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Leslie Langnau

CFD simulation software puts flow analysis into the design process early

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Siemens Digital Industries Software announced the latest version of Simcenter FLOEFD software, a CAD-embedded computational fluid dynamics (CFD) tool. Simcenter FLOEFD software helps users frontload CFD simulation early into the design process to understand the behavior of their concepts. It can reduce the overall simulation time by as much as 75% and it runs seamlessly inside NX software, Solid Edge software, CATIA V5 and Creo. The latest version includes new functions that let designers take advantage of a seamless working environment, as well as enhancements that extend thermal simulation capabilities and lighting applications.

Simcenter FLOEFD includes improvements within process integration, allowing design engineers to implement CFD solutions within their workflow without requiring process changes. Simcenter FLOEFD for NX projects and results can be managed in Teamcenter software. Integrations with HyperLynx software, a suite of analysis and verification software for PCB engineers, allow for enhanced thermal analysis and more accurate simulation of printed circuit boards (PCBs) by taking into account joule heating phenomena.

The latest version of Simcenter FLOEFD also includes expanded capabilities based on Simcenter MAGNET software technology to increase accuracy of thermal simulation by considering electromagnetic phenomena. Direct integration of structural analysis allows CAD-centric users to apply CFD results to perform linear structural stress analysis of complex PCBs accurately. A new interface to Simcenter Nastran software allows easy transfer of CFD results for structural analysis in Simcenter 3D software. In addition, new enhancements for lighting applications include pulse-width modulation of a light source and the simulation of scattering and photoluminescence of phosphor particles, which are used during the manufacture of LEDs.

Siemens Digital Industries Software
www.sw.siemens.com

nTopology 3.0 lets designers preview changes in real time

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nTopology introduces its third update to its software program. One of the new features is an opt-in GPU acceleration, which will deliver a 10x to 100x performance boost that makes it easier to preview design changes in real-time and regenerate parts with complex geometry in seconds.

nTopology 3.0 also consolidates the recent technology improvements introduced to the software. These include functional latticing workflows, topology optimization tools, engineering simulation utilities, and advanced design automation capabilities.

The core of nTopology is the implicit modeling engine. This engine describes every solid body by a single mathematical equation, which enables designers to generate complex part geometry reliably and error-free.

Any CPU can evaluate this implicit equation in milliseconds. Visualization can take a bit longer and can be viewed as a nuisance to design efforts. Through patent-pending technology, nTopology can use both the CPU and GPU of a designer’s system for faster performance. The reduction in the time it takes to develop designs enables latticing, texturing, filleting, shelling, and other field-driven operations to be previewed in real-time. Complex designs are rebuilt in a few seconds.

According to nTopology managers, you can save 10 to 60 seconds every time you change a design parameter. Thus, making 100 such changes a day can save an average of 60 minutes of time per day.

In addition, there are other enhancements to the software.

Lattice generation
Lattices let designers change a structure’s behavior by controlling its geometry at the mesoscale level. Thus, designers can alter material response for specific applications. nTopology’s 3rd generation latticing pipeline offers tools to navigate the mesoscale world.


This generation includes a complete set of ordered, stochastic, and TPMS unit cells to control lattice properties at every point in space using simulation results, test data, engineering formulas, and a field-driven design approach.

New utilities help designers create custom unit cells and build the basic process blocks that feed generative design workflows.

Topology optimization
Topology optimization is a core tool of many generative design workflows due to its effect on lightweighting and structural optimization. nTopology 3.0 uses state-of-the-art algorithms and unique constraints for Additive Manufacturing.

Other features
–nTopology 3.0 expands the capabilities of nTopCL and strengthens the integrations with its Multidisciplinary Design Optimization (MDO) software.

–nTopCL enables users to call generative nTop workflows in Python or Matlab scripts. It can run on a desktop, a private server, or the cloud.

–nTopology offers three ways to simulate while using the software. Simulation results can be an input to drive part geometry, or used to perform design analysis, or users can export meshes for verification in external solvers.

–Built-in simulation tools let users run static, modal, and buckling structural analyses, steady-state, transient, and non-linear thermal simulations, and lattice unit cell homogenization.

nTopology is also expanding its simulation pre-processing capabilities. These include new meshing utilities, import and export capabilities for FEA and CFD data in the native format of any solver including ANSYS, Abaqus, or Nastran to streamline the design verification process.

nTopology
nTopology.com

IronCAD unveils product update for IRONCAD 2021

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IronCAD announced the release of the first update for IronCAD’s newest 2021 edition. The IRONCAD 2021 Product Update #1 (PU1) features enhancements and capabilities that enable IronCAD users to accelerate productivity in the design process in both 3D and 2D.

Created with a focus on design productivity and usability, IronCAD 2021 PU1 has improvements in many key functional items. CAD software users will benefit in areas including Sheet Metal, Point Cloud Modelling, IronCAD Detail Drawing, and General 3D Modeling with IronCAD’s drag & drop push-pull capabilities and patented TriBall positioning and creation tool.

