It’s Sunday afternoon and I am pottering about in the kitchen cooking a Sunday roast. From the living room, I can hear my two children bickering about what they are going to play with. “Why don’t we play with Lego?” says the one. ”I want to play superheroes!” says the other. My husband is, unsuccessfully, trying to reason with them and get them to play together while at the same time sorting some paperwork. This is a typical weekend day for us. Everyone busy, on their own timescale, you could say, but trying to be together as a family. After all, isn’t that what the weekend is all about?
“Lunch is ready” I call from the kitchen, “time to set the table”. They both rush in, still continuing to talk over each other about the preferred game. We finally, sit around the table and the conversation turns more amiable. Now, we are talking about passing potatoes and veg and who wants which part of the chicken. Everyone agrees, the food is yummy!
In physics, as in life, not all processes are on the same timescale. In conjugate heat transfer (CHT) simulations that involve fluids and solids, they can actually be very different. Typically, fluids have fast transients and solids show slow temperature changes for longer periods. Accurate prediction of temperatures in solid components require long simulation times and it is essential for predicting thermal fatigue life. Such cases are turbine blades or engine blocks over the course of a typical use cycle. The challenge in these cases where we have large differences in time scale between fluids and solids is the large, almost prohibitive, computational cost.
The little Sunday routine of ours and its effect on our family life makes me think of this very issue and the new single simulation multi-timescale workflow for CHT introduced in Simcenter STAR-CCM+ v13.06. The new workflow introduces various features with the aim to eliminate the use of complicated macros. In Simcenter STAR-CCM+ v13.02 we introduced dedicated reports for fluid and solid and in Simcenter STAR-CCM+ v13.04 we improved the definition of Total Heat Flux to account for cases where radiation is turned on the fluid. And in this version, Simcenter STAR-CCM+ v13.06, we are introducing two additional very important features, an explicit mapped contact interface and solver specific stopping criteria.
The new explicit fluid-to-solid mapping links the different timescales by passing the right physical quantities, taking radiation and other thermal effects into consideration. In the case of transient flows, an efficient averaging mechanism can be employed on the thermal properties. It also enables coupling with the Finite Element solid energy solver also released in Simcenter STAR-CCM+ v13.06. This mainstreams multi-scale CHT simulations and eliminates user error.
Related to this, the latest version also provides new solver-specific stopping criteria to aid simulations that run multiple solvers consecutively. Previous stopping criteria were shared by solvers, forcing users to write lengthy macros to change the values when switching solvers. Simcenter STAR-CCM+ v13.06 moves the ownership of stopping criteria from the user to the solvers and introduces fixed stopping criteria in a “delta” sense enabling automation and consecutive multiple solver iterations. This means that in a multi-timescale simulation the fixed number of iterations will run will run without manual interaction, every time the continua is activated.
The case used here to demonstrate the functionality is an exhaust manifold with the heat shield included. It’s a case of heating up the engine up to a certain temperature. Those simulations can take up a lot of time as the solid might take a few minutes to heat up while the fluid, if run transient, needs a time step of about 1e-4 to converge. In this case for simplicity we run the fluid as steady.
Use of solver specific stopping criteria takes advantage of the faster convergence of the fluid as simulation progresses, so fewer exchanges are needed. Several stopping criteria are used to trigger a rerun of the fluid. What's particularly nice with this set-up, is that the expensive fluid part of the simulation is initially using more iterations but as the simulation progresses the number of fluid iterations required to converge to the monitor-based stopping criteria is significantly reduced. It is obvious that the new solver-based stopping criteria provide the user with easy access to tools that enable speed up of expensive CHT simulations.
In the animation you can see the temperature changes with time. The vertical lines signify a fluid run. Exchange is happening through the explicit mapped contact interface when the solid temperature shows a certain delta of temperature. This way we make sure we don’t exchange when it is not needed, and the explicit mapped contact interface takes care of the averaging ensuring accurate passing of information either side.
Which brings me back to my family lunch on that beautiful Sunday afternoon. Makes me think of how a family lunch can bring us all together, just like the explicit mapped contact interface, and how we all need to have our very own control of our time. Lunch is now finished, and we are tidying-up. As we are finishing putting the plates away I can hear them laughing. “Let’s make superheroes with Lego” they say to each other and wander off happily.
Have you ever wondered about the physics of a roller coaster?
Or thought about how strong the bolts and joints have to be to withstand the impact of the racing cart. They better be strong if people are ridding them, otherwise, there will be life-threatening consequences. The same goes for the vehicles we drive. The bolted joints are exposed to dynamic structural loads and constant vibrations daily. One loose joint could not only be extremely costly but more importantly, could put someone's life in danger. That is why it is of the utmost importance to develop safe, reliable joint solutions. This is nothing to be concerned about because innovative technology is helping many companies determine the likely causes of joint failures and help secure them.
We have established that joints are important. That is why Nord-Lock made it their goal to "provide maximum security for bolted joints." As mentioned above, innovative technology has made it so we reduce the reliance we have on physical testing. Nord Lock made this possible by adopting Simcenter 3D and NX Nastran to stay ahead of the game. Using Simcenter 3D motion software, Nord Lock is able to analyze stress states such as deformation, movement in joints, provide precision and reliability of NX Nastran solver and management of CAD. These simulations allow Nord-Lock to gain insight and validate internal business rules. For example, Simcenter is used to investigate failure situations. The weakness in joints generally have two main sources:
Spontaneous loosening caused by vibrations and dynamic loading effects
Slacking from preload loss as a result of settling and relaxation
Nord-Lock turned to digital technology as an alternative to physical testing which has helped them test both giant and small structures.
