In previous posts, we showed you how to parameterize DesignModeler, Spaceclaim and Creo Parametric.  Very recently, we had for for creating Named Selections and Parameters in NX.  Today, we finally get to one for Catia. I don’t have Catia installed on my computer, so thanks to Richard Mitchell, UK Sales Support Manager for recording the video.

Simulation driven product development has been a key theme at ANSYS for well over a decade, we often just refer to it by its acronym.  It is the reason that ANSYS Workbench was designed to be a parametric and persistent platform. Tools like DX can help you drive those parameters, but first, you need to parameterize your model!

You can parameterize the physics or even the meshing, but being able to parameterize the CAD using our bi-directional CAD interfaces is a real ANSYS Advantage.

To create a Named Selection in Catia:

1. Select the entities to be named
2. Navigate to “Tools => Publications”
3. Give the publication an appropriate name (keep in mind that you can use a filter such as a prefix “NS_” if you don’t want all your publications to be seen as Named Selections in Workbench
4. OK
5. These appear in the “Publications” branch of the tree in Catia

To create a parameter in Catia:

1. Go to “Tools => Formulas”
2. Find the dimension you want to “parameterize”
3. Give it a better name (again, many experts like to use the prefix “DS_” so they can filter for it later)
4. Apply

In order to transfer these named selections and parameter to other systems in Workbench, don’t forget to turn on the Basic Geometry Options for parameters and Named Selections and that the filters are appropriate (or cleared to bring in all the parameters or Named Selections).

You can drive a parameterized model by adding rows to the table of design points, or you can automate the process with design exploration and optimization tools, such as ANSYS DesignXplorer.

The post How to Create ANSYS Workbench Parameters and Named Selections with Catia appeared first on ANSYS Blog.

Flows around aerodynamic bodies, like aircraft wings, helicopter blades, wind turbines and turbomachinery components develop boundary layers that, to a large extent, define their performance. The boundary layers can either be laminar or turbulent depending on numerous factors, like Reynolds number, freestream turbulence levels and surface roughness, to name a few. Understanding which type of boundary layer is present, and the location of the laminar-to-turbulent transition point under varying operating conditions, is essential for accurate predictions of the performance of aerodynamic devices.

For many years, modeling the transition from laminar to turbulent flow was one of the most difficult challenges of computational fluid dynamics (CFD), although many industrial flows have Reynolds numbers in the range of 10^4 to 10^6 — regimes in which significant portions of the boundary layers can be laminar. Our ANSYS team succeeded in solving this problem about 10 years ago with the Local-Correlation-based Transition Modeling (LCTM) approach. LCTM successfully introduced transition effects into general CFD. The first model (called γ-ReΘ) solved two transport equations and incorporated experimental correlations to trigger the transition onset. The model formulation was strictly local and, therefore, fully compatible with modern general-purpose CFD codes.

In a new article recently published in the Springer Journal Flow Turbulence and Combustion, we present a second generation model that simplifies the original γ-ReΘ model of the LCTM concept, reducing the number of equations to be solved from two to one. The new transition model (called the γ-model) is now available in ANSYS CFD solutions.

By reducing the number of transport equations to be solved from two to one, the new γ-model substantially decreases the complexity and solution time of boundary layer simulations. The γ-model is also more robust because an even wider range of flows, both generic and industrial, was considered during model calibration, relative to the γ-ReΘ model.

Comparison of the two models with experimental data reveals the improved accuracy of the γ-model. Figure 1 shows a NACA 0021 airfoil. Such thick airfoils are representative of wind turbine blade sections that require proper simulation of the transition onset location to predict performance.

Fig. 1 Computational mesh for NACA 0021 airfoil.

Figure 2 shows the lift coefficient CL of the airfoil vs. angle of attack. The green curve shows a fully turbulent simulation. The red and the blue curves represent the γ-model and the γ-ReΘ models, respectively. Both models predict a significantly closer agreement than the fully turbulent simulation relative to the experimental data. The γ-model is even more accurate in the prediction of stall onset for this case. In addition, the γ-model is substantially less complex and has enhanced capabilities.

Fig. 2. Lift coefficient for NACA 0021 airfoil.

A more complex test case, involving a multistage compressor investigated experimentally at the University of Hannover, is shown in Figure 3. Details are given in the Springer article.

Fig.3 General view of the test rig for four stage high-speed axial compressor. Courtesy TFD Hannover.

Figure 4 shows the efficiency of the compressor for different mass flow rates. The blue curve represents the fully turbulent simulation without a transition model; the red curve demonstrates the improved simulation accuracy when the γ-model is added. The agreement between simulation and experiment is significantly closer when transition is taken into account.

Fig. 32. Efficiency at design rotational speed.

