The cloud Generator allows both internal and external clouds to be created. The user simply uses the Curve tool to draw an open profile for the cloud and then selects either an Internal or External Cloud.
The options dialogue provides control over the cloud arc width and height for both internal and external independently.
This simple Solid Edge add-on application allows users in the mining, oil and gas and infrastructure industries to benefit from Solid Edge’s superior drawing productivity.
EDGE plm software is a privately owned Australian provider of software solutions aimed at the Engineering and Manufacturing sectors. EDGE has been providing engineering design centric solutions since 2004 with over 500 customers across Australia and New Zealand. Typical solutions from EDGE would include the provision of software, maintenance, support, consulting and training services.
The EDGE software portfolio includes CAD, CAM, FEA & PDM solutions and EDGE fully supports and offers training and mentoring services on its entire portfolio. EDGE has been a business partner of UGS/Siemens since 2004. EDGE also configures and sells Dell hardware to assist our customers maximise their software investments. Read more about us…
EDGE plm understands the importance and training to the successful adoption of our products. However no two companies are the same and their training requirements often require a different or tailored approach which is why we have developed our flexible approach to training and mentoring.
We offer scheduled classroom-style training, bespoke training to suit customer requirements as well as one to one mentoring for any of our customers around Australia and New Zealand. Our Solid Edge training courses are created with the aim to get participants up to speed with current industry software quickly and effectively, giving you and your company the competitive edge.
Our experienced and qualified instructors run a range of training courses designed to suit your exact requirements, whether this consists of scheduled classroom training at our offices, customised courses delivered at your site, or online sessions.
Please call us on 1300 883 653 or send us an email [email protected] for our latest training schedule or to enquire about specialised training and mentoring services.
This course is the follow on from the initial foundation course. It covers a foundation review, providing an opportunity to revisit and answer any questions from the initial course. It covers Drafting in [...]
This course is the follow on from the initial foundation course. It covers a foundation review, providing an opportunity to revisit and answer any questions from the initial course. It covers Drafting in far [...]
The course focuses on sheet metal design tools, from the creation of simple sheet metal folded parts to the adding of deformation features and the subsequent creation of flat pattern blanks and 2D drawings. [...]
Delegates attending this course must have completed the foundation course or have been using Solid Edge for a minimum of 3 months. This course offers an introduction to the concepts of surface modelling, particularly [...]
This course is designed for users that wish to improve their overall Assembly knowledge and students will be given instruction on how to make full use of the advanced assembly modelling functions for both [...]
The course aims to improve the productivity of users when designing with Solid Edge. It includes a knowledge assessment test and sessions aimed at the correct approach to advanced modelling techniques for parts and [...]
Talk to us to find more details and the next available course. This course designed to improve the productivity of users when designing with Femap. It includes a knowledge assessment test and sessions aimed at [...]
“The European automotive industry invests more than €50 billion in R&D annually, a large percentage of which goes towards fuel-efficiency technologies, to meet EU CO2 emission reduction targets thereby complying with NOx and soot standards. However, very few are likely to be able to change the makeup of their fleets fast enough to meet the immediate challenge of the 2021 EU CO₂ emission reduction targets and avoid the significant fines for missing them.” Source: The CO₂ emissions challenge: some carmakers are running late in the race to 2021 - PA Consulting reportCO₂ emission reduction over time against 2017 actual data and 2021 targets
Consequently, to achieve future regulations OEMs and suppliers must innovate in their conventional powertrain design and at the same time come up with competitive alternative propulsion systems as soon as possible. Nevertheless, innovation in conventional powertrains in many instances implies an increase of technology complexity while alternative technologies imply an immediate need of removing uncertainty through rapid system development. Either way, ideally, what OEMs and suppliers would need is to equip themselves with the best engineering tools to accelerate the transformation. That’s a challenge we at Siemens PLM Software accept: we provide a set of simulation solutions to make virtual design and evaluation of new innovative real.