"We particularly appreciate the teams business expertise, their extensive knowledge of THE software and their availability." -Zouhair Chaib
The latest release will help you increase system simulation efficiency through a seamless process integration, maximum modeling accuracy and easy access to digital twins.
Among many other enhancements, major development efforts have been put to help you address 5 key applications:
Vehicle systems and components performance engineering
Aircraft systems performance engineering
Discover Simcenter Amesim 17 in a nutshell:
Let us walk you through the main new capabilities.
Import of electric motor characteristics from Simcenter SPEED
Expansion of air conditioning system capabilities for battery cooling
Battery thermal run-away modeling and battery pre-sizing tool
Hybrid and electric vehicle model templates
In 10 years, hybrid and electric vehicles could represent about half of the automotive fleet. That’s why there have been major development efforts to support electrification. With the newest version, you can automatically import motor characteristics from the Simcenter SPEED electric motor design software and assess electric powertrain performance early in the development cycle.
To safeguard proper battery operating conditions, you can link the battery cooling system with the air conditioning system. The new brazed plate heat exchanger component helps you easily check the capability of the cooling system to manage the battery and cabin thermal operation.
Further, for electric and hybrid vehicle design, Simcenter Amesim 17 comes with ready-to-use templates to assess consumption, range, cooling and drivability. These templates provide a good starting point for vehicle electrification projects by delivering parameter consistency and detailed internal combustion engine, transmission, electric drive, battery and cabin cooling subsystems models.
Upgraded signal bus capability and statechart management
Cooling system functional components
Real-time compatible components in the fluid component design libraries
Tunable parameters for FMI 2.0 export
In the context of software-intensive products, Simcenter Amesim 17 offers new plant modeling capabilities to support controls design, validation and calibration. For instance, the signal bus feature has been reworked to optimize central processing unit (CPU) performance and the user experience. When modeling control units, you can now easily create, edit and manage supercomponents containing statecharts.
Additionally, the release comes with real-time compatible components for automotive cooling system design as well as for hydraulic, thermal-hydraulic and pneumatic component design.
Vehicle systems and components performance engineering
Exhaust calibration tool including optimization features
Engine manifold design study through full coupling with Simcenter STAR-CCM+
Kinematics and Compliance data generator
Cam profile definition from the valve lift
Hypoid gear component
Extended modeling capabilities for vane and gerotor pumps
For conventional and hybrid vehicles, a broad set of new capabilities in Simcenter Amesim 17 will help to tackle critical challenges, such as the real driving emissions (RDE) or Worldwide harmonized Light vehicles Test Cycles (WLTC) standards. Among them, the exhaust calibration tool now enables accelerated test data import, batch processing and automated optimization of model calibration.
Moreover, by coupling Simcenter Amesim with Simcenter STAR-CCM+, you can efficiently run an engine design study for operating points of interest. This allows you to assess intake line acoustics or the impact of manifold geometry on performance.
Aircraft systems performance engineering
Intuitive and detailed jet engine performance analysis
Fuel systems and flight dynamics coupling
Fuel tank mapping from CAD
Model templates for landing gear and flap systems
In support of the aerospace and defense industry, Simcenter Amesim 17 offers unique virtual integrated aircraft (VIA) capabilities to frontload system integration, electrify propulsion systems and streamline jet engine design. It enables rapid modeling of compressors and turbines with variable geometry as well as assessing mixture composition corrections and degradation performance.
Since fuel represents a large portion of the aircraft weight, it is critical to understand its impact on handling qualities. You can now quickly assess the aircraft mass balance and trajectory while accounting for its tight coupling with the fuel system.
Moreover, Simcenter Amesim now enables you to generate fuel tank maps from CAD geometry. Therefore, you can extract the fuel inertia tensor for coupling with flight dynamics, and tank wet areas for thermal management optimization.
Embedded Simcenter STAR-CCM+ technology for enhanced cabin air flow modeling
Ego vehicle modeling for ADAS/AD validation with Simcenter Prescan
When I first joined Siemens PLM Software, Dirk De Vis, Vice-President of Simcenter Engineering and Consulting services, explained me the different types of projects his engineering team executes. Before anything else, he put a glass of water on the table and slammed his fist on the table. Obviously, the water was disturbed, splashing over the edge of the glass. My first notion of the source-transfer-receiver approach…
As you understand from this example, a noise and vibration issue originates from a source, which is transferred via one (or more) transfer paths to a given receiver location. Transfer path analysis, or in short TPA, is a methodical approach to vibro-acoustic design. It enables you to quantify the various sources and their paths, figure out which are important, which contribute to the noise issues and which ones cancel each other out.
The source-transfer-receiver concept nor TPA approach are new. All over the world, automotive engineers apply it to investigate and understand a product’s noise, vibration & harshness (NVH) performance. Different TPA methods are available: test-based and/or simulation-based. The preferred methodology depends on the structure, single or multi-reference sources, and the stage of the development.