The γ-model is already in industrial use, and has been applied to a wide range of flows, covering turbomachinery blades, wind turbines and racing cars with good success. The model has recently been extended for inclusion of crossflow instabilities, which will be covered in a separate article.

Together with my collaborators at General Electric, we published these results in a paper, A One-Equation Local Correlation-Based Transition Model. Normally this is behind the publisher’s paywall but for a limited time only, you can download a free copy. Act now, this offer expires October 15.

The post Taking Laminar-Turbulent Transition Modeling to the Next Level appeared first on ANSYS Blog.

There were a number of new and exciting workflow enhancements included in ANSYS 16.0 for those who design and analyze rotating machinery to make data transfers and simulation setup easier. Here are the top five enhancements:

1 – BladeGen to BladeEditor

In ANSYS16.0 it is possible to load BladeGen data into BladeEditor in ANSYS Workbench. Users could always link BladeGen to BladeEditor (i.e ImportBGD function) in ANSYS Workbench, but to perform a LoadBGD command, it was required to go into BladeEditor and find the BladeGen file to load manually.  With the Create New > Geometry feature from the BladeGen (right click menu shown below) this process is much easier.

2 – Turbo Setup for compressors

ANSYS Workbench 16.0 has a new component system named Turbo Setup. This will quickly create a new throughflow or CFX analysis for different impeller geometry, mesh sizes or operating conditions. Check out this video to see how it works.

Currently this can only be used for compressor geometries. If you turn on beta features, you will also see that you can setup a speedline or compressor map from the Turbo Setup panel.

3 – BladeGen to Vista TF (ThroughFlow)

If you watched the video above, you may have noticed it is now possible to connect BladeGen to Vista TF. Of course, if you want to run multiple blade rows using Vista TF it is still required to connect Vista TF to a BladeEditor model that contains all stages.  But if you want to do a quick throughflow analysis on a single component in BladeGen, it is now possible!

4 – Radial Element Blade output for Vista RTD (Radial Turbine Design)

In Vista RTD there is now a drop down menu for Spanwise distribution.  Selecting General will provide geometries as created in R 15.0, but selecting Radial all camberlines will have the same theta value at any given axial coordinate when exported to BladeGen or BladeEditor.

5 – Vista CCD (Centrifugal Compressor Design) to CFX

You can now take a design directly from Vista CCD to a CFX setup. This uses the Turbo Setup feature outlined above. Right clicking on the Vista CCD component and selecting a new Turbomachinery Fluid Flow will populate the Turbo Setup component in Workbench with information such as speed, flow rate, etc, and geometry from Vista CCD. A geometry component and Turbomachinery Fluid Flow component will automatically be created as shown below. Updating the project will result in a CFX simulation at the specified design point with a report from CFD Post. All with one click!

I think these features are pretty slick and they should make more productive by reducing the time it takes to create different workflows in ANSYS Workbench!

The post 5 Improved Workflows for Rotating Machinery Design and Analysis appeared first on ANSYS Blog.

Many structural analysis models that use shell elements consist of a large number of bodies that need to be connected together to create a valid analysis model. These structures are typically manufactured by welding, for example ship structures.

There are a number of methods that can be used in ANSYS Mechanical for creating this type of model, which requires the geometry to be meshed and connected.

1. Connect all geometry together prior to meshing, then mesh. This requires the geometry to be created as a Multibody part in either Design Modeler or Spaceclaim, which will be meshed with common nodes at joints that exist in the geometry.
2. Bring the separate bodies into Mechanical and use contact elements (edge to face and edge to edge).
3. Bring the separate bodies into Mechanical and use mesh Connections to join the independent meshes at joints that exist in the geometry.
   

Methods 1 and 3 require the geometry to have lines that exist in the geometry at joints, which can be done in Design Modeler by Tools>Joint and in ANSYS Spaceclaim by Prepare>Imprint.

Method 3 is the recommended best practice from ANSYS 16.0 onwards. As an example, the ship geometry below consists of 191 bodies and 10070 faces. The comparative times to create a connected mesh are:

Method 1 (Multibody part) 380 seconds.
Method 3 (Mesh connections) 66 seconds to mesh, 49 seconds to connect, total 115 seconds.

It can be seen that using mesh connections in this example is 4 times quicker that using a multibody part. Meshing separate bodies takes advantage of parallel meshing, which was introduced at ANSYS 15.0.

Method 2 (contact) is not generally recommended as a large number of constraint equations get added to the model, which will slow the solution considerably.

It should be noted that the edge connectivity can be displayed on the mesh as shown below.

For more information see how engineers at MMD Mineral Sizing are using this approach to fabricated structures in this on-demand webinar.

ANSYS Convergence Webinar Series: The Role of ANSYS in the Design of an MMD Sizing Station

The post Mesh Creation for Large Fabricated Structure Analysis appeared first on ANSYS Blog.