One of the specific innovation areas where OEMs and suppliers can focus on is the thermal management of the internal engine combustion system. Optimizing thermal management of an aftertreatment systems is really a challenge. Indeed, for maximum efficiency (and so satisfying emission rate) aftertreatment systems such as catalysts require specific temperatures to operate efficiently. Using CFD simulation is a way to do a detailed thermal analysis and assess the best powertrain architecture.Engine thermal management analysis using Simcenter Star-CCM+
In the on-demand webinar “Optimizing thermal management in modern powertrains using CFD simulation”, Carlo Locci – simulation powertrain application specialist – showcases how using our CFD simulation tool Simcenter STAR-CCM+ can support the thermal management modeling of your engine, and introduces:
An innovative technique to predict the thermal interaction between the fluid film and the wall in an SCR. This technique was developed to allow for long transient runs in a Selective Catalytic Reduction (SCR),
The most recent developments in this field of Conjugate Heat Transfer (CHT) related to Powertrain problems,
Perspectives on heat production modeling for fuel cells.
If you are eager to know more about our whole Simcenter portfolio for powertrain applications, Warren Seeley Simcenter Director of Powertrain gives an overview in the introduction of this online presence.
Just one numerical simulation contains a wealth of information – we can gain a lot of insight on how a device performs, and from that, we can infer how to make that device better. To confidently recommend one design over another, though, we’ll need to run more than one simulation. As our device knowledge is informed through simulation, we can expect to make numerous geometry/part modifications to the original design. How quickly we can turn these changes around will determine how many simulations we can run within our time budget. Without a highly efficient and flexible workflow, we might find ourselves in the position of being less certain of our final product recommendation. Risky. Now, you’ll be hearing a lot soon about Design Manager, a native capability within STAR-CCM+ v12.04® to do design exploration – that’s not this story. Instead, I want to share how just two mouse clicks can now get you quickly from that first simulation to the next one, and to the one after that and the one after that...
First, some history. In STAR-CCM+ v9.04, we introduced logic based “Filters”. For example, you could create a Filter to return all the geometry parts that contain the name “chip”. Using your Filter to make your part selection in an Operation saves you the trouble of having to find and select all of these objects in the simulation tree manually. Faster. Less error-prone. Repeatable. Good.
But, if you were to then add another “chip” geometry part, you had to go back to your Operation, re-apply your Filter and update your selection. In other words, the part selection wasn’t dynamic. To address this, we delivered "Query-Based Selection" in STAR-CCM+ v10.06. Automatable. Better. But still limited in coverage to just Operations, Displayers and Derived parts. Why is this limiting? Because Regions were statically linked to parts, so if you added, modified or removed parts, you would need to update your part selection for your region manually.
This is now a thing of the past. In STAR-CCM+ v12.04 , we’ve extended Query-Based Selection to apply to Regions, Boundaries, Sub-Groups, Interfaces and Reports. Faster. Less error-prone. Repeatable. Automatable. Better still.
To show how this can help you, let’s consider the simulation of a packed bed reactor for dry reformation of methane to produce hydrogen gas. These reactors contain randomly packed solid catalyst particles which can be various shapes and sizes:
Examples of catalyst particles used in packed bed reactors.
Our operating conditions may be fixed to a narrow range, so if we want to improve our reactor performance, the choice of particle size, shape and number is going to be critical. Let’s consider our workflow starting point to be a simulation (with the solution cleared) in which the physics continua (fluid and solid), regions, boundaries, interfaces, reports, scenes and displayers have already been set up. Lets say we want to replace an existing packed bed containing cylindrical shaped particles with seven wedge shaped holes in each (above at far left), with a new packed bed containing smaller tri-lobe shaped particles (above at far right). We’ve got four Query-Based Selections in play that we will use to assign…
…any geometry part with a name containing “__particle” to a Unite operation (this was possible in previous versions).
…the Geometry Part generated by the Unite Operation to the solid particle Region.
…all Part Surfaces containing the name “__particle” to a Region Boundary defined in the fluid region and another defined in the solid Region (the same dynamic query is used for both regions).
… all Part Surface Contacts (created when the Volume Extract Operation is run) to Interfaces.
Use four Query-Based Selections to automate your two mouse click workflow.