Although, traditional approaches to transfer path analysis such as: airborne loads estimation, acoustic source quantification, structure-borne loads estimation, multi-reference TPA and energetic power-based ASQ are still relevant and widely employed, new methods are being developed.
Faster results, more accurate, better product refinement, and as a consequence faster troubleshooting at reduced cost, our customers are on top of their game. They apply TPA to benchmarking and target setting, vehicle development and pass-by noise engineering. Additionally, these new TPA methods empower suppliers to predict how their system will perform not just in one vehicle, but in a whole series of different variants. Component-based TPA using blocked forces is a prime example of how new TPA methodologies put the relationship between OEMs and suppliers in a completely new perspective.
Electrification is in the air and is everywhere. There’s not a week we don’t hear about a carmaker getting public about a new electric vehicle within a specific segment. If the electrification trend was still questioned a few years back, it is now impossible to deny it. As the New York Times mentions, what was considered as a “Californian-way-of-life” accessory not a long time ago is now entering mainstream. 2017 is considered as the year in which the electric car became inevitable, and even the most skeptical automakers (like Toyota) announced plans to develop battery-electric vehicles. But there are still resistances to dissipate to go for a massive public adoption. In addition to acquisition cost, range anxiety is still a key issue to overcome, even if vehicle brought to the market today easily reach 200+ miles.
@Bloomberg - Sales volume prediction of EVsThere is undoubtedly fun to drive an electric car (it’s quiet, it as an incredible power and torque) but for everyday driving, could we really live with a battery-powered vehicle? With electric cars, no tricks, no cheating possible. Who has never gone out of gas and walked to the next station with an empty bottle of water to make it possible to refill for the 5 kilometers required to reach the “black gold oasis”? I admit, it happened to me. But we definitely cannot use our portable mobile phone battery charger for a car – actually the analogy of being anxious to get out of battery for your phone really rings a bell, so I can definitely start getting what could be the feelings of an e-car owner.
@BMW Range ExtenderThe hilarious blog “My husband’s electric car – Tips for surviving life (and marriage) with a Nissan Leaf” totally addresses those issues – in a very funny way. “My husband and I had an important appointment and dinner planned for last Monday evening. So why did he arrive late? And why, afterward, did we get stuck eating dessert in an empty parking garage at 10:30 p.m.?” Deborah Petersen speaks up on what it is to own an electric car, and addresses charging stations, driving pleasure, life spontaneity, and much more. But electric car drivers just want to have fun, don’t they? So, how can we arrange that?
Range anxiety is a strong point being addressed by car makers and manufacturers. But how to extend range? This is not that simple, and it ends up being a combination of technology and engineering choices. Range is strongly dependent on a variety of factors: the battery chemistry selection, the e-motors performance, the thermal management of electric devices, the choice of material that impact weight, the car design and aerodynamics, etc. It's not only about the battery itself. And decisions made to improve range need to be balanced with the impact they have on drivability, safety, NVH and design. Adding cells to a battery can improve its power, but it also adds weight that can totally anihilate the benefits of its additional power. It’s all about balance, and this requires to compare hundreds of combinations of different systems, at a component, system and vehicle level. Being able to rapidly analyze what is the best vehicle architecture and what are the best options in terms of systems choices requires new engineering methodologies.
But the good news is, that’s what we do at Siemens PLM Software. We propose simulation and testing solutions covering every aspect of electrification for both hybrid and electric vehicles. We offer a complete, integrated, and accurate digital twin addressing challenges for all aspects of electric and hybrid vehicle engineering - from electric powertrain to electrical/electronic system architecture to vehicle engineering, controls, and embedded software. This not only enables companies to achieve a significant competitive advantage, ROI, and operational performance edge in developing electrified vehicles but also empowers them to adapt and evolve in the fast-approaching era of new mobility. We believe vehicle electrification will play an essential role in reducing greenhouse gas emissions, and we are committed to support the electrification supply chain with engineering tools and solutions to help develop cars that are affordable, safe, performant and with a comfortable range.
If you want to know more about our solutions for vehicle electrification, we’re holding a live webinar on Nov 15where we’ll talk about what we do for our customers to support vehicle and system performance engineering in the context of vehicle electrification, and, as an example, how we help them to make range anxiety become something of the past. Join us, it’s free and fun!
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Our world is changing and becoming increasingly complex. I feel that every day we are facing more and more challenges implicating conflicting goals, both in personal and professional lives. We keep hearing “more”, “better”, “faster”, and “cheaper”. No matter how conflicting those words are to each other, we want them all. I recently noticed that I had very little free space in my work calendar for the upcoming weeks, many blocks filled out with travels, meetings or calls. I got quite concerned about the lack of time I actually had to get my work done. This reminded me of an article I read which nicely captured this issue in the form of the two-by-two matrix below. The bottom axis is “doing”, basically all the meetings scheduled in my calendar, the left axis is “thinking”, meaning the time I need to prepare and get my work done. We always happen to be in one of the four boxes. The “Space” box is the perfect opposite of “Burn” one: low thinking, low doing. It’s the sandy beach vacation or relaxing with a glass of wine on the terrace of your house. I always get teased by my American colleagues because we get a lot of “Space” box in France, lucky us! In the “Think” box, your mental is actively working and doesn’t allow you to do much. The “Do” box is pure activity without giving any chance to your mind to think. Obviously, Thinking and Doing are two competing objectives and achieving success relies on the ability to move between these boxes and accept to achieve one of them at the expense of the other… The optimal answer depending on your goal for the day and the constraints you have that day.