Fused Deposition Modeling (FDM) is increasingly being used to make functional plastic parts in the aerospace industry and this trend is expected to continue and grow in other industries as well. All functional parts have an expected performance that they must sustain during their lifetime. Ensuring this performance is attained is crucial for aerospace components, but important in all applications. Finite Element Analysis (FEA) is an important predictor of part performance in a wide range of industries, but this is not straightforward for the simulation of FDM parts due to difficulties in accurately representing the material behavior in a constitutive model. In part 1 of this article, I list some of the challenges in the development of constitutive models for FDM parts. In part 2, I will discuss possible approaches to addressing these challenges while developing constitutive models that offer some value to the analyst.

It helps to first take a look at the fundamental multi-scale structure of an FDM part. A 2002 paper by Li et. al. details the multi-scale structure of an FDM part as it is built up from individually deposited filaments all the way to a three-dimensional part as shown in the image below.

Multiscale structure of an FDM part (Li et.al., 2002)

This multi-scale structure, and the deposition process inherent to FDM, make for 4 challenges that need to be accounted for in any constitutive modeling effort.

• Anisotropy: The first challenge is clear from the above image – FDM parts have different structure depending on which direction you look at the part from. Their layered structure is more akin to composites than traditional plastics from injection molding. For ULTEM-9085, which is one of the high temperature polymers available from Stratasys, the datasheets clearly show a difference in properties depending on the orientation the part was built in, as seen in the table below with some select mechanical properties.

Stratasys ULTEM 9085 datasheet material properties showing anisotropy

• Toolpath Definition: In addition to the variation in material properties that arise from the layered approach in the FDM process, there is significant variation possible withina layer in terms of how toolpaths are defined: this is essentially the layout of how the filament is deposited. Specifically, there are at least 4 parameters in a layer as shown in the image below (filament width, raster to raster air gap, perimeter to raster air gap and the raster angle). I compiled data from two sources (Stratasys’ data sheet and a 2011 paper by Bagsik et al that show how for ULTEM 9085, the Ultimate Tensile Strength varies as a function of not just build orientation, but also as a function of the parameter settings – the yellow bars show the best condition the authors were able to achieve against the orange and gray bars that represent the default settings in the tool.  The blue bar represents the value reported for injection molded ULTEM 9085.

Ultimate Tensile Strength of FDM ULTEM 9085 for three different build orientations, compared to injection molded value (84 MPa) for two different data sources, and two different process parameter settings from the same source. On the right are shown the different orientations and process parameters varied.

• Layer Thickness: Most FDM tools offer a range of layer thicknesses, typical values ranging from 0.005″ to 0.013″. It is well known that thicker layers have greater strength than thinner ones. Thinner layers are generally used when finer feature detail or smoother surfaces are prioritized over out-of-plane strength of the part. In fact, Stratasys’s values above are specified for the default 0.010″ thickness layer only.
• Defects: Like all manufacturing processes, improper material and machine performance and setup and other conditions may lead to process defects, but those are not ones that constitutive models typically account for. Additionally and somewhat unique to 3D printing technologies, interactions of build sheet and support structures can also influence properties, though there is little understanding of how significant these are. There are additional defects that arise from purely geometric limitations of the FDM process, and may influence properties of parts, particularly relating to crack initiation and propagation. These were classified by Huang in a 2014 Ph.D. thesis as surface and internal defects.
  • Surface defects include the staircase error shown below, but can also come from curve-approximation errors in the originating STL file.
  • Internal defects include voids just inside the perimeter (at the contour-raster intersection) as well as within rasters. Voids around the perimeter occur either due to normal raster curvature or are attributable to raster discontinuities.

FDM Defects: Staircase error (top), Internal defects (bottom) (Bin, 2014)

Thus, any constitutive model for FDM that is to accurately predict a part’s response needs to account for its anisotropy, be informed by the specifics of the process parameters that were involved in creating the part and ensure that geometric non-idealities are comprehended or shown to be insignificant. In part 2 of this post, I will describe a few ways these challenges can be addressed, along with the pros and cons of each approach.

Editors Note:  Special thanks to PADT, Inc, an ANSYS Channel Partner, for submitting this blog. This blog was first published at http://www.padtinc.com/blog/additive-mfg/constitutive-modeling-of-3d-printed-fdm-parts

The post Constitutive Modeling of 3D Printed FDM Parts: Part 1 (Challenges) appeared first on ANSYS Blog.

In part 1 of this two-part post, I reviewed the challenges in the constitutive modeling of 3D printed parts using the Fused Deposition Modeling (FDM) process. In this second part, I discuss some of the approaches that may be used to enable analyses of FDM parts even in presence of these challenges. I present them below in increasing order of the detail captured by the model.