Now, with your .sim file set up this way, when you hit the Generate Volume Mesh button on the toolbar, our first of our two mouse clicks, the Mesh Operations pipeline is executed. What you end up with is a .sim file, meshed and ready to go – all Parts to Region assignments are automatically done. The second of our two mouse clicks, hitting the Run button, is almost anticlimactic in comparison. Your simulation starts running and any derived parts, reports and scene displayers that also use Query-Based Selection get automatically updated.
A cylinder derived part (intersecting the packed bed near the reactor wall) is unrolled to compare hydrogen gas production rates between the two packed bed designs.
Data Focus highlights areas of higher (in color) compared to lower (grey) catalyst site blockage.
To get the workflow down to two clicks did take some preparation and the methodology does rely on a part naming convention. When does it make sense to go through the extra steps? If we want to examine just 3 different particle sizes for each particle shape pictured above, that’s 21 different random packed bed geometries; 21 .sim files that can be consistently set up and run; 21 sets of reports and plots and scenes that can be consistently compared in an automated fashion. And, if that isn’t enough of a reason, there are two great new features in STAR-CCM+ v12.04, Replace Assemblies and 3D-CAD Part Synchronization, that also leverage the benefits of Query-Based Selection. The bottom line is this: Some initial preparation to set up your simulation template is the logic based choice.
Throughout the computer-aided engineering (CAE) design process, engineers must balance a variety of critical performance aspects to validate whether the product under development will work as intended. This complex task cannot be based on a test-and-repair approach. Such an approach would lead to expensive iterations on physical hardware. Other unique projects require that the first prototype is the final product. Testing these kinds of products under extreme boundary conditions can have dramatic consequences.
As a result, Siemens PLM Software solutions provides engineers with the necessary tools to conduct upfront analysis for a variety of applications during the design process. To be successful, machine manufacturers must use models to reproduce the complex behavior within the operational environment. Engineers require pinpoint accuracy to understand how structures work and expedite the analysis of new designs for potential modifications that optimize performance.
The right solution for any nonlinear application
Computation of accurate dynamic loads in structural analysis often requires the consideration of nonlinear behaviors. Simcenter Samcef nonlinear motion analysis fully exploits the augmented Lagrangian method and the large-displacement-large-rotation approach to deliver this capability. The software features an extended library of flexible kinematic joints that can be included in FEA. By coupling these joints to super elements and beams, the complete kinematics and dynamics of the system can be simulated.
When combined with Simcenter Samcef nonlinear structural analysis, nonlinear and fully meshed components can be included to capture material and geometrical nonlinear structural behavior. Furthermore, Simcenter Samcef can be used to integrate sensors, actuators, and controllers in the simulation. These can be imported from Matlab®/ Simulink® and Simcenter Amesim™ software or preprogrammed in Simcenter Samcef. In that case, the control parameters can be optimized. Simcenter Samcef can also be coupled to Matlab and Simcenter Amesim for co-simulation. This co-simulation capability is done through a dedicated module that enables coupling between different transient solvers. This mechanism is used to connect Simcenter Samcef to the AMRC tool (a research center linked to the University of Sheffield) that provides the cutting forces of the machines.
Twin-Control is a European project (H2020, grant agreement nº 680725) aimed to develop new concepts for machine tools and machining process performance simulation. It is coordinated by IK4-TEKNIKER in Spain, with additional partners Renault, COMAU, MASA, Gepro Systems, ModuleWorks, Artis, Predict, TU Darmstadt, University of Sheffield and Samtech, a Siemens Company, in Liège, Belgium.
In the Twin-Control project, Siemens focuses on the dynamic modeling of machine tools, including its Computer Numeric Control (CNC), and its interaction with the machining process. To properly simulate modern high-speed tools, which show close interactions between the dynamic behavior of the mechanical structure, drives, and the CNC, it is crucial to build models that represent the flexibility of all components and interactions.
Simcenter Samcef Mecano enables accurate modeling of machines by considering FEA modeled components connected by a set of flexible kinematic joints. Models are implemented to deal with drive-trains and motor dynamics. To fully capture the dynamic behavior of the machine tool, force interactions between the cutting tool and the workpiece are also considered in the models. These forces consider the dynamics of the tooltip, combined with the tool work-piece engagement determined by Module Works CAD/CAM for toolpath generation and simulation.