Among the many new enhancements, key functional items addressed for IronCAD 2021 Product Update #1 are:

Sheet Metal Enhancements: These enhancements support more capabilities in sheet metal design and even design in Corrugate Packaging by supporting single line Cut and Cross-Break Design with the Sketch Bend tool. Users can create complex combinations of bends, line cuts and cross-breaks in a single tool simplifying the process and steps required to create their products.

Point Cloud Enhancements: Improvements in the Point Cloud capabilities have been added to allow users to work more effectively with scanned data. Import multiple scanned data sets into a single working environment. Position imported data sets to the correct locations. Reduce data sets on import to control the amount of data required. Use new tools to deleted points and to hide/show point clouds to enhance the workflow when working and creating geometry from the point cloud data.

IronCAD Drawing Enhancements: Many usability improvements have been added in the detail drawing environment including the Double-Click to Quickly Edit Properties for More Annotations which speeds up the annotation process. Another key improvement is the Bill of Material improvement for Top-Level Assemblies to Expand on View Based Selections allowing for more control to create specific tables and callouts for products.

Further improvements within Product Update #1 also address enhancements to general items including, Pin Favorite Stocks in the Pop-up Stock Table on Drop from the Catalog, Project Edges from Import 2D Reference Curves in 3D Into Other Sketches, Improve/Support Larger Preview Images in Windows Explorer for IronCAD Files, Add New Option in the Right-Click Menu of the TriBall to Turn off the TriBall for quick access, as well as improvements to IronCAD’s KeyShot Integration and support for Rhino 5.0.

IronCAD
www.ironcad.com

NVIDIA launches Omniverse design collaboration and simulation platform

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NVIDIA announced the coming general availability of NVIDIA Omniverse Enterprise, a technology platform that enables global 3D design teams working across multiple software suites to collaborate in real time in a shared virtual space.

NVIDIA Omniverse Enterprise makes it possible for 3D production teams, which are often geographically dispersed, to work seamlessly together on complex projects. Rather than requiring in-person meetings or exchanging and iterating on massive files, designers, artists and reviewers can work simultaneously in a virtual world from anywhere, on any device.

NVIDIA Omniverse Enterprise has been in early evaluations with design teams at companies like BMW Group, Foster + Partners, and WPP. It follows the launch three months ago of an open beta for individuals.

Said Jensen Huang, founder and CEO of NVIDIA, “Building on NVIDIA’s entire body of work, Omniverse lets us create and simulate shared virtual 3D worlds that obey the laws of physics. The immediate applications of Omniverse include connecting design teams for remote collaboration to simulating digital twins of factories and robots.”

Omniverse Enterprise includes the NVIDIA Omniverse Nucleus server, which manages the database shared among clients, and NVIDIA Omniverse Connectors, which are plug-ins to industry-leading design applications.

It also includes two end-user applications: NVIDIA Omniverse Create , which accelerates scene composition and allows users in real time to interactively assemble, light, simulate, and render scenes, and NVIDIA Omniverse View, which powers seamless collaborative design and visualization of architectural and engineering projects with photorealistic rendering. NVIDIA RTX Virtual Workstation (vWS) software, also part of the platform, gives collaborators the freedom to run their graphics- intensive 3D applications from anywhere.

Omniverse Enterprise is tested and optimized for professionals to run on NVIDIA RTX laptops and desktops, and NVIDIA-Certified Systems on the NVIDIA EGX platform. This makes it possible to deploy the tool across organizations of any scale, from small workgroups using local desktops and laptops, to globally distributed teams accessing the data center using various devices.

NVIDIA Omniverse Enterprise software is available on a subscription basis and includes NVIDIA’s Enterprise support services. NVIDIA’s partner network of leading computer makers — including ASUS, BOXX Technologies, Cisco, Dell Technologies, HP, Lenovo and Supermicro — are supporting NVIDIA Omniverse Enterprise.

NVIDIA
www.nvidia.com

Spatial computing supports large spaces

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PTC announced the newest addition to its Vuforia augmented reality (AR) enterprise platform, the Vuforia Engine Area Targets offering. This program supports the creation of immersive augmented reality (AR) experiences for spaces up to 300,000 square feet. Through the use of Area Targets, industrial organizations can create AR interfaces within their facilities to enable employees to better engage with machinery and understand how the environment is being used.

PTC’s Vuforia Engine Area Targets leverages spatial computing capabilities to support large spaces equivalent to six American football fields

With support from Matterport and Leica 3D scanners, along with NavVis’s indoor mobile mapping systems, Area Targets users can generate photorealistic, survey-grade digital twins, empowering them to create digital canvases of spaces, such as factories, malls, or offices for spatial computing applications.

As one of the leading emerging technologies, spatial computing powers digital twin renderings to support the activities of machines and people, as well as the environments in which they operate. When deployed across the industrial enterprise, spatial computing enables seamless interactions between employees through AR, enabling companies to close the loop on performance management, improve machine learning capabilities with spatial analytics, and optimize design and factory floor operations.