• Conservative Value: The simplest method is to represent the material with an isotropic material model using the most conservative value of the 3 directions specified in the material datasheet, such as the one from Stratasys shown below for ULTEM-9085 showing the lower of the two modulii selected. The conservative value can be selected based on the desired risk assessment (e.g. lower modulus if maximum deflection is the key concern). This simplification brings with it a few problems:
  • The material property reported is only good for the specific build parameters, stacking and layer thickness used in the creation of the samples used to collect the data
  • This gives no insight into build orientation or processing conditions that can be improved and as such has limited value to an anlayst seeking to use simulation to improve part design and performance
  • Finally, in terms of failure prediction, the conservative value approach disregards inter-layer effects and defects described inthe previous blog post and is not recommended to be used for this reason

ULTEM-9085 datasheet from Stratasys – selecting the conservative value is the easiest way to enable preliminary analysis

• Orthotropic Properties: A significant improvement from an isotropic assumption is to develop a constitutive model with orthotropic properties, which has properties defined in all three directions. Solid mechanicians will recognize the equation below as the compliance matrix representation of the Hooke’s Law for an orthortropic material, with the strain matrix on the left equal to the compliance matrix by the stress matrix on the right. The large compliance matrix in the middle is composed of three elastic modulii (E), Poisson’s ratios (v) and shear modulii (G) that need to be determined experimentally.

  Hooke’s Law for Orthotropic Materials (Compliance Form)

Good agreement between numerical and experimental results can be achieved using orthotropic properties when the structures being modeled are simple rectangular structures with uniaxial loading states. In addition to require extensive testing to collect this data set (as shown in this 2007 Master’s thesis), this approach does have a few limitations. Like the isotropic assumption, it is only valid for the specific set of build parameters that were used to manufacture the test samples from which the data was initially obtained. Additionally, since the model has no explicit sense of layers and inter-layer effects, it is unlikely to perform well at stresses leading up to failure, especially for complex loading conditions.  This was shown in a 2010 paper using ANSYS that demonstrated these limitations in the analysis of a bracket that itself was built in three different orientations. The authors concluded however that there was good agreement at low loads and deflections for all build directions, and that the margin of error as load increased varied across the three build orientations.

An FDM bracket modeled with Orthotropic properties compared to experimentally observed results (Celik et. Al, 2010)

• Laminar Composite Theory: The FDM process results in structures that are very similar to laminar composites, with a stack of plies consisting of individual fibers/filaments laid down next to each other. The only difference is the absence of a matrix binder – in the FDM process, the filaments fuse with neighboring filaments to form a meso-structure. As shown in this 2014 project report, a laminar approach allows one to model different ply raster angles that are not possible with the orthotropic approach. This is exciting because it could expand insight into optimizing raster angles for optimum performance of a part, and in theory reduce the experimental datasets needed to develop models. At this time however, there is very limited data validating predicted values against experiments. ANSYS and other software that have been designed for composite modeling (see image below from ANSYS Composite PrepPost) can be used as starting points to explore this space.

Schematic of a laminate build-up as analyzed in ANSYS Composite PrepPost

• Hybrid Tool-path Composite Representation: One of the limitations of the above approach is that it does not model any of the detailswithin the layer. As we saw in part 1 of this post, each layer is composed of tool-paths that leave behind voids and curvature errors that could be significant in simulation, particularly in failure modeling. Perhaps the most promising approach to modeling FDM parts is to explicitly link tool-path information in the build software to the analysis software. Coupling this with existing composite simulation is another potential idea that would help reduce computational expense. This is an idea I have captured below in the schematic that shows one possible way this could be done, using ANSYS Composite PrepPost as an example platform.

Potential approach to blending toolpath information with composite analysis software

Discussion: At the present moment, the orthotropic approach is perhaps the most appropriate method for modeling parts since it is allows some level of build orientation optimization, as well as for meaningful design comparisons and comparison to bulk properties one may expect from alternative technologies such as injection molding. However, as the application of FDM in end-use parts increases, the demands on simulation are also likely to increase, one of which will involve representing these materials more accurately than with orthotropic, continuum properties.

Editors Note:  Special thanks to PADT, Inc, an ANSYS Channel Partner, for submitting this blog. This blog was first published at http://www.padtinc.com/blog/additive-mfg/constitutive-modeling-of-3d-printed-fdm-parts

The post Constitutive Modeling of 3D Printed FDM Parts: Part 2 (Approaches) appeared first on ANSYS Blog.

Rapid growth in the use of engineering simulation tools – and in the demand for high performance computing (HPC) – is driving interest in cloud computing. Using the cloud for simulation presents unique challenges with different solution types required for specific use-cases. For many years, I have been on this journey with customers adopting cloud computing. Quite a few of them has been enabled through the UberCloud project. Let me share some lessons learned and key takeaways. I will basically do that by means of eight “best practices”:

1. Don’t Move the Data (More Than You Have to)
2. Remote Graphics & Graphics User Interface for End-to-End Simulation
3. Secure Network Communications & Data Storage
4. Effective End-User Access for Job & Data Management
5. Re-Use On-Premise Licenses (or Not)
6. Consider a Mix of Business Models
7. Match Your Cloud to Your HPC Workload
8. Start Small, Grow Organically… but Think Big

This post will cover the first four cloud computing best practices for engineering simulation.