As seen in figure 1, a model of the CNC is connected to the machine model by specialized elements that compute motor forces from controller inputs, calling a dynamic library embedding the Matlab Simulink model of this controller.
Figure 1: Coupling scheme
To properly model the machine tools when operating, the following objectives are followed:
Properly account for flexibility of all structural components, connections and feed drive to obtain a model that can represent interior vibrations. The guiding system is modeled by flexible slider elements, which constrain a node to move along a deformable trajectory represented by beam elements.
Limit the number of degrees of freedom (DOF) as much as possible to use the model in the time domain (small time step imposed by the machining simulation module and the controller model). This is done by using a super-element technique. The model contains super-element techniques and can represent the desired levels of vibrations.
The proposed technology is applied to build a flexible multibody mechatronic model of a box-in-box fast machine of project partner COMAU, as seen in Figure 2. This approach provides comprehensive simulation capabilities for virtual machine prototyping in working conditions.
Figure 2: A Multibody model of the COMAU machine tool. Courtesy of Comau.
Another example that illustrates this technology is the 5 axes machine from project partner Gepro Systems (shown in Figure 3).
Figure 3: A Multibody model of a five axes machine tool with multiple spindles. Courtesy Gepro Systems
An industrial CNC controls the motors of the axes to follow the desired trajectories with minimal error. In the model, all frames are fully flexible, as the rails and screw drivetrains, which are represented by a set of slider elements. The control loops are modeled in MATLAB/Simulink, translated into a dynamic library associated with specific control elements to manage the coupling between 1D models and flexible 3D models.
The resulting Twin-Control simulation package is dedicated to both machine tool builders for design activities and machine tool users looking to improve their processes. In both cases, this virtual model will avoid performing costly physical. Simcenter Samcef, coupled with the different modules from our partners, allows building this virtual model in the form of a fully flexible and nonlinear finite element based digital twin.
We can all be blinded by the obvious. The number ofDilbert cartoonson the topic is great evidence for how often it happens to all of us.
This has been on my mind lately because of a recent experience. About a year ago, my family finally had our kitchen renovated. When we first saw the house before buying it many years ago, I distinctly remember walking in and saying “well, we will need to renovate the kitchen.” But then time slips by and priorities shift. Soon the kitchen that so clearly needed renovating just became our kitchen. Our brains so quickly and easily plaster over the imperfections around us that those imperfections disappear from our perception.
On the last day in our old kitchen before renovations started, we took a picture of everyone crammed into the one corner that always seemed to be where everything in the kitchen was located. I found that picture the other day and was struck by what I saw. Was it really that small, that dingy? I found myself slightly embarrassed that we had happily hosted guests for so many years with a kitchen that looked like that!
This ability to tune things out that continually bombard us is often rather useful. Just think, that ability allowed me to happily live with a kitchen that desperately needed an update for many years. Imagine how draining it would be to wake up every day and have all the imperfections be as obvious as the day we first toured the home. However, there is also danger in not stopping to reevaluate. It’s possible to go on so long without reevaluation that our perception becomes entirely detached from reality.
As simulation engineers, we are especially at risk in this regard. One of the most important aspects of what we do is to determine what is important,what should be included into a model being developed and what can be neglected. Even worse, we must balance the amount of personal and computational effort required to capture a certain piece of physics. We may deem it important, but not so important that we are willing to invest in modeling the phenomenon.
One perfect example is the process that goes into designing and modeling a gas turbine such as those used for powering aircraft or generating electricity. These are massive machines that start with tens-of-rows of compressor blades working to create massive amounts of high-pressure air. That air is then mixed with fuel and ignited, producing gasses at even higher pressures and temperatures. All that work is done so that the high-pressure and temperature gasses can rotate turbine blades to extract mechanical energy. The gasses driving those turbine blades are so hot that cold air is pumped through complex internal passageways of the blades and out over their surface just to keep them from being damaged.
To simulate a system this complex, the level of physics appropriate for a model depends on how far along the design process we are. For example, when coming up with the right shape for those turbine blades so that they extract the most energy possible, those complex internal passages are usually not included. Conversely, when determining how to most efficiently cool those blades, it is necessary to include that complex internal detail. However, it’s not always so easy to decide what can safely be neglected.