PTC
www.ptc.com

Maple Flow provides a flexible mathematics tool for engineering projects

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Maplesoft announced Maple Flow, a mathematics tool that allows engineers to more easily brainstorm, develop, and document their mathematics and analyses. Maple Flow provides a virtual, whiteboard-style environment that automatically keeps calculations live as users refine, reposition, and develop their calculations. Maplesoft also announced a new release of the math software Maple.

Maple Flow provides an interface and workflow specifically tailored to design engineers doing calculations and quick scratchpad math, while Maple is a more general-purpose tool that supports the advanced mathematical analysis and algorithm development work done by research engineers.

By providing a flexible, whiteboard-style environment, Maple Flow allows design engineers to easily sketch out and formalize technical ideas, revising and reordering content with simple drag-and-drop behavior. Users can add math, text, and images to a live, interactive document, and Maple Flow keeps all of the mathematics automatically updated. The Maple Flow environment handles the design calculations of virtually all engineering projects, such as circuit analysis, beam loading, highway pavement design, and combustion.

The new version of Maple offers a range of enhancements across the entire product, from small productivity changes to new areas of mathematics. Improvements include a stronger math engine that can tackle even more problems, a streamlined workflow, and expanded tools for signal processing, working with thermophysical data, and physics.

Maplesoft
www.maplesoft.com/products/MapleFlow

Avoiding singularities in FEA boundary conditions

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FEA boundaries can usually be obtained using simple fixed constraints. But, this can result in singularities that produce erroneous results. To determine whether results show a real stress concentration or a singularity, an accurate solution can usually be obtained by either using elastic supports or modeling contact between components.

Singularities caused by sudden changes in boundary conditions can be harder to spot and resolve. In fact, setting up realistic boundary conditions is often the most challenging aspect of a simulation.

Dr. Jody Muelaner, PhD CEng MIMechE

Singularities in Finite Element Analysis (FEA) can cause real issues, even for an apparently simple structural analysis. Singularities lead to completely erroneous results and stresses that continue to rise as a mesh is refined. Many singularities are caused by stress-raising geometry such as holes and sharp internal corners, and this is generally well understood. Singularities caused by sudden changes in boundary conditions can be harder to spot and resolve. In fact, setting up realistic boundary conditions is often the most challenging aspect of a simulation.

What is a singularity?
A singularity is a point in the model where a value, such as stress, tends to infinity. As the mesh is refined, the increasingly small elements get closer to this point and the value therefore rises. As the element size tends to zero, the stress will tend to infinity. This produces nonsensical results and prevents mesh convergence.

Geometry that causes singularities
Singularities caused by stress-raising geometry such as holes and sharp internal corners are well understood. In the real world, there is likely to be a small radius on any internal corner, meaning the stress would not actually continue to rise. In any case, local yielding will limit the stress in such features. The location of these singularities can often be readily identified, excluded from convergence results and localized models used to predict the true stress in the features responsible. Singularities at corners are similar to cracks and the stress intensity factor can be calculated using the J-Integral, or considering the strain energy release rate – the energy dissipated during fracture.

There is, however, another type of stress raiser in FEA models that is talked about less often and which can be more difficult to deal with. Where there are abrupt changes in boundary conditions, such as a split line where a fixed constraint ends, this can also result in stress that continues to rise unrealistically and causes mesh convergence to fail. Let’s explore why this happens and how it can be avoided.

How can boundary conditions cause singularities?
The most obvious way that a boundary condition can cause a singularity is when a force is applied to a single node. Since stress is force divided by area, applying a force at a single point will give an infinite stress. If the area where the load is applied is not of interest, then it can be acceptable to use such a boundary condition. Due to Saint-Venant’s-principle, which states that, if the distance from the load is large enough, two different but statically equivalent loads create essentially the same effect. This can be easily seen where the same total force is applied to the sponges in two different ways. The fingers represent point loads and the flat hands distributed loads. Although the effects close to the applied loads are different, in the center of the stack, at a sufficient distance from the loads, the effect is virtually the same.

Saint-Venant’s principle states that, if the distance from the load is large enough, two different but statically equivalent loads create essentially the same effect. This image illustrates the point. The fingers represent point loads and the flat hands distributed loads. Although the effects close to the applied loads are different, in the center of the stack, at a sufficient distance from the loads, the effect is virtually the same.

When loads are simplified to point or edge loads, it is simply important to understand that the very high stress around the applied force does not represent reality. These regions must not be included in results, mesh convergence or adaptive meshing. Next, we’ll look at some more involved examples of boundary conditions casing singularities.

Abrupt changes to a fixed boundary condition
It is often convenient to fix a face of a model, to constrain a component that is loaded with forces applied in some other area. It should first be noted that such a fixed constraint can never truly represent reality. A fixed boundary condition essentially means that the face is bonded to an infinitely stiff body. In the real world, all solid bodies have some flexibility and often a part will actually be clamped rather than bonded. However, if the peak stresses are not expected in the region being represented by a fixed boundary, this may seem like a reasonable approximation. As with point loads, it can, therefore, be a good idea to simply exclude the stresses in this region from any mesh convergence. However, not all software allows this, and it is particularly problematic if automatic mesh refinement, or adaptive meshing, is being used.