1 –  Don’t Move the Data (More Than You Have To)

The first best practice relates to data storage – and the idea of minimizing the transfer of data back and forth between the cloud backend and the end-user. Clearly, some data motion will be needed: the end-user may be doing CAD on the desktop and will need to move that CAD file up to the simulation center on the cloud. Luckily, these input files are relatively small – measured in MBs, and typically less than a minute to transfer. Simulation result files, on the other hand, are typically huge – gigabytes or even terabytes of data – and will take hours to days to download.

So the best practice is not to download the data – make sure that the cloud is both a compute and storage solution, at least for WIP data. That means you need data security on the cloud (I’ll come back to that) and you need backup/disaster recovery for that data.

2 – Remote Graphics & Graphics User Interface for End-to-End Simulation

The second best practice follows from the idea of leaving your data on the cloud.  End-users will need to perform full end-to-end simulation on the cloud, meaning not just batch solves but also interactive GUI processes and graphical post-processing.  Most simulation workloads involve 3D graphics, so you will need a remote graphics software tool with server-side acceleration and good performance over the network – and reasonable network latency. And you’ll want full remote desktop access to that server (not just the application in a window) so that you can edit and manage files, maybe compile add-in routines, etc.

All this implies a graphics server on the cloud, which needs sufficient memory to load and display large simulation models. One of the issues I have seen is that not all cloud back-ends support this, and this figure shows a solution architecture that ANSYS has been demonstrating in conjunction with partners Nice and AWS – using a high-memory server instance to run the application, with Nice DCV handling graphics via external rendering on a lower-memory graphics server. Through the Enterprise Cloud solution, customers have experienced that this can be a robust and economical way to enable large model graphics on the cloud, without large investment in multiple high-memory graphics servers.

3 – Secure Network Communications & Data Storage

This best practice probably relates to the biggest concern that most companies have when thinking about an external cloud solution for simulation.  Simulation models contain the ‘crown jewels’ – product data – and the customer needs to be convinced that this data is secure and IP is protected.

For the sake of simplicity, data security really comes down to two ideas:

• Encrypting of data in motion depends on a secure network protocol – and I see two gold standards here:
  • The first is to establish a site-to-site VPN. This is a significant effort to put in place (in terms of IT resource) but provides scale to support many users and lots of data.
  • The second approach is to perform all data transactions within a web user interface over HTTPS, which is more pragmatic if you are enabling just a small group of end-users or using the cloud intermittently.
• Securing data at rest should similarly be accomplished via encryption – ensure nobody can read it if compromised. This can be done on the file system level or application level. Doing it at the file system level is the easiest though but depends on having the right file system tools available.

I see our cloud-hosting partners setting up dedicated storage accessible only by an individual customer account, so that the stored data is secured by physically isolating it. Some of them provide ITAR compliance, and AWS has GovCloud with controlled access. A key concept related to security – that I am personally quite keen on – is that of “divided responsibility” (AWS calls it “shared responsibility”):

• the cloud provider needs to provide physical (building) and internal network security,
• the customer needs to ensure the OS is patched for security. The customer needs to ensure that they are using applications that are secure, good access controls (who has access to what, license compliance, etc.), with secure network protocols.
• the Independent Software Vendor (ISV) needs to ensure that the software applications are secure – esp. cloud portal software needs to enable encrypted data transfer and data storage.

The ANSYS Enterprise Cloud is designed as a direct extension of a company’s enterprise IT infrastructure and resources while leveraging the public cloud platform of Amazon Web Services (AWS). Designed and implemented as a Single Tenant Cloud (STC), I can say that it is essentially a virtual data center on the cloud that addresses one of the major market concerns with cloud computing (i.e., data security and IP protection).

4 – Effective End-User Access for Job & Data Management

This best practice relates to a key concern I see when our customers are thinking about cloud: will end-users lose productivity? End-users will need simple, intuitive job submission procedures, tuned to the requirements of the specific applications they are using. They’ll need a way to monitor jobs in progress. They will need ways to move, find, and retrieve data in a centralized, secure environment.

I see our cloud-hosting partners creating web user interfaces that enable these capabilities. And ANSYS has built its own web-based interface, ANSYS Cloud Gateway^TM, that manages the end-to-end simulation process. It provides a secure environment for model and results visualization, data storage and management, HPC job orchestration, and remote session management. It is also customizable to incorporate other non-ANSYS commercial software as well as in-house developed codes.

