles and calculate erosion rate on participating surface.

7. Display contours for the erosion rate at walls of interest. Different erosion models can be plotted at the same wall for comparison, Figure 5.

(a) Contour Panel

(b) Erosion Rate Contour—Oka Erosion Model

(c ) Erosion Rate Surface Contours—McLaury Erosion Model

Contour Plot of DPM Erosion Rate in ANSYS Fluent

Watch the Webinar

Learn more about what ANSYS 18 is delivering for fluids including the accuracy and advanced modeling in our ANSYS 18 Innovations Fluids webinar on February 16th.

References:

1. A. Hamed and W. Tabakoff, “Erosion and Deposition in Turbomachinery.” Journal of Propulsion and Power, Vol. 22, No. 2, pp. 350-360, 2006.
2. I. Finnie, “Erosion of Surfaces by Solid Particles.” WEAR, Vol. 3, pp. 87-103, 1960.
3. Y.I. Oka and T. Yoshida, “Practical Estimation of Erosion Damage Caused by Solid Particle Impact. Part 2:  Mechanical Properties of Materials Directly Associated with Erosion Damage.”  Wear, Vol. 259, pp. 102-109, 2005.
4. B. S. McLaury et al. “Modeling erosion in chokes”. Proceeding of ASME Fluids Eng. Summer Meeting. San Diego, California. 1996.

The post Better Particle Erosion Fluid Dynamics Modeling in ANSYS Fluent 18 appeared first on ANSYS.

My friend, a fellow Romanian, just told me a funny story. She just relocated to the U.S. and was asking her dentist “When will I have the root channel treatment?”. The dentist kindly replied “Did you mean root canal, my dear?” 

Human kindness is a beautiful thing. As a software developer, I often wish that computer programs would be equally technically kind. Most of them are not. Many times, when a user mistypes a command, applications crash.

This is pretty common when driving ANSYS Fluent from a remote program like Matlab. The Fluent scripting language is contextual. Consequently, the syntactic rules are changing during the simulation. For instance, substantially different commands are available when the CFD solver is changing the state, for example from meshing mode to simulation mode.

Fortunately, the aaS interface supports technical kindness. Consequently, when a command seems to be incorrect or incomplete, Fluent will switch to a “kind” response. Ultimately, the kindness will be offered as a clarifying exception. The exception should help the Matlab user understand the context. At this point such user could correct or complete the command.

My friend is using Matlab to drive ANSYS Fluent, so I decided to demonstrate the technique in snippet of Matlab code for her. Here it is for you:

Matlab Code Driving ANSYS Fluent

disp('Start Example')
load_ansys_aas;
orb=initialize_orb;
fluent=actfluentserver(orb,'aaS_FluentId.txt');
tui=fluent.getSchemeControllerInstance();
out='Report not received';
bIKnowHowToClarify=false;
try
   disp('Requesting <report summary>');
   out = tui.doMenuCommandToString('report summary -?')
catch ex
   ex_id=ex.identifier;
   if strcmp(ex_id , 'MATLAB:Java:GenericException')
      ex_object_class=ex.ExceptionObject.getClass;
      if strcmp(ex_object_class,'class AAS_CORBA.EYesNoQuestion')
         disp(ex.ExceptionObject.questionPromptWithDefaultAnswer);
         bIKnowHowToClarify=true
      end
   end
   if bIKnowHowToClarify==false;
      rethrow(ex)
   end
end
if bIKnowHowToClarify==true
   disp('Answering <No> to the clarification question...');
   disp('Requesting <report summary no>');
   out = tui.doMenuCommandToString('report summary no -?');
end
disp(sprintf('Output:\n%50.50s....\n',char(out)))

Program output

Start Example
Requesting <report summary>
write to file? [no]
Answering <No> to the clarification question...
Requesting <report summary no>
Output:
Fluent
Version: 2d, dp, pbns, lam (2d, double pre....

Additionally, I would like to note something in the code. Adding  the suffix “-?” to a command indicates that suggestions are welcomed. Similarly, the absence of “-?” will turn off Fluent “kindness”.

Finally, Fluent supports a set of four clarifying exceptions. Of course, they are properly documented in ANSYS manuals. Just search for “aaS” keyword.

For more information about Matlab Fluent cosimulation please check out How to Make Matlab Apps for ANSYS.

The post The (Technical) Kindness of ANSYS Fluent aaS, a Matlab Example appeared first on ANSYS.

Pipe exist everywhere. There are a wide range of applications involving pipes. For daily life, pipes are used in the water line for our house, the air conditioner of the car we are driving, and in the gas station where the gasoline and diesel are transported. Industry-wise, a lot of pipes are used for processing, gas and liquid transmission, transmission as well as extensively in power plants. power plants.

From a structural analysis point of view, a pipe is a slender structure with a tubular cross section that could be very long along the length direction. A beam can also have tubular structure, but most beams or columns are used for strength purposes. The dominant function of the pipe is used for transporting fluids and gases. The liquid/gas transporting could be hot, under high pressure, and also be viscous. We want to use a minimum pipe thickness to save material while still satisfying the temperature and pressure requirements.

Engineers whose main focus is designing piping systems typically rely on specialized analysis tools. Those niche tools have streamlined the process from creation of piping system geometry to final output per industry code. They are efficient for everyday pipe designers. However, for a structural design engineer who comes across piping structures or the piping geometry is relatively simple. Those specialized tools could be overkill. Also, there are observed limitations in those tools when it comes down to more detailed or advanced analysis.

ANSYS Mechanical, known as a generic purpose finite element program, provides a set of technologies and workflows that allows piping analysis to be an easy task. Some capabilities(contacts, detailed modeling, hybrid model) goes beyond what a typical piping software can do.

In this blog, I would like to give you a brief introduction into some technologies and features we have for piping analysis. To start with, let me introduce you three elements: namely pipe288, pipe289 and elbow290.  Pipe288 is 3-D 2-node pipe, pipe289 is 2-D 3-node pipe, elbow290 is 3D 3-node elbow. Purely by name, you can tell that elbow elements are used for elbow (per piping terminology).