A shaft can provide a good example of these issues. The shaft illustrated below has been cut in half and a symmetry fixture applied. The smaller cylindrical face at the right-hand end of the shaft has been split into three separate faces to allow a vertical force to be applied to a defined region. The other end of the shaft would be held by two bearings, with the outer bearing constraining axial movement against a shoulder and the end face.

The shaft has been cut in half and a symmetry fixture applied. The smaller cylindrical face at the right-hand end of the shaft has been split into three separate faces to allow a vertical force to be applied to a defined region. The other end of the shaft would be held by two bearings, with the outer bearing constraining axial movement against a shoulder and the end face.

 

When standard inelastic fixtures are used, stress singularities occur where the fixtures end. This effect is equivalent to the edge of a stiff part digging into a soft part. This can be seen below, where a bearing support extends between a split line and an external radius. It also occurs on the face of the shoulder which was constrained using a roller/slider constraint in SolidWorks Simulation. When the mesh is refined on the radius it is clear the singularity occurs where the fixtures end and is not a real stress concentration in the radius. This is particularly problematic because the radius is also a stress concentration and this region cannot, therefore, simply be excluded from the results or mesh refinement.

When standard inelastic fixtures are used, stress singularities occur where the fixtures end, as shown here where a bearing support extends between a split line and an external radius. This effect is equivalent to the edge of a stiff part digging into a soft part.

 

Using elastic supports
One solution is to use elastic supports rather than fixed constraints. In a fixed constraint, each node on the constrained surface is forced to zero displacement. An elastic support consists of an additional spring element for each node on the constrained surface. One end of the spring is attached to the node on the surface and the other end of the spring is fixed with zero displacement. The actual stresses in the springs are not normally included in the results. Using elastic supports can eliminate the issues with stress singularities at the edges of boundary conditions, but care must be taken to select realistic stiffnesses for the supports. If the stiffness is too small, the model may encounter excessive displacements which cause the solver to fail. On the other hand, if the stiffness is too great, a spurious stress concentration may still be seen at the edge of the constraint. Although an initial value for the support stiffness may be calculated by considering the type and thickness of the actual material which would provide the support. The image below shows that, with correctly set elastic supports, the model properly converges on the actual high-stress region.

With correctly set elastic supports, the model properly converges on the actual high stress region.

 

Modeling contact
Another similar approach is to model contact between the supporting components and the component of interest. This can be the most accurate, but can also be seen as simply pushing the problem to another area, since the supporting components must then be constrained in some way. However, if the supporting components can be excluded from the mesh convergence and final results, the supporting components can be simply constrained by fixing faces.

Mesh convergence and adaptive meshing
Mesh convergence is one of the most important methods to ensure a reliable FEA simulation. The basic process is simply to rerun the simulation a number of times, refining the mesh around areas of interest and recording the relevant values for the simulation using each mesh. When the value of interest varies randomly, and by a small amount, in both directions, the model can be said to have converged. If there are large differences in the result, or the result keeps creeping in the same direction as the mesh is refined, then this indicates a problem, often a singularity. What constitutes a small change is somewhat subjective but can generally be considered as a few percent of the value under consideration.

When the value of interest varies randomly, and by a small amount, in both directions, the model can be said to have converged. If there are large differences in the result, or the result keeps creeping in the same direction as the mesh is refined, then this indicates a problem, often a singularity.

 

Adaptive meshing takes mesh convergence a stage further, automatically refining the mesh at areas of interest and rerunning the simulation until the model is converged, or convergence fails according to some criteria. There are two types of adaptive meshing: H-Adaptive reduces the element size and P-Adaptive increases the element order. SolidWorks Simulation does not currently support P-Adaptive meshing for elastic supports.

Conclusions
In many cases, it may be possible to obtain useful results while using simple fixed constraints. However, this can result in singularities that may prevent mesh convergence and produce erroneous results. It is important to be aware of this issue, since some judgment may be required to determine whether results show a real stress concentration or a singularity arising due to simplified boundary conditions. When this happens, an accurate solution can usually be obtained by either using elastic supports or modeling contact between components.

A view on where AR/VR is headed, roundtable discussion from those who know

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Recently, Ron Fritz, CEO of Tech Soft 3D, hosted a roundtable discussion with five other industry executives to discuss the current state of augmented reality (AR) and virtual reality (VR). The core question at hand: whether AR/VR is finally poised for its breakthrough moment – and if so, what barriers might need to be removed to usher in this new era.

The participants included:

– Asif Rana, COO of Hexagon, a provider of sensor, software, and autonomous solutions

– Martin Herdina, CEO of Wikitude, an augmented reality technology company

– Susanna Holt, VP Forge Platform, Autodesk, a provider of 3D design and engineering software

– Thomas Schuler, CEO of Halocline, a developer of VR products for production planning and manufacturing

– Tony Fernandez, CEO of UEGroup, a user experience agency

A lightly edited and condensed version of the conversation and their unique perspectives follows.