Stay tuned for the other 4 best practices which I will share with you next week!

The post Cloud Computing Best Practices for Engineering Simulation appeared first on ANSYS.

In the first part of this two-part post, I already addressed four of the eight cloud computing best practices that are fundamentally related to simulation data and end-user access. Now I’ll address best practices that are associated with licensing, HPC workloads, and business support for cloud deployments.

5 – Re-Use On-Premise Licenses (or Not)

Right behind data security, the next most common question and issue I see relates to software licensing on the cloud. I believe that the ability to use licenses that already exist on-premise is key. This gives customers the ability to migrate their infrastructure to the cloud without having to decide how to apportion their license assets between on-premise and cloud. With the right network setup (either a site-to-site VPN or a point-to-point firewall configuration), the licenses that exist on-premise can be used interchangeably between the cloud and on-premise.

That said, and as the headline of this paragraph implies, there is an alternative – which is to just move licenses to a cloud-based license server. And/or buy more licenses and house them on the cloud license server. In many ways this is simpler, and probably a good way to get started, but just doesn’t give the flexibility you get if licensing is served between the customer and the cloud.

6 – Consider a Mix of Business Models

This best practice is a bit of a cautionary tale: careful what you ask for. I find that most customers moving to the cloud immediately ask for a pay-as-you-go model. In practice, pay-as-you-go models (either for software or hardware) may sound great, but produce the risk of exceeding the simulation budget fairly quickly. After all, it’s difficult to know for certain how many computing resources (again in terms of software and hardware) end-users will need for their simulation projects. So budget predictability is key, and I think that it should be part of a mix of business models as another best-practice on the cloud.

On the software side, it’s pretty evident that the traditional lease or paid-up model is most cost attractive for addressing compute capacity needs for average workloads. When customers are facing burst capacity needs for short-term projects (scheduled well in advance), short-term leases remain likely most suited. And, customers may clearly be most interested in some kind of usage-based licensing model to best meet their fluctuating/peak workload needs.

On the hardware side, customers are having similar needs: I hear more and more that customers want to mix their on-premise hardware with cloud computing resources. Among other drivers, I often say that cloud allows customers to use the compute capacity what they need, when they need it, and pay for what they use.

With the recent addition of our new Elastic Licensing, I believe that we will be in sync with what customers want on both software and hardware side: support of a mix of business/licensing models addressing their variety of needs, from average, to burst to fluctuating workloads.

7 – Match Your Cloud to Your HPC Workload

Cloud for simulation is fundamentally about getting access to computing power. But not all workloads will require the same cloud backend. If you’re after extreme HPC scaling, you may need to go to a national lab where 30,000 cores can be allocated to a single job. If you’re trying to get throughput for 100 or 1000 simultaneous jobs – to explore a design space – you’ll need a cloud backend that supports that kind of job submission (for example, a job submission tool that supports using ANSYS Workbench and our RSM).  It might be that GPU acceleration will help your application – or not. It might be that your job performance is limited by I/O and a parallel file system is key – or not.  So this is an obvious point, but customers may consider benchmarking their workloads on the cloud backend to be sure they are getting the right match of technology to application.

I always encourage our cloud partners to run and publish our standard benchmarks, and we’re currently working with them to test and certify ANSYS workloads. For our ANSYS Enterprise Cloud solution, we have tested and certified a complete simulation environment (“our reference architecture”) including ANSYS applications, batch and interactive use, HPC management, license managers, etc.

8 – Start Small, Grow Organically… but Think Big

Although I hear companies (particularly their CIOs) expressing ambitions to move 100% to the cloud, it is not uncommon to look for a significant project that is not well-served by in-house infrastructure and consider tackling that project in the cloud. I think this is particularly true when companies have already significantly invested in on-premise computing resources. Because of the transformation nature, I also believe that cloud adoption is best driven by a senior executive. In order to get the executive buy-in, making the journey to cloud will have to deliver the benefits they’re seeking such as improved operational agility, greater IT flexibility, and accelerated innovation. Finally, without IT team involvement and a clear plan to get there, companies run the risk of a failed cloud adoption.

My experience has been that the more successful cloud projects result from a step-by-step approach to their adoption. I believe that most companies are likely to reach a hybrid cloud model in the long run, and that the mix across private, managed and public cloud sourcing will evolve over time. That is why I recommend starting with a holistic view covering all the different options. Through our cloud core team, we help customers gain clarity on cloud solutions, identify the various cloud options that can work for their engineering simulation activities, and subsequently draw actionable steps.

While the focus of my post has so far been on customers migrating existing workloads to the cloud, it’s worth noting that technology startups have a unique opportunity to be “born in the cloud” for simulation; eliminating migration challenges and allowing for lighter computing devices on premise.