PIPE288 and PIPE289 can handle both thin-walled and thick-walled (even a solid circular) cross-sections.  With the thick-walled option, a full 3D stress state is adopted.

PIPE288 and PIPE289 accept only circular cross-sections. The cross-section will remain circular during deformation (i.e., only uniform radial expansion is allowed). Therefore, PIPE288 and PIPE289 should only be used for straight pipe segments.

On the other hand, ELBOW290 allows for initially non-circular sections and accounts for general section deformation, including ovalization, warping, and non-uniform radial expansion.  With these advanced capabilities, ELBOW290 is suitable for pipe bends and straight pipe segments that may undergo large section deformation (e.g., collapse of the section).

Pipe288/289 elements can account for added mass (internal fluid mass), hydrodynamic added mass(external fluid), wave loading and buoyant effects. Pipe to surface contact (like pipe-lay on seabed) and pipe-to-pipe contact (like PIP) can also be addressed.

Now let’s go through some of the basic features related to pipe analysis in ANSYS Mechanical. Please be aware that ANSYS Workbench Mechanical only exposes a subset of the features of the pipe capabilities, but you can always insert commands snippets or use MAPDL to explore full capabilities.

Geometry:

Starting with geometry, you need a “line body”(ANSYS terminology) for pipe and assign tubular cross sections. If you build a geometry in ANSYS pre-processing tools like ANSYS SpaceClaim or DesignModeler, that is pretty straightforward. If your geometry has been pre-built in other tools, then you can bring them in ANSYS SpaceClaim. If it comes in as just lines then you can simply create some cross sections and assign to them. If it comes in as volumes, you can take advantage of “Beam Extraction” tool to automatically convert a volume to a line plus cross section. Once you load the geometry in ANSYS Mechanical specify “Model Type=Pipe”.

Meshing and Material:

Meshing and material specification is no different than if you are working on solid-based structures. You can also assign tabular temperature dependent properties (like stiffness).

Loading and Constraint:

Internal and external pressure, Temperature. “Line Pressure” can be used to apply external loading (like overburden load).

What does “Pipe Idealization” do? It is used to convert selected pipe elements to elbow elements, this is especially important for curved pipes. Since “Pipe Idealization” is using elbow290 element, which is a quadratic element at the solver level, please be sure to keep mid-side nodes when you are meshing.

Post-processing:

For common items like axial force, bending moment or shear force, these can be accessed through “Beam Results”. Additional output like hoop stress, internal/external pressure can be accessed using “Solution”->” Worksheet”->“ Create user defined result”.

You can use these results and do some result combinations, and compare with a pipe hand calculation spreadsheet. But we have something easy set up for you.

For those who are involved in gas transmission and distribution pipe systems, you will know ASME B31.8 well. For others, it is basically a requirement on how to process calculated stresses and compare that with code specified allowable. We have an ANSYS ACT APP for that! Please go to ANSYS App store and download an extension called “ASME Pipe Check”. It can save you some time. Here’s a snapshot.

Lastly, I want to highlight a unique point about ANSYS pipe simulation. I call it “Hybrid Model”. You can have combination of line, surface and volume structures in one system.  In this way, you can study a bulk structures with some piping components, and interactions between pipe and other components.  I am including a few snapshots using “hybrid model” here.

I hope this has been a good use of your time —  please SUBSCRIBE to the ANSYS blog for future discussions on pipe simulation and modeling.

The post Pipe Simulation Using ANSYS – A Quick Introduction appeared first on ANSYS.

If you turn on the TV or browse the internet these days, automation is a familiar topic. From smart homes to learning thermostats, the drive to save time and effort by automating repeated tasks is everywhere. We use words like ‘smart’ to indicate that our devices are no longer one-size-fits-all but instead adapt and can be programmed to better suit our needs and behaviors. So, too, should be the case with our engineering software! That is why we have spent over a year to add scripting, a broadly applicable interface for automation and customization of modeling, to the ANSYS SpaceClaim geometry modeling environment.

In a previous blog post, we introduced SpaceClaim scripting and how it can automate repetitive or tedious tasks. With ANSYS 18.1, we’ve taken it a step further and made it easier to share and use SpaceClaim scripts outside of the editing environment. By publishing scripts to dedicated buttons in the user interface or calling them from within a Workbench script, you can now use the power of scripting in more places than ever before. Furthermore, we have extended the interaction with scripts to allow for user input of selections and values during execution. Let’s take a closer look at these improvements.

Here we have the geometry for a vehicle braking system. One thing I am often looking to automate is the preparation of geometry models for simulation. In this case I would like to replace the bolts, colored in teal, with ANSYS beam and shell elements to accurately capture the connection between parts during simulation. Thanks to the “Record” feature, I can generate a script by simply using SpaceClaim modeling tools the way I normally would, and then generalizing the actions to apply them to a repeated set of objects. My completed script automates my modeling work.

Create simulation-related elements such as beams and shells using scripting

Once you have recorded a script, you are going to want to replay it. Thanks to the new “Publish” button in the editor, you can easily promote your script to a dedicated button in the SpaceClaim ribbon for easy use on other models. Better yet, you can share published scripts with others in your organization allowing them to benefit from the created automation. Given that authoring scripts is not for everyone, sharing is an important method for improving the productivity of entire teams.

When publishing a script, you can select a name, tool tip, and icon to be used

Critical to almost every script is control over the inputs. These can be geometric inputs, such as target faces or bodies to execute against, or they can be numeric, such as parameters or desired feature sizes. With ANSYS 18.1, scripts can now define an interactive tool that accepts selections and parameters during execution. Not only does this make it easier to provide input to scripts, but it allows for interacting with scripts in novel and unique ways. Script-defined tools look and behave the same as other modeling tools, meaning that you can now customize and extend SpaceClaim in an interactive way that feels very different and significantly more approachable than the traditional ‘running a script’. With SpaceClaim we strive to make automation as smart and easy to use as possible so that every engineer can customize and accelerate their work.