Figure 1: The four boxes, The Happiness Equation - Neil Pasricha
In the industry, competing goals are present everywhere, especially in engineering. Evolving technical challenges are pushing the technology to its limits and often forcing engineers to make hard choices. Increasing performance while reducing energy consumption, increasing robustness while reducing mass, increasing comfort while reducing costs are some example of recurrent conflicting goals problems. Because every design must satisfy competing objectives, there must be a compromise among, if not the complete exclusion of, some of those objectives, in order to meet what are considered the more important of them. The overall challenge being to evaluate the cost to improve one objective in terms of deteriorating the other ones. Multi-objective optimization, also known as Pareto optimization, is then relevant to assess the trade-off between two or more competing objectives.
In Pareto optimization, there is no single best design but multiple best designs depending on which objective we want to favor and which one we are ready to sacrifice. The goal is to find the relationship or trade-off among the objectives. The result of a Pareto optimization search is a set of optimal design points, also known as the Pareto front. But gathering this data is just the first step. It is crucial to then have the right visualization tools to confidently understand this relationship and the relative cost/benefit ratio between objectives. The new Pareto Plots feature of Design Manager in Simcenter STAR-CCM+ v13.06 aims to help you quickly visualize these trade-offs across designs between two plotted objectives. If you have more than two objectives, you can create a Pareto Plot for each of the combinations of the objectives.
Figure 2 is the Pareto Plot of a multi-objective optimization that was runfor an industrial exhaust system aiming to decrease the pressure drop while increasing the velocity uniformity. The blue dots form the Pareto front which doesn’t tell us which design is the best but provides us information on which design is optimal for a specific trade-off between pressure drop and velocity uniformity. This information is essential to help the engineering team makechoices while understanding the related repercussions.
Figure 2: Pareto Plot for an Industrial Exhaust System. The Pareto front, formed by the blue dots, represents the optimal designs found in the Pareto optimization study. The green square represents the baseline design. The gray dots represent the other non-optimal designs. The green dashed lines represent the constraints on pressure drop and on velocity uniformity.
In figure 2, let’s consider the optimal point with a pressure drop equal to 54.65 Pa and a velocity uniformity equal to 0.8613 (bottom left point of the Pareto front). If we strongly need to increase the uniformity, the cost in pressure drop to reach a velocity uniformity of 0.8818 is an additional 1.22Pa (4th point of the Pareto front starting from the bottom left one). Let’s now consider the optimal design with a pressure drop equal to 60 Pa and a velocity uniformity equal to 0.9145 (top right point of the Pareto front). If this time we prefer to decrease the pressure drop, reaching a pressure drop of 57.51 Pa will cost us a decrease of 0.0581 on thevelocity uniformity. So Pareto Plots will help you quickly and easily analyze your resultsand understand the consequences of any design decision.
The goal of a Pareto optimization search is to identify the entire Pareto front of the design space, which may extend over a wide range of the objectives. At the same time, the algorithm tries to spread out the design points as evenly as possible on the Pareto front. But sometimes, you might be interested only in part of your design space because you are aware of some constraints that limit the acceptable range of your objectives. In order to optimize the overall timeof your study and focus the search on the part of the design space that is of interest to you, it is highly recommended to apply these constraints to your objectives. That’s what we did in our industrial exhaust system case. A constraint on the pressure drop has been set to a maximum of 76 Pa and a constraint on the velocity uniformity has been set to a minimum of 0.85. In figure 2, these constraints are respectively represented by a vertical and a horizontal green dashed line. Coloring the constrained design space in green helps clearly visualize the area including the potential feasible designs. We also notice that the algorithm has focused the search towards this area of the design space since the Pareto Plot displays a higher number of calculated design variants in the top left box.
Another key benefit that Pareto Plots will provide you is the possibility to control and minimize the overall time of your optimization by monitoring its convergence. As long as your search is moving forward, each optimization cycle will provide you with new optimal points and therefore a new Pareto front. But once the search has converged to the optimum set of optimal points, the Pareto front will converge as well to reach a final state.On the video playing below, we are animating the Pareto front through our 84 different cycles calculated for our industrial exhaust system Pareto optimization. This animation captures powerfully the evolution of the optimization and highlights how the Pareto front can be used as a good convergence indicator.
Last but not least, Pareto Plots in Design Manager comes, as any other plot, with all the power of synchronization with the Design Table and the different snapshots of your study. As you can see on the video below, you can open snapshots directly from the Pareto Plot results. Clicking on any point of the Pareto Plot will also automatically update any snapshot displayed and highlight the associated design in the Design Table.
Coming back to my crazy upcoming weeks... I unfortunately don't have a way to easily assess trade-offs between Thinking and Doing objectives as you now have in Design Manager with Pareto Plots. But as paradoxical as this can sound, I am sure that stopping by the "Space" box for a bit will help me figure this out!