Q: At various points over the past decade, many of us have believed that AR/VR was ready to really take off in the industrial setting – but it hasn’t happened yet. What are the barriers that are standing in the way of that widespread adoption, and what should the industry be focusing on?

Asif: One of the fundamental things that we tend to forget when we think about commercializing a technology is the user experience. I think one of the main hurdles of AR/VR in the commercial usage is we don’t think about the full user journey or what the full end-to-end solution looks like.

Martin: For a while, there was such a focus on technical benchmarks that nobody really talked about what could be achieved with AR/VR. Even when people did start to talk about what could be achieved, they didn’t really look at the full picture and at how things could be scaled beyond a single isolated use case. As long as that underlying basis is missing, widespread adoption of AR/VR will be hampered.

Susanna: I think one thing that’s lacking around AR/VR is pre-processing of data and data preparation – from CAD design data, to mesh poly count reduction. That kind of stuff needs to be automated, robust, fast, and scalable. And at the moment, all of that still seems to require too much manual work to really enable this AR/VR takeoff that we’ve been anticipating for the past 20 years.

Tony: I think the core issue is that AR/VR did not emerge from a human-centered point of view. It emerged from a technological exploration point of view. And what that has meant is that the human factors of this technology are terrible.

To take the case of VR: Who thought it was going to be a great idea to duct tape a TV to your head and blindfold you? Meanwhile, with AR, one of the problems that we continually run into is arm and body fatigue from having to hold up a device. Because AR/VR technology hasn’t centered around the reality of the human body, how it gets fatigued, and how people feel motivated to use their bodies, it will continue to have a difficult time breaking through to the mainstream, regardless of the value proposition it may offer.

Q: From what everyone’s saying, it seems that the user experience is one of the big barriers to mainstream adoption. What needs to be different for people to feel comfortable? How can companies remove this barrier?

Tony: I think mobile AR is a really difficult problem to solve. And again, part of the problem with most existing AR solutions is that they require people to use their bodies in unnatural ways. From a hardware perspective, we’re going to be much closer to solving that problem once we get to some sort of compact glasses. Of course, glasses come with their own problems around power and where to place the battery and so on. But I think that’s what AR’s waiting for, in terms of a hardware platform solution.

Asif: I wonder whether there are the same expectations on an enterprise level as at a consumer level for AR/VR. I say that because in the enterprise, you do see technology that’s not so comfortable to use – but it delivers such a high value that it’s used anyways. So, perhaps the AR/VR hardware is “good enough,” and it’s the content side that deserves more focus to deliver applications that can really make an impact and deliver value. Either way, I’d say that if the hardware companies focused on more business cases, that would be helpful to the enterprise sector.

Susanna: It’s true that the enterprise use case may put up with all sorts of inconveniences. But when I think of a use case for us at Autodesk, which might be an architect or structural engineer at a construction site or building site, inconvenience can quickly become a safety concern. AR provides a limited field of vision. In normal life, we don’t just look straight ahead – we’re constantly taking in things occurring on the periphery. Excluding that visual information in a potentially dangerous environment like a construction site does strike me as a risk factor. So, the hardware has to be natural to the way we conduct ourselves as humans in a particular environment.

Martin: I think the most important point that people have hit on is that things have to feel natural. When you wear a HoloLens, it’s cool, but it’s nothing that you would want to wear for 10 hours per day at your workspace. Another aspect that companies should address is the fact that so many AR use cases totally lack context. For example, why would you use AR to project a team roster on your desk when there are so many other user interfaces that make so much more sense for that objective? AR needs to really link reality to a reasonable set of content.

Q: Lots of big names – including Google, Apple, Facebook, and Microsoft, to name a few – are heavily investing in the belief that the barriers around AR/VR adoption are being resolved and that this an area that is ripe for explosion. All of your companies are, to varying degrees, investing in that belief as well. What makes you optimistic that AR/VR is getting close to a real breakthrough? What drives your confidence?

Thomas: It takes a long time to bring hardware technology from an early prototype to a usable product. You have to really keep at it for quite some time. What makes me optimistic is that the hardware vendors are still investing in it and pushing it forward – they’re not standing still.

At the same time, more and more content is now being produced that makes more sense. I think more people understand now that you need a different set of tools for AR or VR rather than taking the same old tools that you had before, but just manipulating them differently. So, while the progress might be slower than everyone expected, that progress is very much ongoing. That makes me optimistic that we are on an eventual path towards more widespread adoption for AR/VR.

Susanna: Well, let me turn this question the other way around. We’re hearing so much from our customers about how AR or VR is needed and how they’re expecting it to play a bigger role in their workflows. Some of that, of course, is a reflection of hype that they see in the media, but a significant proportion of it is a reflection of real need.

For example, while wearing a HoloLens headset might be uncomfortable today, it does allow you to make those important decisions much faster than having to look at something, take a photograph, go back to the office, think it through, discuss it, and so on. It will speed everything up. It’s about faster decisions, better decisions. There’s a real need in the market – so that bodes quite well for AR/VR, because a lot of technological advancement and evolution is driven by market need.