The Create Bolt script can be turned into an interactive tool that
accepts parameters and selections during execution

Scripting also lets you connect applications together and facilitate workflows. ANSYS Workbench is the leading tool for coordinating materials, boundary conditions, meshing, and results between all ANSYS applications. It supports scripting for customizing and automating these simulation processes. With ANSYS 18.1, you can now call SpaceClaim scripting from within a Workbench script to extend this automation through geometry creation and editing. From reading custom 3-D data formats to parametric creation of complex geometries, ANSYS scripting tools are now more capable and cohesive than ever. For more information, please visit our website.

The post Interactive Script-defined Tools Change the Game in Modeling appeared first on ANSYS.

In Part 1 and Part 2 of this three-part series, Mike Kuron discussed modeling techniques for printed circuit boards (PCBs), and thermal analysis of electronic components. In this final installment, I will address structural analysis of PCBs in terms of assessing their ability to survive vibratory load environments.

PCBs are used in most electronic products to mechanically support and electrically connect chips, capacitors, resistors, or other electronic components via soldered joints. Since many of these products will experience loading environments that include vibration loading, it is vital to determine the structural integrity of the PCB and its components due to these loads. This determination is often achieved using a random vibration finite element analysis.

The steps to perform a random vibration analysis of a PCB can be divided into three parts:

1. Create the finite element model including representations of PCB, stand-offs, and board components.
2. Perform a power spectral density (PSD) random vibration analysis.
3. Evaluate the PCB and component integrity based on the PSD response.

The main highlights, and some common practices, for performing these tasks will be briefly described.

Model Creation

PCBs can be very complex structures, usually consisting of a stack of layers of FR4 glass epoxy and copper foil, with vias connecting conductors between different layers. As described in Part 1 of this series, there are several approaches to modeling the board, and any of these approaches can be used in a random vibration analysis. However, the mapped material approach represents the best mix of accuracy and efficiency. This material mapping requires a fine, three-dimensional mesh to model the distribution of all the details in the board. The mapping is automated in ANSYS Mechanical. An example is shown in Figure 1.

Figure 1: Board mesh and mapped material properties.

Stand-offs are the columns that connect the board to the underlying structure. The board is typically screwed down to the stand-offs. It is typical to model the stand-offs with solid elements. The screws can be modeled with a simplified connection, such as stiff beams. The key is the ability to extract axial and shear forces at these support locations.

Depending on the structure to which the PCB attaches, the opposite end of the stand-offs can be either fixed, or they can be connected to the underlying structure which, at least partly, is included in the finite element model. The approach taken depends on the flexibility of the underlying structure.

Since there can be many components on the board, it is typical to identify a few critical components (with large mass/stiffness ratio or large footprint) that will be modeled with solid elements. The less critical components are modeled using discreet mass elements, and the remaining components not included in the model. However, since this is a dynamic analysis, the mass properties must be accurately represented, so the mass of the missing components can be accounted for by increasing the density of the PCB so that it has the correct mass and center of gravity. The details of the attachment of the critical components to the board are generally not modeled, unless very high accuracy is necessary. They are generally considered rigidly attached to the board. Figure 2 shows a critical component modeled explicitly, and other components represented by point masses.

Figure 2: Components modeled explicitly and with lumped masses.

Random Vibration Analysis

If a mode superposition approach is used, the analysis procedure starts with a modal analysis to determine the natural frequencies and mode shapes, as well as to supply the dynamic characteristics of the structure to the PSD analysis. The frequency range for mode extraction should be approximately twice the highest frequency for the applied excitation in the subsequent PSD analysis.

The participation factor calculation from the modal analysis should be reviewed to ensure that the ratio of the effective mass to the total mass is close to one in each direction of subsequent excitation (X, Y and Z). A general guideline is to assume that values greater than 0.90 are considered adequate for most applications. Mode shapes should be reviewed to verify expected response.

The PSD input in terms of frequency versus load must be defined. In most cases, the loading is described in terms of a base acceleration. International and company standards exist that represent the vibration requirement the structure must meet. Verification of the loading units should always be made. An example of a standard PSD input spectrum taken from International Standard IEC 60068-2-64 is shown in Figure 3.

Damping plays a critical role in the response and it is important to define accurate values for the damping ratio, which may be a function of frequency. If damping is not known, a low value will produce conservative results.

Figure 3: PSD spectrum input.

Evaluation of Structural Integrity

Since the PSD analysis predicts the random vibration response of the structure, the results will be in terms of probability of occurrence. It is typical to use 3-sigma probabilistic response for all PSD result quantities. 3-sigma represents a 99.7% probability that the result will be at or below this value if the loading is described by a normal (Gaussian) distribution.

The weakest link and most likely failure that would be expected in PCBs subjected to vibration loads would be the connection of the components to the board. If these fail, the operational electrical function of the board will be compromised. A popular electronic component life prediction method is by Steinberg , which states that components can be expected to achieve a fatigue life of approximately 20 million stress reversals in a random vibration environment if the displacement at the center of a perimeter-supported board is limited to the value Z:

Where:

B = length of PCB edge parallel to component (inch)
L = length of electronic component (inch)
h = height or thickness of PCB (inch)
C = constant for different types of electronic components
r = relative position factor

Constant C is a factor based on the type of electronic component being evaluated, with the table below containing values to use in the expression for Z.

Constant r is the relative position factor, defined as follows:

r = 1.0 represents the center location.
r = 0.707 represents a location toward the center of an edge.
r = 0.5 represents a corner location.

Once Z is calculated, the 3-sigma displacement at the center of the board can be obtained and compared to Z to assess the fatigue life. If the 3-sigma displacement is less than Z, the component is expected to achieve at least 20 million cycles.

This approach to component prediction is very basic, and there are others which introduce more complexity and accuracy. Which one is used depends on the level of accuracy and safety margins required. In any case, Z can be used to help identify the candidate components for more detailed modeling efforts.