S-FMEAs (System Failure Mode and Effects Analysis) and dysfunctional analysis in general are becoming cornerstones of product development when it comes to autonomous vehicles. As an example, functional safety standards like ISO 26262 and 15998 are more and more adopted by OEMs and suppliers to ensure their products' reliability and safety.
In this blog post we explain why current Excel-based S-FMEA process can be error-prone and why connecting it to Simcenter Amesim can improve its overall quality.
Based on my experience, the process of working with S-FMEA is the following:
At the beginning of a given project, reliability team asks system responsibles to update their S-FMEAs.
The S-FMEA update is done through several brainstorming meetings with reliability engineer, system responsible, software engineer, hardware engineer... The aim is to determine the failures, their causes and effects and their importance using the Risk Priority Number (RPN) = Severity x probability of Occurrence x probability of Detection.
Once the RPN is defined for all the failures listed, a report is shared with the project manager who decides for countermeasures. It can be either new requirements, new software calibration, new component sourcing, ...
The action plan to deploy these countermeasures is then handled by the project manager.
S-FMEA is to be complete at a given project milestone (typically when product design is frozen).
Quite often, the S-FMEA is done using Microsoft Excel and stored in a shared repository (typically Sharepoint).
The content of S-FMEA strongly depends on human factor and so is error-prone. If one expert leave, the knowledge can vanish; if the expert don't think out of the box, he may miss some failures; if one team member cannot join all the brainstorming sessions, the S-FMEA might be incomplete...
To avoid these drawbacks, a solution is to leverage the Simcenter Amesim digital twin of the product developed and used by CAE teams.
Let's see how it works considering the Battery Management System S-FMEA of a given vehicle.
The first step of the "digital twin-assisted S-FMEA" is, as before, to identify the failure modes and causes of the system through brainstorming sessionsS-FMEA failure modes and causes identification
Then, with the support of the Simcenter Amesim model owner (typically the system engineer or the simulation engineer), determine how these failures could be simulated on the Battery Management System model. This can be done through the Simcenter Amesim Excel Add-in by drag-and-dropping the selected Amesim parameters:
Once the connection is done, define some failure values to selected parameters (either in Excel or in Amesim) and run simulations to see their effects dynamically on BMS performances.
Amesim simulation results
Finally, by analyzing these results, fill-in the S-FMEA effects columns so as the Severity and Probability of detection ratings. This step can be automated using Simcenter Amesim post-processing and scripting capabilities. Regarding the Occurrence rating, it requires a statistical approach that is not part of Amesim native capabilities:S-FMEA RPN calculation
We end up with much more reliable S-FMEA outputs that include the following benefits:
Combinatorial failure effects easier to understand
Bi-directional connection between S-FMEA and digital twin (Through Simcenter Amesim Excel Add-in)
Then, the identified hazards can be followed-up by the project management team using Teamcenter or any other PLM solution.
A similar example of model-based fault diagnosis is depicted by Salim et al., 2017 .
Is this way of working similar to yours? Do you think it could help maximizing the value of your digital twin? Do you see any limitation to this methodology? Please comment or reach out to me.
 R; Salim et al., Fault Diagnosis of a Commercial PEM Fuel Cell System using Simcenter Amesim, IEEE 2017
Meet Albert: Albert has worked with Simcenter Amesim for 2 years. He is working right now on a project which is meant to be re-used company-wide. Once his model is created and validated, it will be used by people who have never used Simcenter Amesim. They might need to adapt some parameters based on specific customer needs and be able to give good information to their customers. Albert thinks about programming a dedicated Excel sheet using Visual Basic, so that they can access a simplified user interface where they only need to change parameters and get results. Yes, that’s possible but Albert needs to know someone who is able to program in Visual Basic. Albert wishes there would be a simpler way to share models with these guys, define parameters and variables of interest, and let them work easily.
Meet Joseph: Joseph needs to get access to these Simcenter Amesim models. Albert prepared an Excel worksheet for him but it seems Joseph needs a full Simcenter Amesim installation on his computer. He also needs to open a ticket to his IT because the license server is not configured. He comes to the conclusion that he had better ask Albert to quickly run a simulation with a specific set of parameters and send back the results as soon as possible, as the customer is waiting for feedback. Joseph wishes he could depend less on Albert and his colleagues and get an instant access to the results he needs.
Meet Frank: Frank is an IT administrator, he sets everything up so that Simcenter Amesim is deployed in Albert's department, but Joseph has never needed it until now. He is questioning the reason why he opened a ticket for installing a license server on his machine? He thinks it is simpler to deploy a full Simcenter Amesim installation on Joseph’s laptop the next time he will be in the office. Frank wishes he could allow both groups to collaborate more efficiently without having to deploy the simulation software to people who are not supposed to use it.
This story is pure fiction but totally relates to some specific needs our customers already expressed. As we’re strongly committed to answering our users' needs, we are proud to introduce you to our new product, Simcenter Webapp Server.
This web-based solution will enable collaborative work between different departments within an organization. By re-using Simcenter Amesim watch parameters and watch variables, it will allow Albert to quickly make a validated model available for Joseph and his colleagues. With a simplified web-based user interface, it will allow Joseph to quickly access the important information that matters to him: the model, the parameters and the results. And it will provide Frank with a centralized installation of the server.