Cloud – Not “One Size Fits All”

From the above, I hope you can also agree with me that cloud is not a “one size fits all” offering. Multiple solutions are needed to address different customer needs. On the one hand, I notice customers working on or interested in different HPC & cloud infrastructure. From desktop or workstation-based computing to so-called private or enterprise HPC based, to hosted cloud and public cloud. On the other hand, customers clearly have different requirements and practices for their engineering simulation – from HPC scale up and software utilization optimization to job scheduling, need for more collaboration across geographically distributed teams, and more mobility requirements.

Therefore, I am strong believer of the ANSYS Open Cloud Strategy^TM, that is:

• Open to multiple cloud platform and vendors, from an ecosystem of ANSYS partners. To learn more, watch the webinar series on cloud-hosting partner solutions.
• Open to multiple cloud solution types, from cloud hosting services, internal private clouds to virtual private cloud on the public cloud. To learn more, watch the webcast HPC from Desktop to Cloud.
• Open to multiple business models on the cloud, from traditional lease/paid-up to pay-per-use licensing. To learn more, watch the webcast Pay-per-Use Licensing for Cloud Computing.

The post Cloud Computing Best Practices for Engineering Simulation Pt.2 appeared first on ANSYS.

ANSYS AIM brings easy simulation to every engineer. The results from these simulations can be used to create fantastic images that bring your simulation to life.

You may have noticed a new graphics display mode that can be enabled by clicking on one of the toolbar buttons in ANSYS AIM 17.0. Its name is Enhanced display, and it is the third display mode option after Standard and Translucent displays:

It may be tucked away behind an unassuming little button, but this is a powerful feature that can make your model come to life. Just click the button, and a car transmission model display goes from this:

to this:

Note: hide model edges for best results with enhanced display.

And, if you happen to have materials assigned in your physics setup, AIM will use its built-in material appearance information to create an even more realistic display:

Best of all, you do not need to compromise on usability or the speed of interaction for all this added visual richness. The heavy lifting is done by the GPU, leaving most of your computer resources available for other computation. You can continue working as in the Standard display, including fast view navigation, selection, or results analysis. You just get a better looking model display:

What if you want to further customize the appearance of your model? For example, you may wish to add some paint on the metal components, or specify material for the parts of the model that are not included in the physics simulation.

Let’s say you are modeling fluid flow around a sports car. You create a box to model the fluid around the car, and specify air as the fluid material. The car itself is not part of the simulation, and has no material defined on it. When viewing the Physics task in Enhanced display mode, you will see something like this:

While it is correctly representing all specified materials (only air in this case), it is not particularly exciting to look at.

Fortunately, you can continue specifying materials for parts of the car itself, without impacting the fluids simulation. This is because these material assignments will simply be ignored by the solution.

Here I specified Structural Steel for the wheels and a few other parts, and Hard Rubber for the tires and the exhaust. I also hid the fluid volume.

Getting there! Now, I would really like to paint the car red. However, there is no red material in the database. Coming in ANSYS AIM 17.1, there is a way to do what I want.

The procedure consists of two simple steps. First, assign any material to the body of interest. Here I assigned Structural Steel:

Then, open up the assigned Structural Steel and scroll down to Appearance Properties:

Here I can change any appearance property of my material, including color, opacity, whether it is metallic or plastic, and how rough or shiny in should look. Now I have my blazing red sports car look:

If I keep doing this for all parts of the model, I can get virtually any look by assigning and then fine tuning the materials.

I can also customize the appearance of the materials for bodies that are included in the physics regions, without impacting the simulation results. In my previous example, all transmission components were made of structural steel – I just customized their “finish” to make certain components stand out. In my car example, I can use this trick to make the air look less translucent, and then show the bottom face of the fluid volume to get the appearance of the floor:

Possibilities are endless! Get creative and have fun. With just a few simple steps, you too can make your simulation results more impactful and exciting.

Want to learn more about ANSYS AIM? You can visit our website here or by registering for an upcoming webinar.

ANSYS AIM: Democratize Simulation for Your Engineers

May 24, 2016

9:00 AM – 10:00 AM (EDT)

4:00 PM – 5:00 PM (EDT)

The post Bring Your Easy Simulations to Life in ANSYS AIM appeared first on ANSYS.

In 2013, I wrote a blog showing ANSYS users how to make MATLAB apps for ANSYS Fluent. Just as a quick reminder, a friend of mine, who is also an ANSYS Fluent and Mechanical APDL user has a Windows Matlab code programming a Linux Fluent session. She had just updated her hardware. Everything is moved to Linux. She also needed to integrate a Mechanical APDL session.

She was asking me: “Why, can’t I port my MATLAB®  code running on the platform of my choice and be able to also connect to Mechanical APDL?” She challenged me to to create a less than 20 lines code example. Back in 2013, my example was for ANSYS 16.0. Here is my update for ANSYS 17.0.