If the mapped material approach was used to model the board, 3-sigma stresses can be extracted directly from the board and compared with endurance limits for board materials to assess fatigue life of the board itself. A relatively fine mesh along with the mapped material properties will provide a reasonable prediction of the nominal stresses in the board layers, since the mapping can be used to determine areas of FR4 and copper as opposed to a smeared or lumped property approach. Figure 4 contains a stress contour of a board. The maximum stress location can be compared to the material map to determine the material in which this stress exists.

Figure 4: Stress contour of layer of board.

3-sigma axial and shear forces can also be extracted at the stand-off connections and compared to the axial and shear capability of the screws to evaluate the response at these connection points.

Well that concludes our three-part series, but that’s not the end of the story. I’d love to hear how you approach the problems we tackled. Please comment on this post and let’s start a conversation.

Lastly, ANSYS offers a wide variety of solutions for printed circuit board manufactures. I invite you to check out solutions for Electronics Cooling and Chip Package System Design.

The post Thermo-Mechanical Analysis Methods for Printed Circuit Boards Part 3: Vibration Analysis appeared first on ANSYS.

There is a new CAESES^{® —} ANSYS app that allows you to plug any CAESES geometry model into ANSYS Workbench. With just a few clicks you are ready to run parametric studies, such as correlation or sensitivity studies, design of experiments (DoE), optimization, or six-sigma analysis, in a fully automated way.

Optimization of Complex Geometries

Many of you are very familiar with combining simulation tools in the ANSYS Workbench environment. Now, you can drag and drop to take advantage of efficient and robust CAESES  geometry models without any scripting required.

CAESES  customers often deal with rather complex geometries that are exposed to flow such as ship hulls, propellers, rotors and stator blades, ducts, manifolds, exhaust systems, turbines and many more. A recent example of a complex shape is shown below in the animation of a parametric shear head. Traditional CAD tools often fail to re-generate new design candidates of such complex parametric models in automated processes.

Complex shear head geometry for polymer injection molding: Variable and 100% robust CAESES model for automated studies

Preparation in CAESES

So how does this new connection actually work? First of all, you have to make sure that your CAESES  geometry model is ready with a set of design variables that control its shape. Export a *.fsc control file of the setup via the CAESES  file menu (file > export > fsc file) — that’s all you need.

Shape Optimization in ANSYS

In the ANSYS Workbench, you have to install the CAESES — ANSYS app (ACT extension) to make CAESES  available as a component in the ANSYS Workbench. Load the *.fsc file through the CAESES  component and update it. The generated geometries get exported and are loaded into the ANSYS Workbench automatically.

We recommend exporting an ACIS (*.sat) file which contains additional information for repeatedly identifying the different geometry parts. In CAESES, you can assign colors with user-defined names to the individual faces which are then transferred into the ANSYS Workbench as named selections. This is required to automate the meshing procedure where you should be using those named selections to reference the different patches of the model. You can find more information about patch coloring in the section simulation-ready geometry.

After the update of the CAESES component in ANSYS Workbench, the design variables of the geometry are automatically shown in the parameter set. New design candidates can now be generated by changing these parameters, either manually or by optimization tools, such as ANSYS DesignXplorer, ANSYS optiSLang or other 3rd party optimization tools.

No matter which CAE task you have to solve (e.g. CFD or structural analysis) or which product you actually use (e.g. ANSYS FLUENT, ANSYS CFX or ANSYS Mechanical) – this new connection works for all of them.

ACT App Development

We have worked together very closely with CADFEM GmbH in Berlin to develop this connection between CAESES^® and the ANSYS Workbench. In the first stage, our engineers got 3-days of training from CADFEM to understand how ACT works. Since ACT is quite intuitive and comprehensive, we were able to finalize the core requirements of the CAESES app within these 3 days in a joint effort. Back in our office, we developed the app a bit further to make sure the named selections worked and could be automated. We also implemented workflows with 2D geometries and shared topologies.

Right now, we are distributing the add-on to our existing customer base so that it can be applied in production. We are still collecting individual feedback to make sure that the app works for a variety of workflows and applications.

In the next weeks, we will submit the CAESES^® app to the ANSYS app store so that it can be installed directly from within the ANSYS Workbench user interface.

Once again, thanks a lot to the guys from CADFEM who provided very efficient support with the ACT development!

The post Shape Optimization in ANSYS with CAESES appeared first on ANSYS.

The LOBOmotorsports Team at FSAE Lincoln 2017

Students and faculty looking to start a Formula SAE team are faced with an uphill battle. The team needs to create an immense number of designs, simulations and prototypes just to qualify, let alone win the contest.

Few know this better than John Russell — the director of the University of New Mexico’s (UNM) LOBOmotorsports Formula SAE program for over 20 years.

During this time, Russell has learned a few tips and tricks to get an SAE car on the raceway.

When it comes to optimizing a Formula SAE car, Russell suggests starting with simulation software. He says: “ANSYS products have played a major part in validating our designs to withstand the forces and conditions we see during testing and competition.”

SAE Designers Focus on Validation First

FEA of a font upright during pure braking.

Russel notes that his design engineers first focus on validating all the components of the SAE car.

This is where static structural analysis using finite element analysis (FEA) comes into play.

LOBOmotorsports uses FEA to assess many of their parts, including:

• Uprights.
• Clevises.
• Control arms.
• Rockers and.
• Tabs.

“We will continue using ANSYS Mechanical to ensure our components can withstand the maximum loads from braking, cornering and acceleration,” says Russell.

According to Russell, the more you learn about the sturdiness of your designs, the more you will know how to lightweight them. This will help your car go faster and use less fuel.

Optimizing the Design of Your SAE Racecar

Thermal mechanical simulation of a front rotor.

Russell hints that as your team grows, it starts to apply what it learns from old designs. This optimization process improves future designs.

For instance, LOBOmotorsports wants to improve the thermal performance of its brakes and rotors.

“[We want to remove] the hot spots that appeared on the rotors in simulation,” says Russell. “These hot spots increase the need to replace the rotors over time and could cause cracks to form as the parts are heat cycled.”