As a conclusion,
Albert can share his models independently
Joseph can access and run them directly from his browser
With the on-premises installation, the IP never leaves the company
For more information on Simcenter Webapp Server, watch our video:
Gears. Gears are used since the middle ages. Should be settled science by now, right? That’s about what I thought when I started. However, after discussing with colleagues and digging into the work of our research team, I recognized: Far from it! The current trend from Internal Combustion Engine towards electric motors reveal noise and vibration issues, which many companies in that industry do not have a solution for to this day. Part of them are originating in the gearboxes, and to solve them you need advanced simulation methods and high-end testing methods.
Siemens PLM Software is focusing on rotating machinery, gears and driveline, both from the test and simulation methods and tools perspective. Today's post is the first of a two part blog post that gives you more insight in the high precision gear test rig, that we have built for the validation of advanced multi-body simulation methodologies, so we can increase the confidence in virtual prototyping based on real-life data.
1.) Context: NVH, need for light and complex gear geometries
Gears are key components of any mechanical systems containing transmissions and drivelines. Widely used in industrial applications, their range of application goes beyond automotive, marine and aerospace industry. The requirements and regulations on the noise and vibration levels (in terms of safety and comfort for the users and the environment) become restricted every year. As a result, current industrial needs are centered on higher power-to-weight ratios, robustness, power loss reductions and improved noise/vibration behavior of the transmission systems.
The prediction of gear characteristics using simulation tools can help to optimize designs and to significantly reduce production costs of gear transmissions. As the accuracy and robustness of numerical modeling techniques improve constantly, gears can be designed in their optimal operating configurations, leading to quieter power transmission systems. The quality of simulation tools is usually evaluated by experimental validation. The comparison of the vibration levels between numerical and experimental results is a good indicator of the modeling accuracy.
Figure 1: The mesh is adapted to the complex geometry of the lightweight gear, including three holes on the body. (Illustration made in Simcenter 3D)
2.) The Solution: Simcenter 3D
The Simcenter portfolio by Siemens PLM Software is all about combining the best of both worlds of Test and Simulation. Currently, the RTD team of Siemens PLM Software is very active to extend its Simcenter 3D offering to drivetrain and powertrain multi-body simulation. Siemens has a wide portfolio of gear multi-body simulation capabilities, ranging from fast screening methods to efficient and very accurate advanced analysis methods. Also, we have the capabilities and experience to validate our simulation methodologies for these and other complex mechanical engineering scenarios. Lastly, leveraging our Simcenter Engineering services department capacities.
Figure 2: The gear test rig is mounted on a large concrete base block. (Image credit: SISW)
3.) Zoom on the Advanced FE modeling techniques
The advanced FE preprocessor is the most accurate gear contact modeling methodology at Siemens. This method is based on a novel formulation, leveraging on model order reduction technologies, and therefore highly accurate as well as relatively efficient (see more information in the white paper). The pre-processing tool named FE preprocessor has been developed in a joint cooperation between the KU Leuven and the University of Calabria. The advanced FE preprocessor methodology allows the modeling of gear bodies with complex shapes. The capabilities have been extended to account for a wide range of flexibility-induced phenomena (lightweight gears, convective coupling effects, etc.).
4.) The Gear Test Rig: motivation, capabilities, and instrumentation
For the purpose of validating the accuracy of the gear multi-body simulation methodologies on the basis of experimental data, Siemens PLM Software, KU Leuven and the University of Calabria have designed a high precision gear test rig at the Siemens PLM Software premises in Leuven, Belgium. The main objective of the test rig activities is the assessment of typical gear-related physical quantities in static and dynamic conditions, under controlled operating conditions (torque, speed, center distance, misalignment). The test rig provides the possibility of heavily instrumenting a gear pair under controlled operating conditions. Recently, a lot of efforts have been dedicated to the application of strain gauges (both at tooth root and gear body) on lightweight gears. The gear test rig is used to experimentally investigate the response of the system while excited by a meshing gear pair. The test rig is presented in Figure 2. The different components of the test rig are illustrated in Figure 3.
Figure 3: Gear test rig three-dimensional representation.
5.) Cooperation context: RTD projects from which the Gear Test Rig originates
The gear test rig has been designed and manufactured in the framework of the EC Marie Curie project “DEMETRA” and the VLAIO project “ECO-Powertrain”. The Marie Curie project framework is dedicated to the recruitment of new staff members and the exchange of staff members between organizations, which has made the “DEMETRA” project a fertile ground for the exchange of technologies, ideas and personnel between the partners Siemens PLM Software, KU Leuven and coordinator University of Calabria (UNICAL), which have all been instrumental for the achievement of the gear test rig.
6.) Lightweight gears
In a recent experimental campaign performed at Siemens, a pair of precisely manufactured gears have been tested using the in-house test rig. The lightweight gear has a thin rim and three holes in the body. On the other side, a solid gear is mounted with identical dimensions, but a slightly different microgeometry. Once the gears are fully instrumented, they are mounted on the test rig, to be analyzed in different operating conditions (torque, speed). The lightweight gear pair is shown in Figure 4, in a mounted configuration.