Tony: I would say AR/VR will break through if it can focus on its fundamental promise, which is to reveal information and perspectives in ways that would be difficult to do any other way. I’m not necessarily a believer that the way most companies have defined AR at this point is necessarily the path forward. For example, AR doesn’t necessarily always have to be visual in nature, right? It can be haptic in nature. It can be lots of other things. But visual is the primary road for now, and I think the need to visualize information that is otherwise difficult to do any other way or get access to any other way is going to drive the solution.

Martin: At my company, we perhaps have a unique perspective, because we have thousands of developers using our tools on a daily basis to create AR use cases, and we can see what those people are working on. The things they are doing today with AR are substantially different from what we saw two or three years ago. There are still people working on proof of concepts, but the number of people who are moving from POC to commercial grade installations – and the number of use cases we see that are no longer for two or three or five users, but 10,000 to 20,000 users – has rapidly increased in the past year.

Also, from a finance perspective, AR is no longer tapping into the budgets of the innovation units – it’s tapping into the budgets of the actual business units. That’s the ultimate sign that technologies like AR/VR are starting to take hold in the enterprise space.

Asif: There are at least three reasons why I’m very feeling positive about AR/VR. The first is the acceleration of digitalization that has taken place as a result of the COVID-19 pandemic. Many, many systems are getting digitally transformed, and digital journeys that might have taken years to complete are now on the fast track. So, the ground is really set for AR to make a move.

The second reason is that digital process management has really evolved. The journey really starts with connectivity first, then it goes to the integration, then it goes to the digital workflows. Once you have the workflow, to augment the workflow with AR is very straightforward.

The third reason is the advent and proliferation of smartphones and tablets that are loaded with the sensors and features that are required for AR/VR. These devices are now at everyone’s fingertips, ready to be used for various advanced workflows. So, really, I think the time is very, very good right now for AR/VR.

 

Workstation makes custom bike design easy

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While 2020 wasn’t without its challenges, we also saw innovation at its best as companies across industries incorporated emerging technologies and developed new workflows to ensure business continued to move forward amid all the chaos caused by the pandemic.

One such company is Predator Cycling, a manufacturer and designer of high-end custom carbon fiber bicycles for cycling enthusiasts and Olympic-level cyclists. Using the pandemic as a catalyst for change, Predator Cycling’s CEO and co-founder, Aram Goganian knew the latest technology gains could transform his Nashville-based business. The engineers and designers develop all of their frames and conduct all of their simulation, rendering and manufacturing processes in house. In addition, they also manufacture, build and simulate all of the machinery and equipment used to build custom bikes. They take any chance to optimize these processes throughout the design and production lifecycle.

The latest bike launch is the RF20 frame. After being in research and development stages for years, Goganian wasn’t sure the new road bike would ever see the light of day due to increasing costs of materials and the complexity of the design, which impacted bike manufacture and assembly. However, through performance and efficiency gains – including the ability to process more complex models, render, and run simulations in real time, and streamline manufacturing processes – Predator Cycling was finally able to introduce the new bike at a competitive price.

The design team uses the ThinkStation P620, equipped with the NVIDIA RTX A6000 GPU built on the new Ampere architecture. Because the team does so much custom work and each of their bikes are purpose built for each individual rider, customers need an easy way to see what their bike is going to look like and start to build an emotional connection with their bike. To do this, they use Luxion’s real-time ray tracing application Keyshot running on Lenovo and NVIDIA’s hardware to quickly load all of their existing CAD models and drag and drop all of their finishes, textures and surfaces onto the part to give the customer a realistic representation of what their bike will look like.

Seeing rendered prototypes not only provides customers with seamless options for component and finish requests that they can alter in real time, it also saves the company time and money. Prior to this, the Predator team would build physical prototypes for marketing purposes, which would take months to complete from start to finish.

They are now able to show customers renders of bikes long before they get to production or even physical prototyping. Thanks to the instant feedback they receive from customers, they can go from prototyping straight to testing. Goganian estimates that this new workflow has saved somewhere between 12-16 weeks in their go-to-market timelines.

“As I find myself deep into the design and simulation process of a component, I can be approached with an editing or simulation update task from the production line without my workflow being disrupted,” said Goganian. “I can now run concurrent simulations – from topology optimizers to thermal dynamics – all while hosting a video call with a client. As a result, I’ve saved more than 1-2 weeks in design-to-manufacturing turnaround using this workflow.”

The designers have seen performance gains of 2 to 6x across a number of the key applications they use including Luxion Keyshot, Ansys Discovery, Ansys Mechanical, Ansys CDF, and Autodesk Fusion 360. Plus, they can validate and test their designs more efficiently. For instance, the designers can implement mechanical simulations to help optimize carbon fiber orientations as well draping patterns to enhance the structural integrity and riding characteristics of their frames. They also use fluid simulation to identify any drag in order to design a frame system that will “cut through the wind.”

Moving forward, Goganian and team are exploring other ways they can take advantage of the power and flexibility of the 64-core enabled ThinkStation P620 workstation. From integrating digital twins into their design workflow and transforming their manufacturing process by using generative design and 3D printed parts to capturing and editing their own 6K video footage for marketing, Lenovo is helping Predator pave a smarter way forward.

