Femap Case Study- Consumer Products

ASICS

CAE facilitates design direction and prototype reduction, cutting time for new product development.

Continuous commitment to CAE for a quarter of a century

ASICS Corporation, Institute of Sport Science (ASICS) is very popular in the sports footwear market. This includes both the general consumer and running shoe segments, where ASICS is among the market leaders.

The popularity of ASICS shoes is largely based on their superior functionality, which can be directly attributed to the company’s continuous efforts to improve shoe performance.

One of the major contributors to improved shoe performance has been the aggressive use of computer-aided engineering (CAE). At ASICS, serious commitment to simulation began around 1987.

For 25 years, ASICS professionals – both CAE specialists and other engineers/researchers – have actively utilized various analysis tools. Among these tools, the most significant contributor to performance enhancement has been the use of Femap™software with NX™ Nastran® software.

The CAE effort is led by Dr. Tsuyoshi Nishiwaki, Fellow and senior general manager of ASICS Institute of Sport Science.

Since joining ASICS, Dr. Nishiwaki has strived to implement and deploy the best CAE tools across the product development process.

Before devoting his time to shoe development processes, Dr. Nishiwaki worked on analyzing sporting goods, such as tennis rackets. Dr. Nishiwaki is not only experienced in the use of CAE software, he has personally developed a numerical model that represents the mechanical properties of composite materials.

CAE as a decision-making tool for development direction

Through experience, Dr. Nishiwaki has observed that CAE software is a great tool for decision-making across a number of areas, especially for development direction. He points out, for example, that because of the use of 3D computer-aided design (CAD) in conjunction with Femap with NX Nastran, ASICS has reduced and continues to reduce physical prototypes per project.

He also notes that reducing CO2 emissions is now becoming an important issue at the corporate level, as well as for virtually every manufacturer today. By using CAE early in the development cycle, the production of physical prototypes is significantly reduced, as are CO2 emissions.

“Using CAE effectively to decide product development direction, we estimate we are shortening turnaround by 30 to 35 percent for general development projects,” says Dr. Nishiwaki.

Many CAE users focus on how well analysis results match with experimental results. According to Dr. Nishiwaki, for purposes of shoe design, it is a rare case to have exactly the same results. To do so, all of the required information, such as material properties, constraints and loads must be completely known.

This is particularly true for products like sports shoes, which require factoring in certain physical conditions of the human body, although much of that information is highly variable or unknown.

For example, muscle flexibility/stiffness often varies as a person’s mental state changes. So even if just one of these characteristics is unknown, that often means that one cannot obtain a highly accurate result.

Nevertheless, Dr. Nishiwaki emphasizes how critical it is to use CAE tools to capture the impact of design on shoe performance, explaining that simulation can provide special insight on the design of a shoe’s sole, enabling important decision-making data regarding overall performance to be captured in the early phases of development.

According to Dr. Nishiwaki, it is just this type of use of Femap with NX Nastran that has enabled ASICS to reduce product development time by 30 to 35 percent.

Using subjective metrics to improve objective performance

When Dr. Nishiwaki began his work on sports footwear development, he soon tackled the issue of leveraging subjective metrics using CAE. “Although quite a few biomechanics professionals have been researching human body movement with respect to shoes, these findings weren’t leveraged to improve the shoe development process,” says Dr. Nishiwaki.

To quantify performance metrics that could only be evaluated in physical ways in the past, ASICS established eight domains of functionally: cushioning, stability, flexibility, fitting, durability, grip, weight and ventilation.

ASICS then established metrics within each domain through experiments and hypotheses. For example, to establish metrics for cushioning properties, ASICS hypothesized that such properties are tightly linked to the acceleration of the shin.

Through a series of experiments, the company found that when the sole absorbs low frequency waves, the shoes have better shock-absorbing characteristics. Moreover, with that knowledge in hand, ASICS set about to determine specific cushioning metrics using CAE.

“The design process for shoes is the same, whether it’s for a professional athlete or a casual jogger,” says Dr. Nishiwaki, pointing out, “Actually, shoes for the casual jogger have more performance features.”

According to Dr. Nishiwaki, shoes have two major roles. One is to improve the performance of the user; the other is to protect the user from injury.

For the professional athlete, the second role, protection, is secondary. While protection is meritorious, a professional athlete’s No. 1 priority is to improve performance.

However, for the casual athlete or young person first trying sports, protection from injury is very important.

Moreover, sports shoes for general consumers must serve highly diverse needs, so they must perform well for most individuals.ze in simulation.