It turns out that as the team improves the symmetry of its rotor design, it also reduces these hot spots. As a result, the team maintains the part’s symmetry as it is lightweighted.

“We maintain a symmetric design and remove similar amounts of material from each section,” says Russell. “Given the same track, our new designs should be able to drive longer and harder than previous cars.”

LOBOmotorsports builds SAE cars that rely heavily on simulations. It suggests that new teams will also benefit from learning simulation software.

“Using the tutorials to learn the program helps immensely,” says Russell, “It allows our team to instantly transition our models into FEA. With ANSYS’ user-friendly method to import [CAD] models, we can quickly analyze and iterate our parts to ensure a competitive design at competition.”

To gain access to the ANSYS portfolio of free student software, click here.

The post Tips to Design a Formula SAE Racecar with Simulation appeared first on ANSYS.

Gas Turbine spray pattern colored by temperature using ANSYS Fluent

If you perform as many gas turbine combustion and flameout simulations as I do, then you must be praying for the day that quantum computing becomes a reality.

The fine meshes you could process with quantum computing power would make simulating turbine behavior a breeze.

Most CFD and combustion simulations rely on a limited, affordable mesh count. Engineers use coarser meshes than they would prefer for a fast turnaround with the available hardware resources.

However, simulation experts must ensure that these coarser meshes still achieve the desired accuracy. After all, these simulations are used to prevent accidents such as the engine flameouts of British Airways’ Flight 9 in June 1982 and Flight 38 in January 2008. Fortunately, mathematical models provide engineers with the accuracy they need on the mesh they can afford.

For example, finite rate effects — which result because combustion reactions do not occur instantly but take some time — are leading contributors to flameout in gas turbine engines. Simulating finite rate effects is challenging because you may need hundreds of scalars and fine-scale information to achieve accurate results.

Traditionally, this level of detail is beyond the affordable mesh resolution. However, ANSYS Fluent users can use some cool tricks to simulate near-flame extinction using only a few scalars and a relatively coarser mesh than you might expect.

What Causes Turbine Flameout?

Flame extinction is caused by an excessive amount of heat transfer outside of the reaction chamber or a significant loss of chain-branching radicals. If either of these events happens, the heat loss is greater than the heat generated by the burning fuel, and the flame blows out — hence, the term “flameout.”

A simulation of turbine flameout using ANSYS Fluent

Flameout conditions affect gas turbine engines at high cruise altitudes due to weather effects, fuel starvation, compressor stall or other mechanical failures.

Engineers use simulations to model flameout and near-flameout conditions. This empowers engineers to study, predict and limit flameouts by improving turbine designs.

How to Model Engine Flameout in ANSYS Fluent

Cambridge bluff-body swirling burner

Engineers simulate flameout by accurately modeling the flame’s finite rate transient effects and its interaction with small turbulent eddies.

Flameout conditions occur under a low Damköhler number (Da). The Da number is defined as the ratio of the characteristic turbulence time scale and the characteristic chemical time scale.

ANSYS Fluent can simulate swirling spray flames near extinction in a Cambridge bluff-body swirling burner. These simulations can then be used to compare a stable n-heptane flame (H1S1) and an n-heptane flame that is close to blowout (H1S2).

Six-vane swirler and bluff-body geometry

The simulation shows that the spray pattern is a 60-degree hollow cone (see figure below).

Spray pattern colored by temperature using ANSYS Fluent

A stress blended eddy simulation (SBES) model is used to predict near-wall heat losses. This model accurately predicts the boundary layer conditions near the wall using a stress blending technique.

The SBES model resolves the near-wall boundary layer using a Reynolds-averaged Navier–Stokes shear-stress-transport (RANS SST-k-w) model. However, large eddy simulations (LES) are used to resolve the core turbulent flow.

The blending of the RANS and LES models allows for better near-wall heat transfer modeling and alleviates the need for a fine mesh near the wall. This makes flameout simulation more efficient.

A map showing where these two fluid dynamics models are used can be seen below.

SBES blending function. Red indicates RANS modeling and blue indicates LES modeling.

A flamelet generated manifold (FGM) model in a Eulerian-Lagrangian framework is used to model the combustion of the system. The FGM models the complex evolution of the turbulent turbine environment with only a few scalars.

In other words, Fluent’s FGM can improve the efficiency of your simulation. The FGM details the chemistry as a function of the scalars, including:

• Mixture fraction mean.
• Mixture fraction variance.
• Progress variable mean.
• Progress variable variance.
• Mean enthalpy.

Finally, a conjugate heat transfer model is used to better predict the heat transfer between the flow and the bluff-body surface. The bluff-body wall temperature distribution is shown below.

Bluff-body wall temperature distribution

How Flameout Simulations from ANSYS Fluent Compare with Experiments

Instantaneous flame isosurface at T=1699-2200 K colored by OH mass fraction

An instantaneous flame isosurface model shows how FGM can capture finite rate effects. The model (on the left) depicts an instantaneous snapshot of the flame surface which is colored by the OH mass fraction.

The isosurface model shows a few holes in the flame. These holes reflect local extinctions. However, the data also shows that these holes will eventually reignite. This study found that the data in this isosurface model compares well with experiments after the data has been averaged.

Another flame surface can be seen below. This image is colored by the flame’s height above the bluff body.

Mean flame brush visualized by an iso-clip of mean temperature at a range of 1600-2000 K

The simulation shows that the flame is lifted about 4-6 mm, in good agreement with experiments.

H1S1 flame liftoff height

In-addition, the simulated spray’s mean axial velocity shows good agreement with experimental measurements at different locations.

Mean spray axial velocity

In summary, the results shows the success of ANSYS Fluent’s combustion tools to predict near-extinction characteristics.

If you want to learn more about ANSYS solutions for combustion and reacting flows, join our webinar “Using Detailed Chemistry in ANSYS Chemkin-Pro for More Accurate CFD Combustion and Reacting Flow Simulations” Aug. 21, 2018, at 11 a.m. (EDT).