Lenovo
www.lenovo.com/us/en

Developing a Parametric Model of a Bicycle and Human

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A general-purpose parametric SolidWorks model has been created to enable rapid evaluation of novel bicycle concepts. It can be used for ergonomics, checking clearances, aerodynamics, kinematic and degrees of freedom studies, and product visualization.

Dr. Jody Muelaner, PhD CEng MIMechE

For this study in ergonomics, a set of anthropometric models from 3D Human Model were used as the starting point. Key bicycle geometry was defined using reference geometry in individual part files, located using distance and angle mates. This geometry includes the contact points at the saddle, handlebar grips and pedals, as well as the steering axis and wheel positions. Anthropometric models were then configured to fit to these contact points, with a few additional angle and distance mates that allow further adjustment of the rider position. The included models represent 5th percentile females, 50th percentile males and 95th percentile males. The model is stored as an assembly template file, enabling it to be easily reused as a layout for different design concepts. It was created for the BriefBike project, which is developing a new class of folding bicycle that will effortlessly fold into a roller-case.

Key bicycle geometry
There are three ways that references can be parametrically defined within an assembly. Using a sketch or creating reference geometry (point, axis or plane) is often more straightforward and, in the case of a sketch, allows multiple parameters to be defined in a single model tree feature. However, parameters defined in this way cannot be animated or adjusted using the Mate Controller. The Mate Controller is particularly useful within this model as it allows parameter sets for different riding positions to be stored independently of configurations. It is, therefore, possible to apply different standard riding positions to any configurations that a are created. In order to provide this greater flexibility, parameters must be defined using distance and angle mates. This means the reference geometry is first defined with a part file, and the part is then mated in the assembly file.

The parametric bicycle geometry definitions are:

  • Planes parallel with the Right plane, defining the width of each pedal from the centerline (Right plane).
  • The crank length part three axes – the bottom bracket axle and the two pedal axles. The crank length is defined by different configurations of the part file. The part is mated on the right plane with the bottom bracket axis on the Front plane and at a distance from the Top plane, representing the bottom bracket height from the ground. This leaves the pedals free to rotate.
  • Pedal thickness parts contain a pedal axis and a plane representing the top surface of the pedal. They are mated to the pedal axes in the crank length
  • A Seat part contains planes representing the seat tube angle and the top surface of the saddle. The seat tube angle is mated coincident with the bottom bracket axis and at an angle from the top plane. The top surface is mated at a distance from the bottom bracket axis.
  • A Bar part contains an axis to represent the handlebar. It is mated at a vertical and horizontal distance from the bottom bracket axis.
  • The orientation the handlebar grips is defined in terms of two sequential Euler rotations – the backwards and downwards sweep. A separate part is used to define each rotation.
  • Grip_Sweep parts are mated to define the position and backwards sweep, with the following constraints:
    • Two translations by mating the origin coincident with the Bar axis
    • The remaining translation, the width of the grips, is defined by a distance mate between the part’s origin and the assembly Right plane.
    • Two rotations are constrained by mating the part’s Top plane parallel with Top in the assembly.
    • The backwards sweep is the only remaining degree of freedom. It is defined with an angle mate between the part’s Front plane and Front in the assembly
  • Grip parts are then mated relative to the Grip Sweep part, defining the downwards sweep.
    • All three translations are constrained by mating the origin coincident with the origin of Dum_Grip_Sweep
    • Two rotations are constrained by setting the Front plane parallel with Front in Dum_Grip_Sweep
    • The downwards sweep is the only remaining angle. It is defined with an angle mate between the Top plane and Top in Dum_Grip_Sweep
  • Wheels contain an axle axis and a ground plane set at the wheel radius. Different configurations are used for different wheel sizes. Certain configurations may also include basic solid geometry to visualize the tire. The wheels are mated with the ground plane on the assembly Top plane and at distances forwards and rearwards of the bottom bracket axis.
  • A Steering Axis part contains a plane to represent the steering axis angle and an axis to represent the line where this plane intersects with the ground. The axis is mated coincident with the Top plane of the assembly. A distance mate between this axis and the Front plane of the front wheel defines the steering trail. The steering axis angle is then set with an angle mate.

Care must be taken when using angle mates. The direction in which the angle is defined can flip when changes are made to the model, causing assembly rebuilds to fail or result in unexpected behavior. These issues can usually be avoided by defining a reference entity for each angle mate, which is not defined by default. This is normally just a one click operation, by selecting Auto Fill Reference Entity.

Kinematics of the Human Models
The human model has parts or sub-assemblies for hands, lower arms, upper arms, clavicles, head, neck, thorax, abdomen, pelvis, upper legs, lower legs and feet. These 19 rigid bodies have 114 degrees of freedom (DoF) without any joints or other constraints. Joints between the body parts are either spherical, removing the three DoF for translation, or revolute which also removes two rotations, constraining a total of five DoF.