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High analysis functionality + extraordinary ease-of-use = efficient product development

Dr. Nishiwaki notes that the use of CAE tools by all research and development (R&D) professionals – not just analytical specialists – is essential for efficient product development. However, he adds that ease-of-use is critical to engaging the software’s use by engineers and it is here that Femap with NX Nastran truly stands out, because it’s a powerful analysis tool that’s easy to navigate and satisfies the requirements of a broad spectrum of users.

“Femap with NX Nastran is very easy to use, even if an engineer has little to no experience using numerical analysis tools,” says Dr. Nishiwaki. “After one or two days of training, an engineer can start utilizing CAE on his own. Even a complete novice, an engineer with absolutely no CAE experience, can easily start numerical analysis by importing geometry from 3D CAD software, creating mesh using auto-mesh functionality, then analyzing the optimal depth of grooves in a sole and positioning of gels for shock absorption.”

Dr. Nishiwaki points out that such ease-ofuse is commonly associated with low functionality in analysis software, but that’s simply not the case with Femap with NX Nastran. “There are no issues in convergence; enhancement of the code for nonlinear analysis is exactly as we wished. The solution even has good enough functionality for appropriate use by numerical analysis experts, and we expect the software will get even better for such applications with subsequent releases. We are quite satisfied with the well-balanced functionality and usability of the software.

Femap with NX Nastran has helped us create a more efficient product development process. It is up to us to determine how much we make further use of the software for competitive advantage.” Indeed, ASICS is extending the use of Femap with NX Nastran. It is now deploying the software to its product designers. ASICS excels in terms of shoe performance; however, Dr. Nishiwaki notes that outstanding design is equally essential, particularly for running shoes for the general consumer. “No matter how superior a pair of shoes may be in terms of performance, unless the shoes have high appeal from a design perspective, the reality is, the shoes will not have strong marketability,” he says.

“To develop shoes that satisfy both design and functional requirements, and to develop the best products, we feel that use of analysis tools by our product designers is fundamental to improving our best practices. We are deploying Femap with NX Nastran for this role.”

Analysis software facilitates communication, marketing

Effective communication between engineers and non-engineers across the product development organization has grown since deploying Femap with NX Nastran. Even communication with consumers has improved. “It’s phenomenal…we are now using results from numerical analysis in conjunction with experimental data for promotional purposes, and it’s a valuable asset,” says Dr. Nishiwaki. “By leveraging numerical analyses, the development process can be described in the form of animation. Now, even people unfamiliar with the technology can understand our shoe development process, which helps them understand the science and art behind our ASICS brand. Such content is already used in sales meetings with ASICS distributors and representatives, and even at public seminars for general consumers.”

Ease-of-use and superior capabilities that satisfy even numerical analysis experts represent the distinctive attributes of Femap with NX Nastran. Moreover, these attributes are helping ASICS apply CAE across numerous development domains and projects. The results are valuable benefits at the front-end of design, especially from team members who don’t specialize in simulation.

Learn more about EDGE plm software:

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…

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Read the latest news from our blog:

Why focusing on ICE thermal management to reach emission regulation targets?

“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_emissions_reduction_status_vs_targets.jpgCO₂ 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+.pngEngine 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.

Register here to access the online webinar: Optimizing thermal management in modern powertrains using CFD simulation 

 

More information on our website:

Engineer innovation with CFD- focused Multiphysics simulation webpage

STAR-CCM+ v12.04: Two mouse clicks? Hold that thought!

Please note: Original publication date 06-29-2017 

 

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:

 

catalyst_particle_shapes_updated.pngExamples 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…

 

  1. …any geometry part with a name containing “__particle” to a Unite operation (this was possible in previous versions).
  2. …the Geometry Part generated by the Unite Operation to the solid particle Region.
  3. …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).
  4. … all Part Surface Contacts (created when the Volume Extract Operation is run) to Interfaces.

two_click_workflow.pngUse 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.

 

unrolled_view_mole_fraction_H2.pngA 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_filter_for_site_blockage.pngData 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. 

The Digitalization of Industrial Machinery

Sparks.jpg

 

Providing realistic virtual simulation 

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 Project 

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.pngFigure 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. 

 

Photo 2.pngFigure 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).

Photo 3.pngFigure 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.

Blinded by the Obvious

We can all be blinded by the obvious. The number of Dilbert cartoons on the topic is great evidence for how often it happens to all of us.

 

AML-13641_3002297_mutable_color.gif

 

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.  A streamlined 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 and to 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.

 

GasTurbineCHT.jpg

 

Testimonials

“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.”
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“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.”
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