The post How to Efficiently Simulate a Gas Turbine Flameout appeared first on ANSYS.

ANSYS 19.1 simplifies third party app integration so you don’t make Frankenstein’s monster.

You can’t call yourself a simulation master until you Frankenstein numerous third-party applications into one model.

Sure, ANSYS Workbench makes it easy to connect internal tools like Fluent and Mechanical.

However, this hasn’t always been the case for third party apps — or even competitive software.

ANSYS’ first attempt at simplifying third party app integration was with External Connection. However, customer reviews told us that this option required a bit too much effort.

These customer reviews led to the creation of ANSYS ACT. This tool simplifies the customization of simulation workflows. ANSYS 19.1 expands ACT to include the Workflow Designer.

The Workflow Designer allows you to add any script or executable into your Workbench schematic. It even parses the input-output files of your parameters automatically.

You can then publish and build extensions of your third party integration. This extension can then be used by colleagues, partners or customers who use Workbench.

To see how to build these extensions, watch this video:

You no longer need a lightning bolt of inspiration to stitch together a Frankenstein of ANSYS solutions and third-party software. All you need is an executable and a few minutes to make the connection (a)live.

To learn more, check out the ANSYS simulation platform.

The post Integrate Third Party Apps into ANSYS Workbench without Creating Frankenstein’s Monster appeared first on ANSYS.

The speed and accuracy of small-sliding contacts will make it a preferred choice compared to finite-sliding contacts.

I’m often asked, “What is the best contact type for my structural analysis?”

I can’t answer this for every model in existence. However, I can say that ANSYS Mechanical made small-sliding contact the default contact type in small-deflection models or any bonded contact pairs.

Small-sliding contact can solve problems that finite-sliding contact may have difficulty solving. It also maintains sufficient accuracy while boasting a lower computational cost.

ANSYS has performed extensive tests using small-sliding contacts to represent bonded contact pairs in small deflection models. It was found to be an accurate and computationally light option — if your simulation has an absence of large sliding.

How Small-Sliding Contacts Work within Your Structural Analysis

The small-sliding contact assumes the contact interface between two parts will experience minimal motion during the entire analysis. To be exact, the contact defines “small sliding” as movement that is less than twenty percent of the contact length. For large deflection analysis, this option still permits an arbitrary large rotation.

Representation of the small sliding contact

Each contact detection point (ξ₀, η₀) interacts with the same target element throughout the course of the analysis (as illustrated above). These interactions are determined from the initial conditions.

Advantages to Using a Small-Sliding Contact in Your Structural Analysis

Small-sliding contacts do not appear to slip off the edge of a target segment and do not encounter penetration shock.

ANSYS found that the small-sliding contact improves the solution’s robustness and efficiency.

For one thing, the small-sliding contact doesn’t appear to slip off the edge of a target segment. It also doesn’t encounter penetration shock.

Additionally, the logic behind the small sliding contact can solve complex contact models that the finite-sliding logic has difficulty with. This is especially true in models that have a low-quality geometry, mesh and non-smooth contact interfaces.

Another advantage is that the nodal connectivity of the contact element is formed only once at the beginning of the analysis. The value then remains unchanged for each iteration of the solution. In comparison, the ANSYS 18.2 version of the finite-sliding contact reformed the nodal connectivity of the contact element at each iteration. Not having to perform this calculation at each iteration will certainly save on your computational costs.

Similarly, the sparse solver can also reuse the same matrix structure throughout the simulation. This avoids a costly sequential step that orders equations at every iteration. Like the nodal connectivity, ditching this step leads to significant performance improvements and better scalability.

Choosing Between Finite-Sliding and Small-Sliding Contacts for Your Structural Analysis

Small-sliding contact will improve many simulations, but it isn’t always valid. Check your results to see if another contact type is a better option.

In general, small-sliding contacts achieve results that are very similar to those of finite-sliding. However, this presumes that the small-sliding assumption remains valid.

The logic behind the small-sliding contact can cause nonphysical results if the relative sliding motion does not remain small. If large sliding occurs, result accuracy is affected. Your simulation may even experience convergence difficulties if there is too much sliding.

As a result, you will need to ensure that the small-sliding assumption is valid throughout the analysis. This can be done by tracking the contact results and outputs. The ANSYS platform can also monitor sliding violations as the solver iterates.

In the event that the small-sliding contact isn’t valid to your simulation, it is recommended that you use the finite-sliding contact instead.

None the less, small-sliding contacts typically offer improved solution robustness, efficiency and speed when compared to finite-sliding contacts. Therefore, if it is valid to your solution, slide it into your model.

To learn more about the small-sliding contact, click here.

The post Small-Sliding Contacts Can Improve your Structural Analysis appeared first on ANSYS.

Engineers use response surface methodology (RSM) to understand bioreactor performance.

It’s amazing to think of all the products created in bioreactors. The medications we take, the beer we drink and the yogurt we eat are all made in bioreactors optimized for the manufacturing of these products.

Unfortunately, optimizations take a lot of work and data. The first step is to perform a design of experiments (DoE).

The DoE requires that numerous simulations be performed to map out the design space in the form of response surface methodology (RSM).

The computational resources to perform all of these simulations can be burdensome — even prohibitive. Fortunately, engineers can use cloud-based high-performance computing (HPC) to speed up these simulations in a cost-effective manner.

Reduce the Computational Time of Simulation Projects with Cloud Computing

Comparison of solution speed scale-up with different mesh densities

Cloud-based HPC offers the computing power of numerous cores to a simulation project. This will speed up the DoE, as each simulation’s computational time is reduced.

UberCloud offers cloud-based HPC as a software-as-a-service (SaaS) that is tailored for computer- aided engineering (CAE) software like ANSYS simulation products.

This cloud computing resource is available in over 50 data centres around the world and can help:

• Simplify software portability through browser-based access.
• Offer instant use of engineering workflows and computational hardware through CAE- application software containers.
• Maintain computational scalability across multiple compute nodes.