When all of the joints are added to the body parts, the human model still has 43 DoF. Considering the kinematics of the model as a whole is, therefore, overly complicated. Luckily, it can be broken down into smaller kinematic chains that behave independently. For example, each leg forms a kinematic chain which also includes the crank, and each arm forms a kinematic chain between the shoulder joint and the handlebar grip.

An understanding of how a person should be positioned on a bike was provided by Mike Veal, who created the DIY Dynamic Bike Fitting guide.

  • The hip joints should align with the plane of the seat post angle.
  • The angle of the line between the hip and shoulder joints is typically between 45° and 55° from the horizontal. 45° to 50° is usual for a road bike and 50° to 55° is typical for a more relaxed upright position. Dutch bikes can be from 65° to 90°.
  • The angle of people’s feet relative to the floor is quite personal but a typical value is 15°.
  • The leg does not completely straighten at the bottom of the pedal stroke. Typically, the angle between the upper and lower leg does not exceeds 140°. Although some literature puts this angle closer to 150° this is due to static measurements with the foot parallel to the floor. When pedalling, the foot assumes a natural angle which reduces the extension of the leg.
  • Wrists allow three types of rotation and should ideally be in a neutral position for all of them:
    • Flexion/Extension can be fixed at the neutral position, with the flat plane of the hand aligned with the forearm axis. It can be adjusted without significantly changing the position of the arms by rotating the hands around the axis of the grip.
    • Deviation is sideways movement of the hand, towards the thumb is radial deviation and towards the little finger is ulnar deviation. The neutral position does not position a griped bar perpendicular to the axis of the forearm but rather that the third metacarpal bone is aligned with the forearm axis. One study found that a natural grip results in a mean angle of 65° between the grip axis and the third metacarpal, with the grip sweeping back as though the wrist was in 25° ulnar deviation. However, the standard deviation was 7°, due mostly to variation between individuals, suggesting significant adjustability may be desirable for this aspect of the grip position.
    • Supination/Pronation: Rotation about the forearm axis is known as supination when the thumb is rotating towards the back of the hand and pronation when it is rotating towards the palm.

Kinematic chain from pelvis to head and shoulders

This section of the body is made up of the pelvis, abdomen, thorax, clavicles, neck and head. The pelvis is fixed at the saddle, but is free to rotate so that it tilts forwards. The clavicles are mated parallel with the front and top planes of the thorax, effectively forming one ridged body with the shoulders in a neutral position. Although the components could be mated in series, starting with the tilt angle of the hips and then setting the angle between each part, this would make it hard to set an overall lean angle. A reference part is therefore introduced with a plane that defines the lean angle. This part is mated with an axis through the hip joints and with an angle mate relative to the assembly Top plane.

A symmetry mate is used to apportion half of the forward lean to the pelvis tilt. The Front plane of the pelvis is the plane of symmetry. The seatpost angle and the forward lean planes are symmetric about it. The abdomen is mated parallel with the dummy lean plane, and the shoulder joints are set to be coincident with the forward lean plane.

Kinematic chain from hip joint to bottom bracket

Each leg can be considered separately, as a kinematic chain consisting of the crank, pedal/foot, lower leg, and upper leg.

These four bodies have 24 DoF, which are reduced by revolute joints at the knee, pedal axis and crank axle, and spherical joints at the hips and ankles, to leave three DoF:

  • Crank position is intentionally unconstrained to allow pedaling motion.
  • Foot angle from the floor varies between individuals but the toes are typically pointed downwards by an angle of approximately 15 degrees.
  • The spherical joints at the hip and ankle also allow the leg to rotate about an axis between these joints, so that the knee moves in a circular motion. Assuming the leg does not lock out into a fully extended position, this can be constrained by making a point on the knee joint coincident with the plane defining the pedal width.

Kinematic chain from shoulder to handlebar grip

The kinematic chain for each arm consists of the upper arm, lower arm and hand. These three bodies have 18 DoF, reduced to just two DoF by spherical joints at the shoulder and wrist, and revolute joints at the elbow and the grip of the hand around the bar. The remaining two DoF can be considered as:

  • Rotation of the hand around the handlebar
  • Rotation of the whole arm so that the elbow moves in a circular path about the axis between the shoulder and wrist joints

Setting the wrist to neutral flexion removes one DoF. This can be achieved by mating the hand’s top plane parallel with the forearm axis. There are several possible ways to remove the remaining DoF. It was found that the most practical and stable is with a distance mate between a point on the elbow joint and the Right plane of the assembly.

Adjusting and configuring the model

The assembly template contains three different anthropometric models, all configured as described above. These models represent a 5th percentile female, 50th percentile male and 95th percentile male. They can be activated by simply suppressing or suppressing the associated folders in the feature tree.

Different pre-configured body positions are also included. These are defined using a Mate Controller for each percentile model.

Conclusions

The bicycle and human model template enables the rapid evaluation of novel design concepts. Applications include ergonomic studies, mechanical clearance checks, aerodynamic simulation and product visualization. The simple parametric definitions allow easy adjustment of all the relevant variables in the bicycle geometry and rider position.