Figure 4: A pair of solid (left) and lightweight (right) gears are mounted in the test rig. The gears are instrumented with strain gages in the tooth root and on the body. (Image credit: SISW)
Join us next week for part 2 of Gear Test Rig which focuses on the type of measurements that are possible with test rig!
We are pleased to introduce version 17 of Simcenter Embedded Software Designer. The solution enables architecture-driven software design and moves the state-of-the-art in model-based software engineering closer towards managing the industrial complexity of software development.
“Simcenter Embedded Software Designer extends the Siemens MBSE strategy to domain solutions for software engineering,” said Vincent Braibant, Vice-President MBSE Strategy, Simulation and Test Solutions at Siemens PLM Software.
The automotive industry is challenged by the complexity of vehicles becoming “digital devices on wheels” in the context of mobility as a service and self-driving vehicles, increased variability and shortened development cycles, including over-the-air updates, improved quality and distributed development processes. Automotive companies are revisiting their software engineering toolchains to address collaboration issues and streamline their portfolio of applications for more integration.
This is where Simcenter Embedded Software Designer brings value.
As a software architecture design solution, Simcenter Embedded Software Designer extends the definition of the architecture models coming from standard system modelers to create executable contracts frontloading and enforcing specifications for the implementation, integration, validation and deployment steps. As such, Simcenter Embedded Software Designer supports upfront analysis and verification of the architecture consistency and interfaces. It also facilitates integration after implementation and automates unit and integration testing. Consistency throughout the design process, distributed implementation over different tools and internal and external suppliers, and validation of performance with SIL testing are enforced.
Moreover, Simcenter Embedded Software Designer offers bidirectional traceability with Polarion ALM and potentially other ALM tools as well as automated test result reporting and communication between all the stakeholders.
Simcenter Embedded Software Designer 17 is now available
The latest release comes with an open approach towards software implementation, extending the use of software architecture one step further. This enables users to directly generate implementation template in form of C-shell or MATLAB Simulink model for software implementation.
The new release also strengthens the re-use of the existing legacy code, at the architecture design level by enabling the easy extract and instrumentation of software architecture from C-code, and at implementation level by verifying the consistency of existing legacy code with contracts.
To sum up, Simcenter Embedded Software Designer 17 will help you unlock architectures and increase re-use by offering features that are easy to use and incorporate in your existing process. Therefore, you can make better-informed decisions much earlier in the development cycle.
Want to learn more about Simcenter Embedded Software Designer? Discover other blog posts.
When less is more ...it’s like déjà vu all over again!
A while back, I published a blog on a significant improvement to our Solution History (.simh) capability delivered in STAR-CCM+v10.04 where, instead of having to work with the entire volume representation of a .sim file, you could store boundary surface information only and work with that instead. This led to a substantial reduction in .simh file size, increased your productivity and enabled a much more practical ability to analyze your data. So, what’s new again? Solution History support forderived parts! In STAR-CCM+ v11.04, you’ll be able to store iso-surfaces and all section types (plane, constrained plane, cylinder, sphere, and arbitrary section) in your Solution History files.
Anyone who has set up and run a CFD study will likely be able to relate to this:“In theory there is no difference between theory and practice. In practice there is.”As a practical case study then, let’s look at a device called a caterpillar micromixer.
The mixing section length of this device is about as big as a dime, yet it's capable of producing several liters of product per hour. Two feed streams are introduced upstream and the unique static geometry of the device effects the mixing. In order to confidently predict our mixing behavior, we’re likely to run this case at several different mesh resolutions, producing several .sim files. The mixing tracer concentration is shown on two derived part plane sections below for three different mesh refinements.
Side-by-side comparison of mixing for different mesh sizes
This side-by-side illustration gives us a way to effectively compare these cases.In theory, we could load each sim file one at a time and generate our illustrations and figures of merit.In practice, we’ll create a solution history file for several simulations, saving only tracer concentration on the derived part planes. Comparing the .simh file versus the .sim file size we see a general file size reduction factor oftwo orders of magnitude. And as the .sim file size increases (due to an increasingly finer mesh) the file size reduction factor gets better - the bigger your cases are, the bigger the potential benefit when you use derived part based .simh files.
simh file size compared to the sim file it is derived from
Here’s yet another benefit: you can load multiple representations on top of the underlying simulation that they are derived from – that means you can do side-by-side comparisons for multiple cases usingonly one STAR-CCM+ license.
Let’s extend our caterpillar micromixer analysis by running it unsteady, imposing alternating sinusoidal pulses on the feed inlets to improve overall mixing. We’ll set up three Field Monitors based on the mixing tracer: one for the maximum, another for the running average for all time steps and, a third sliding average with a window matching the sinusoidal period for the field inlets. Stepping back for a moment, Field Monitors collect data, and when we are working with sliding monitors, this can generate a “scary” amount of data. If you’re thinking this kind of transient data analysis is prohibitively expensive, without .simh for derived parts, you’d be correct. But, here’s what we achievein practice. The single.sim file after 400 time steps is ~6.4GB. The corresponding .simh file, with all three Field Monitors plus the instantaneous tracer concentration, stored along a mid-plane derived part, for all 400 time steps, is ~5.0GB. By not having to save all the transient time steps as .sim files, we realize over a500Xreduction in terms of file storage.