For this reason, we partnered with UberCloud to show how HPC can be used in bioreactor design.

How to Simulate a Bioreactor in ANSYS Fluent

Iso-surface of gas volume fraction colored with bubble diameter

Engineers can simulate the water and air present in the bioreactor using Fluent’s Eulerian multiphase model.

A population balance model with quadrature method of moments (QMOM) can be used to simulate how bubbles coalesce and break up.

This methodology predicts the overall gas distribution and the distribution of bubble size throughout the tank.

These predictions are key to determining the bioreactor’s mass transfer rate.

These simulations were run using the UberCloud platform in the Microsoft Azure cloud data centre in Singapore. Fluent showed linear scalability on UberCloud throughout the study of the bioreactor.

Scalability study based on a 688K polyhedral mesh

Each simulation — running on 168 cores in the cloud — took less than an hour (versus a week on a typical workstation). As a result, mapping out the design space took less than 12 hours.

RSM outlining the average mass transfer coefficient versus gas flow rate and agitation speed

The next step employed ANSYS DesignXplorer to generate the RSM. In this example, the RSM outlines how the mass transfer coefficient is affected by the agitation speed and gas flow rate within the bioreactor.

The results show that combining UberCloud with Fluent and DesignXplorer streamlined the DOE. This setup also reduced simulation time without investing the entire development budget on HPC.

To learn more about how UberCloud can speed up ANSYS products, click here.

The post Optimize Bioreactors Quickly with Cloud-based HPC appeared first on ANSYS.

Set up your transformer simulations in about 15 minutes.

The last thing consumers want is to plug in a new electronic device and smell burning circuitry. Therefore, engineers must carefully design the transformers which power small electronics using the proper voltages and currents.

Simulation helps engineers test their transformer models in various setups, geometries and scenarios. When engineers bring this testing into a digital space — instead of using physical prototypes — they can certify, optimize and get their transformers to market faster and on budget.

“People can create electronic transformer simulations manually but it’s very time-consuming. Just one setup could require a whole day,” says Mark Christini, lead application engineer at ANSYS. “To address this, we released the Electronic Transformer app for ANSYS ACT. The app provides a way to set up a transformer or inductor simulation in about 15 minutes and then solve it.”

How the Electronic Transformer App Works

Finite element analysis (FEA) of a transformer that was set up by the Electric Transformer app.

The Electronic Transformer app helps engineers set up Maxwell 3D eddy current simulations within the ACT platform. With this tool engineers can:

• Choose a core geometry from a library of (15) Philips and Ferroxcube shapes.
• Define winding strategies:
  • Planar or wound.
  • Rectangular or circular cross section conductors.
• Select materials from a library.
• Add new materials to the library.
• Completely set up the 3D finite element analysis (FEA).

“A wizard is used to input parameters in three steps: core definition, winding definition and analysis setup,” says Christini. “From there you can either create the Maxwell model and stop — or solve it completely. Once solved, users will get a characterized frequency-dependent model of the transformer that is compatible with Maxwell’s integrated multidomain circuit simulator, Simplorer, so they can then test the design within a circuit simulation.”

Optionally, the engineer can manually set up an electrostatic simulation if they wish to assess how capacitance impacts the design.

The Electronic Transformer app is designed to set up the simulations of transformers:

• In the 10kHz-1MHz range.
• With linear, frequency-dependent permeability.
• With Steinmetz core loss coefficients which consider frequency effects.

Thanks to the Electronic Transformer app, consumers can be spared the dread of smelling burning electronics. To get a copy of the Electronic Transformer app, visit the ANSYS app store.

The post How to Set Up 3D Transformer Simulations in 15 Minutes appeared first on ANSYS.

Computational fluid dynamics (CFD) can be esoteric. Every fluid dynamics simulation comes with a laundry list of best practices, turbulent models, geometry meshing needs and repetitive tasks.

Just a few CFD applications in ANSYS Fluent. Each one requires engineers to follow a different set of best practices.

Leading CFD workflows ensure that all this information is available to the engineer by enlisting a shotgun approach. They give you all the information you could possibly need at once. This information overload puts a big strain on industry to ensure their engineers are trained to know which tools to use.

But do engineers need — or want — to know or train themselves on 27 turbulence models when only three are relevant to their application? No, they don’t. They just want the answer so they can solve a problem and improve their designs.

CFD simulation software needs to provide a task-based workflow that can simplify these best practices for novices and offer all the tools experts need. These workflows could reduce or significantly decrease the amount of training required to use CFD software.

How to Simplify Computational Fluid Dynamics with Workflow Improvements

CFD software should be like a pool with lanes. You can go anywhere or follow paths that help get you where you need to go.

Imagine your CFD simulation platform is an Olympic- sized swimming pool.

Traditional workflows offer the “free swim” option. You gain access to the whole pool of tools within the software. You can go anywhere and do anything.

But what if simulation platforms offered users “lane swimming” options? In this scenario, each user is given a path through the pool that pertains to them.

Within CFD simulation platforms, these lanes would represent task-based workflows. These workflows would be:

• Tailored to the current task or application.
• Governed by best practices under-the-hood.
• Able to offer users relevant choices and options.
• Automated with respect to tedious activities.
• Customizable to allow users to create internal workflows and best practices.
• Flexible so power users can still access all the tools they need.

Core users shouldn’t see these workflow improvements as developers taking tools away. Task-based workflows are very flexible. They offer users an easier way to set up their simulations but they also let more seasoned users go off the beaten path.

You could imagine a user accessing a tool that is outside of their task-based workflow as swimming under the lane ropes of the pool. You can do it, but you should have the experience to do it properly.

However, for the average user, these simplified workflows could save a considerable amount of process time and mouse clicks.

What Does a Simplified Computational Fluid Dynamics Workflow Look Like?

Task-based workflow from ANSYS Fluent.

The key to simplifying the CFD experience is the creation of single-window, task-based workflows.