Simcenter STAR-CCM+ is particularly strong for modeling complex cases. Modeling conjugate heat transfer, complex geometry, combustion chemistry and unsteady blade-passing effects are some of the common types of analysis done by our gas turbine simulation users. Astreamlined workflow gives unmatched ability to accurately mesh the most complex geometry features while enabling the simulation of complex physics such as combustion, conjugate heat transfer and unsteady flows.
Multi-timescale simulation capabilities are now available in Simcenter STAR-CCM+, making it a good time to stop and re-evaluate the tradeoffs being made in our gas turbine simulations. Mixing plane interfaces allow us to model just a single blade passage in each row, which keeps the computational cost down. However, these heavily cooled blades produce distinct cold wakes that wash over the next row of blades downstream.
Ignoring the impact of these localized, unsteady wakes on blade temperature prediction is common. Until now, many have decided that capturing that effect would require too high a computational cost and so mixing planes have become the standard. At one point, the decision was made to ignore blade-passing effects and deal with the decreased accuracy of the simulation. Now it is an assumption made so often that most are blind to it, not recognizing that there are other options available.
Simcenter STAR-CCM+ has been a pioneer in developing harmonic balance simulation capabilities for gas turbine engine simulation for many years. The harmonic balance method allows the unsteady blade-passing effects in the fluid to be modeled at a much lower computational cost than traditional time-domain unsteady simulation. The method takes advantage of the periodic nature of the unsteadiness in the fluid to formulate a much more efficient simulation method.
With Simcenter STAR-CCM+ 2019.1, it is now possible to use the harmonic balance solver on the fluid side to capture the unsteady blade-passing effects and the steady solver on the solid side, all within the same simulation. This decoupling of the fluid and solid timescales makes efficient use of computational resources while more accurately representing the physical system. With this time-scale decoupling, it is no longer necessary to assume that the flow-field is steady andto neglect the impact of localized wakes when performing conjugate heat transfer simulations.
Simcenter STAR-CCM+ will continue to push the boundaries of what is possible with simulation, tackling the most complex cases, and timescale decoupling is evidence of that progress.
In addition to taking on the most complex gas turbine simulation needs, a new initiative has begun for gas turbine simulation with Simcenter STAR-CCM+ that is focused on improving gas turbine simulation for all levels of complexity. Each phase of the design and simulation process have unique challenges. Early in the cycle, flow and thermal predictions must be extremely fast and reliable and provide automatic reporting on the performance of a candidate blade. Later additional geometric and physics complexities are added, and more blade-rows of the machine are simulated simultaneously. Late in the cycle, very large simulations are performed once the design is nearing maturity. Many new capabilities are being brought into Simcenter STAR-CCM+ to help address the unique challenges of gas turbine simulation at each of these design phases. Interaction with design tools, specialized meshing and gas turbine specific post-processing are all on the way. Additionally, with unrivaled abilities to simulate the complex, it will become much easier to mature a given model with additional details as a design progresses.
It’s an exciting time for gas turbine simulation. With so many new capabilities, it may be time to reevaluate assumptions and look for blind spots.
“Using Solid Edge with synchronous technology I can actually do many more iterations now that I wasn’t able to do before. And because of that, the cost of the product comes down. The weight of the product comes down. The performance goes up. The warranty is a lot longer. Quality loves it. We love it. The profit margin loves it.”
John Winter , Mechanical Engineering Manager, Bird Technologies
“Siemens’ synchronous solver overcomes the order dependencies that have plagued history-based CAD programs by solving for the explicit and inferred constraints at the same time. The synchronous solver doesn’t use a history tree, but rather holds user-defined constraints in groups associated with the surfaces to which they apply…Ultimately, though, I believe this to be a transformative technology – one that represents an important inflection point in the CAD industry. If you hear someone say ‘that’s nothing new,’ don’t believe them. Synchronous technology is a big deal.”
Evan Yares, CAD Industry Analyst
“Synchronous technology breaks through the architectural barrier inherent in a history-based modeling system,” “Depending on model complexity and how far back in the history that edit occurs, users will see dramatic performance gains. A 100 times speed improvement could be a conservative estimate.”