We appreciate you typically don’t have time to re-run transient cases. Let’s say you want to check something you noticed in the middle of your simulation –“you can observe a lot just by watching after all.”Still, you’re faced with a tough decision – do you go with what you have or, re-run your case to the timestep of interest? Consider your options with Solution Histories. Back to our practical problem, to run 400 timesteps it took ~9 hours with my modest resources. Working with the .simh data, generating the transient animation took just 15 minutes. OK, you only realize the time savings (a factor of35X) if you need to re-do your animation by re-running your transient case. However, in 15 minutes, you can create a completely new animation using just your .simh content. Or, improve the effectiveness of an existing animation, adjusting the scalar values to a range more appropriate for all your timesteps. Finally, let’s say you need to quickly review results in a presentation to your peers or customers. Navigate to the desired state from a list of all your stored states – the time to update your results is typically a few seconds since you’re already working with just the derived parts. So, how much time can you save?
Time savings when using a simh based workflow
The bottom line here is that the smaller .simh files need fewer resources and working with them is simply faster. Fundamentally, this supports one of the key objectives of efficient data analysis: Work with the smallest possible data representation needed to make effective decisions. While the list of derived parts being delivered with STAR-CCM+ v11.04 is not complete, it does cover the ones we tend to use the most often.“After all, if STAR-CCM+ was perfect, it wouldn’t be”– I’m paraphrasing Yogi Berra, and kindly acknowledge his wisdom and humor (there are three other quotes attributable to him herein). And, to close things off, spoiler alert, look for broader .simh support for derived parts in the STAR-CCM+ v11.06 release, with still further enhancements planned after that!
What is the most annoying sound and why is it only 0 dB? How come a source located in the back of a car causes noise issues at the front seat? How can I become more efficient at my work without spending a single euro? And finally, does MLMM mean everyone can now become a modal analysis expert? To answer these questions, and many more, come to the Simcenter Conference - Europe in Prague!
Join the trainings during Simcenter Conference - Europe.
Benjamin Franklin once said:
Some wise words from Mr. Franklin
Staying true to his words, the Simcenter TEST division is extremely proud to invite you for a day full of classes, demos and trainings for 4 very exciting and important topics.
Best practices of sound quality engineering
Sound is a way to convey emotions. In today's market, sound has become a key marketing tool. Products are not judged only on the amount of noise they generate but also on its quality. Closing a car door, or turning on a vacuum cleaner might make us choose brand A over brand B. Communicating the right information through the sound of your product is hard to achieve without a proper sound quality system. This is where simulation and test solutions from Simcenter portfolio can help you. We will talk about the human auditory system, binaural measurements, objective analysis such as using sound quality metrics and jury testing!
Enhanced integrated transfer path analysis techniques
Noise and vibration are important aspects of modern products and it is of great interest to pinpoint the exact root cause of noise and vibration problems. Transfer Path Analysis (TPA) enables engineers to differentiate between potential problems with system characteristics or excessive excitation of the system. Furthermore, it allows the NVH engineer to propose the ideal counter measures and design targets early in the development cycle. It is a critical tool in the NVH engineer's toolbox when dealing with understanding or finding the root cause in context of troubleshooting activities. The technique can be used to assess structureborne and airborne energy transfer paths – from an excitation source to a given receiver location, since the sound at the receiving end will be decomposed in detailed contributions from each of the relevant noise sources. The methodology allows the NVH engineer to have complete insights into the noise, vibration and harshness (NVH) behavior: leading to faster troubleshooting and better product refinement.
Driving innovation and productivity with Simcenter Testlab Neo
The lecture will highlight the potential of Testlab Neo to increase productivity and usability of physical testing. The new developments focus on allowing the engineer to take much quicker decisions, improve data confidence and provide better design insights thus supporting the engineering exploration and assuring a better collaboration within your engineering process.
Check this sneak preview of some of the coolest Testlab Neo features!
Modal analysis: new techniques and methods
The modal analysis technique continues to evolve, particularly when it comes to usability, robustness and expanding the scope of problems that can be tackled with confidence. The lecture will introduce MLMM (Maximum Likelihood estimation of a Modal Model) method which not only helps in solving complex coupled vibro-acoustic problems, but by optimizing the modal parameters it allows to better represent the measured data and thus derive more reliable modal parameter estimates.
Sensile Medical AG, a medical devices company based in Haegendorf, Switzerland, develops and produces innovative rotary micropumps for highly precise dosing and application of liquids. From the beginning the company has used Solid Edge, and more recently has supplemented that with Femap for simulation, NX CAM for numerical control programming, and Teamcenter for data management. Sensile Medical feels that these applications empower strong growth and compliance to various international requirements and standards of the industry.
The company reports that simulations using Femap, with its Solid Edge integration, are quick and easy, save time in the development process and accelerate change cycles. The Swiss Federal Institute of Technology Zurich tested the first prototype of the SenseCore pump. Sensile Medical found that the pressure results from those tests closely matched the Femap simulation results, quickly building confidence in the use of the software. Today Femap is primarily used to perform stress analysis, providing proof to clients and authorities that the complex devices are able to withstand the anticipated loads. Simulation is also an important resource to help with the relentless requirement for miniaturization in the industry.
You can read the full case study for more information on how Sensile Medical leverages Femap and other Siemens PLM Software solutions to help achieve solid growth.
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