The idea is to replace much of the user’s knowledge with an intelligent platform that codifies best practices into the workflow, under the hood.

Instead of tossing users in the deep end of the pool, ANSYS suggests dropping users hints and suggestions. To improve the customer experience, make it easier to input data, make choices and correct issues.

To learn more, watch the webinar ANSYS Fluent Innovations Speed CFD Simulations to Help Engineers Get Accurate Results with Less Training or pay close attention to the release of ANSYS 19.2.

The post How to Get Accurate Computational Fluid Dynamics Results in Less Time appeared first on ANSYS.

Optimal design of a Nautilus-based dual-usage turbine

High resolution images of engineering simulations are fantastic marketing materials. Just look at all the entries we get for the ANSYS Hall of Fame.

If you had the cash, which yacht would you buy:

• The one with a gorgeous print graphic of a ship optimized by simulation software?
• Or the one that says “best in class” seven times in a 30-word ad?

“But, wait a minute, I’m an engineer not a marketer,” you say aloud. “How can I build a simulation image to wow my customers and the ANSYS Hall of Fame judges?”

Well, you follow some best practices.

Printing High Resolution Images of Simulations? Watch those Pixel Lengths.

Comparison of a screen capture blown by a factor of three

Unfortunately, a screen capture from your ANSYS workflow won’t impress anyone.

By the time you blow up your image to an appropriate size, you may as well render it with an Atari 2600.

Print media typically needs a high resolution of about 300 dots per inch (dpi). So, to determine how many pixels your image needs to be you must know the printed length (in inches) and multiply that by 300 dpi.

If your image needs to be printed at 3 inches at 300 dpi, then it needs to be at least 900 pixels long.

Remember, you can always make an image smaller, but you can’t always make it bigger. If you’re not sure how large the image needs to be then you might want to make it a few thousand pixels long and wide.

Oh, and use an .eps or .tiff file format. No point wasting your time with all these pixel measurements if the .jpg format is going to compress the file anyway.

How to Produce Good Print Graphics of Simulation Images

Optimized muffler created by HENN for the automotive industry.

Engineers are used to having images portray technical information. Marketers need images to tell a story.

You might recognize a muffler from a mile away, but your customers may not. Consider having a marketing photo of the whole product and then a close-up of the simulation of the part.

Take a look at one of our 2017 ANSYS Hall of Fame winners, HENN. It used multiple images to tell a story:

• Hero: HENN.
• Wise helper: CADFEM.
• Goal: Create customized mufflers.
• Journey: Multiphysics and parametric equations.
• Setting: Automotive industry.

Pay close attention to the streamlines flowing through HENN’s simulation. They are quite thick. This makes it easier to see in print media. You may need to have similar exaggerations for your vectors, meshes, graphs, axes labels or any other lines or small print.

Also notice how HENN’s image shows a graph. You can see that the labels and axes on the graph  border, but don’t obscure, any part of the car. This appears to be done with care. Make sure the product you’re selling isn’t being overshadowed or covered.

In addition, you should:

• Use a white or transparent background.
• Have clean outlines of your images.

How to Describe a Simulation to Customers, Marketers and ANSYS Hall of Fame Judges

So, you have this cool high resolution image of a simulation.

Now what? You need to show the images to the marketing team for feedback and graphic designers for touch-ups.

But you can’t just hope your team understands what all these images are: They are marketers not engineers.

Keep your explanation short, sweet and simple. You are describing a cool picture not defending a thesis.

When you explain your simulations to a room of nonexperts limit your talk to an elevator pitch. If you can’t finish it in 10 seconds, it’s too long. If you’re stumbling over your words, they are too complicated.

Well now it seems like you have some cool simulation images and a short description. Why not use them to try to win a SoundLink Revolve+ Bluetooth speaker   from the 2019 ANSYS Hall of Fame? To enter, click here.

The post Simulation Best Practices: High Resolution Images that Impress Customers appeared first on ANSYS.

Traditionally, CFD experts and novices won’t be happy working with a hard-to-mesh geometry like this. However, Mosaic technology makes building this mesh much easier. (Courtesy of Centre for Sports Engineering Research, Sheffield Hallam University)

Mesh creation is one of the biggest reasons engineers have a hard time setting up a computational fluid dynamics (CFD) simulation.

Even CFD experts can have a hard time building a fast and accurate mesh from complex geometries and flow regimes.

The trick is that engineers need accuracy near the thin boundary layers, which requires a very tight mesh.

However, this tight mesh slows down the processing of the fluid’s bulk without adding any accuracy.

The solution is to have multiple meshes — with different densities and element types — at different regions of the geometry. This would give you meshes that are accurate at the boundary and fast in the bulk.

Unfortunately, it’s a challenge to connect these meshes as the nodes, element types and element sizes in the bulk and boundary do not align. Engineers can use tetrahedral elements to transition between the meshes. However, these transitions create too many elements, nonconformal elements or low-quality elements.

ANSYS’ newly introduced Mosaic technology can help engineers connect the disparate meshes in their CFD simulations. The tool uses general polyhedral elements to transition between the layered elements hear the boundary layer and hexahedral elements in the bulk.

Building a Computational Fluid Dynamics Mesh with Mosaic Technology

A Mosaic mesh that connects hexahedral elements in the bulk to isotropic elements in the boundary using polyhedral elements.

The Mosaic technology can connect any mesh to another mesh — automatically..

Now engineers can easily build meshes that utilize the best elements for each region of their geometry.

Each element has their advantages and disadvantages. For example, polyhedral elements are best suited to complex geometry while hexahedral are more efficient.

Mosaic meshing allows engineers to use the elements they want, where they want them and without worrying about computational time.

In fact, when Mosaic meshes are compared to hexcore meshes of the same accuracy, engineers needed fewer cells and a third of the memory, and got their solutions in half the time.

To learn more about Mosaic meshing technology, attend the webinar Fluids Innovations in ANSYS 19.2.

The post How to Build a Computational Fluid Dynamics Mesh for Fast and Accurate Simulations appeared first on ANSYS.