Software:

1. Fluent
2. Mechanical APDL
3. Workbench
4. MATLAB

Hardware:

1. Network of computers with ANSYS or MathWorks software installed

Instructions:

1. Download ANSYS aaS Matlab toolbox (supports Matlab R2014b to 2016a) from ANSYS Customer Portal
   • ANSYS as a Server Example: MATLAB Setup (MatlabR2014b-2016a)
2. Open the folder where the mltbx has been downloaded in a Matlab folder view.
   • double click on it and follow instructions to install
3. Start the ANSYS products in aaS mode
   • Read the downloaded pdf for suggestions
4. Collect the aaS keys (aas_FluentId.txt, aaSMapdlId.txt and aaS_WbId.txt)  and transfer them to the MATLAB machine
   • These are the “keys” required to connect with ANSYS aaS products.
5. Now let’s code. What should it do? Well, I will keep it simple: one command per ANSYS product:
   • ask a report from Fluent
   • set a variable in Mechanical APDL
   • retrieve the Workbench Schematic

%initialize aaS
orb=initialize_orb();
load_ansys_aas();

%connect to ANSYS products
iCoFluentUnit=actfluentserver(orb,‘aaS_FluentId.txt’);
iCoMapdlUnit=actmapdlserver(orb,‘aaS_MapdlId.txt’);
actwbserver(‘aaS_WbId.txt’)

%execute a Fluent TUI command
iFluentTuiInterpreter=iCoFluentUnit.getSchemeControllerInstance();
fluentResult=iFluentTuiInterpreter.doMenuCommandToString(‘report summary’)

%execute a Mechanical APDL command
mapdlResult=char(iCoMapdlUnit.executeCommandToString(‘aas_param=22’))

%retrieve the content of the remote Workbench Schematic
execwbcommand(‘systems=GetAllSystems()’)
querywbvariable(‘systems’)

Well, all in all I used only 19 lines of code. My friend added her own code to create her elegant and efficient so-simulation.

Thank you for reading.

The post MATLAB Apps For ANSYS Products: Updated for ANSYS 17.0 appeared first on ANSYS.

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 2013, I wrote a blog showing ANSYS users how to make MATLAB apps for ANSYS Fluent. Just as a quick reminder, a friend of mine, who is also an ANSYS Fluent and Mechanical APDL user has a Windows Matlab code programming a Linux Fluent session. She had just updated her hardware. Everything is moved to Linux. She also needed to integrate a Mechanical APDL session.

She was asking me: “Why, can’t I port my MATLAB®  code running on the platform of my choice and be able to also connect to Mechanical APDL?” She challenged me to to create a less than 20 lines code example. Back in 2013, my example was for ANSYS 16.0. Here is my update for ANSYS 17.0.

Software:

1. Fluent
2. Mechanical APDL
3. Workbench
4. MATLAB

Hardware:

1. Network of computers with ANSYS or MathWorks software installed

Instructions:

1. Download ANSYS aaS Matlab toolbox (supports Matlab R2014b to 2016a) from ANSYS Customer Portal
   • ANSYS as a Server Example: MATLAB Setup (MatlabR2014b-2016a)
2. Open the folder where the mltbx has been downloaded in a Matlab folder view.
   • double click on it and follow instructions to install
3. Start the ANSYS products in aaS mode
   • Read the downloaded pdf for suggestions
4. Collect the aaS keys (aas_FluentId.txt, aaSMapdlId.txt and aaS_WbId.txt)  and transfer them to the MATLAB machine
   • These are the “keys” required to connect with ANSYS aaS products.
5. Now let’s code. What should it do? Well, I will keep it simple: one command per ANSYS product:
   • ask a report from Fluent
   • set a variable in Mechanical APDL
   • retrieve the Workbench Schematic

%initialize aaS
orb=initialize_orb();
load_ansys_aas();

%connect to ANSYS products
iCoFluentUnit=actfluentserver(orb,‘aaS_FluentId.txt’);
iCoMapdlUnit=actmapdlserver(orb,‘aaS_MapdlId.txt’);
actwbserver(‘aaS_WbId.txt’)

%execute a Fluent TUI command
iFluentTuiInterpreter=iCoFluentUnit.getSchemeControllerInstance();
fluentResult=iFluentTuiInterpreter.doMenuCommandToString(‘report summary’)

%execute a Mechanical APDL command
mapdlResult=char(iCoMapdlUnit.executeCommandToString(‘aas_param=22’))

%retrieve the content of the remote Workbench Schematic
execwbcommand(‘systems=GetAllSystems()’)
querywbvariable(‘systems’)

Well, all in all I used only 19 lines of code. My friend added her own code to create her elegant and efficient so-simulation.

Thank you for reading.

The post MATLAB Apps For ANSYS Products: Updated for ANSYS 17.0 appeared first on ANSYS.