The analysis of an axial-flux machine requires three dimensions because of its inherent three-dimensional magnetic flux paths. Thus, to accurately predict its performance, you must consider the complex interactions between the 3D magnetic components, especially for NVH and structural performance. Unfortunately, 2D analysis, with its faster solving speeds, neglects one of the components, whereas the 3D FEA is time-prohibitive in design exploration. Save weeks of work with an improved axial-flux EMAG workflow To reduce the cost of electric vehicles, manufacturers must lightweight their powertrains by optimizing their motor’s power density, a characteristic that axial-flux motors excel at. However, the requisite 3D design exploration will be slow and prohibitive. The solution is to explore early concepts with equivalent analytical models in Simcenter E-Machine Design . Once your initial design converges, it is automatically sent to the 3D environment of Simcenter 3D for a detailed multi-discipline analysis of the motor. Hence, integrating Simcenter 3D and Simcenter E-Machine Design , released in 2024, provides an enterprise solution that quickly searches for an optimum axial-flux design. The selected design is then automatically imported into Simcenter 3D , bringing the EMAG geometry and analysis into a multi-discipline validation analysis that includes NVH, Structural, and CFD-thermal. The import includes the automatic generation of the EMAG geometry and mesh. The air core axial-flux machine To further support axial-flux machines, the 2412 release of Simcenter E-Machine Design , now supports a new stator air core template for axial-machines in addition to those of the radial-flux topologies. These are important in ultra-precise position servo control and near-silent running applications. Air core stators eliminate the no-load cogging torque ripple. Accurate system-level e-motor thermal representation Accurate system-level thermal representation of the e-motor affects the sizing of the cooling circuit and battery capacity. Therefore oversimplifying the motor’s thermal model will result in costly powertrains. A solution has long been desired for this problem. That is an accurate, fast, and easier-to-extract thermal representation of the e-motor in the form of a lumped-parameter thermal network (LPTN). However, analysts must work for weeks to create an LPTN, which may not even adapt to changes in rotor topology. For example switching from an interior PMSM to a spoke-type, or PM assisted rotor topology. The new release of Simcenter E-Machine Design 2412 , and Simcenter AMESIM addresses this challenge. The Simcenter E-Machine Design radial-flux synchronous machine (BLDCs, PMSMs, etc) thermal models, are automatically converted into LPTN in Simcenter AMESIM . The created LPTN model is customizable at the AMESIM system level while supporting multiple operating points. Release 2412 Highlights This 2412 release of Simcenter E-Machine Design accelerates the development of axial-flux machines. It allows you to quickly search for a design, that is then automatically imported into Simcenter 3D . This fast tracks the consideration of EMAG in a multi-discipline NVH, Structural, and CFD-thermal validation analysis of axial-flux machine design parameters. In addition, for radial-flux synchronous machines (BLDCs, PMSMs, etc), together with Simcenter AMESIM , barriers to accurate and fast e-motor LPTN thermal models have been addressed. Schedule a meeting with CAEXPERTS and discover how to optimize your axial flux motor design with Simcenter E-Machine Design solutions! Reduce analysis time, improve thermal accuracy, and accelerate your innovation with our expert support. Contact us now and take your designs to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

CAD-embedded CFD simulation The new Simcenter FLOEFD 2412 software release is now available in all its CAD-embedded CFD versions, and Simcenter 3D embedded version. This release delivers electronics applications enhancements for PCB thermal analysis workflows. For example, in the area of processing imported EDA data and component placement, there are new ways to add libraries and quicker methods to swap thermal models of components within the EDA Bridge utility. Users are also able to more easily create of 2-resistor and LED component thermal models directly in the interface. Learn more about improved handling of missing material for thin gaps in complex CAD assemblies and read on below to discover many other topics such as examples for simulation automation, new plots and goals location functionality, and even batch simulation processing capabilities. For NX users – You can also learn about the newly released NX CFD Designer , a new accessible native NX simulation tool, powered by Simcenter FLOEFD technology, that supports designers with a subset of common fluid flow and thermal analysis capabilities. PCB thermal analysis enhancements EDA Bridge: Libraries and swapping PCB component thermal models Positioning and replacing component models with IC package thermal models of suitable fidelity as part of the process of EDA data import into the electronics cooling tool environment can be a manually time-consuming task. In response to a popular user request, in Simcenter FLOEFD 2412 , there are enhancements to the EDA Bridge utility so that that you can more easily replace components with thermal models contained within an existing library source. Libraries – adding sources Users can add libraries of component sources from either local files, mapped network locations or multiple locations. This way you can leverage your organizations component libraries as you expand them, or if you are importing component libraries from Simcenter Flotherm XT. You add sources from within the component library in EDA Bridge .  This operation does not modify the location files of the sources and you can remove library sources without impacting the files where they are located.  You can also refresh sources so they are up to date when in the Component Library window. Replacing components  in EDA Bridge – new methods You now have the following replacement options from within EDA Bridge : 1) Replace automatically (Replace Matching) Matched by Package Name, Part Number or Thermal Model ID Specify model level type to use 2) Replace manually (Replace Selected single or multiple components) There is also scripting based support for the above replacement operations so that you can leverage these new capabilities in your automated workflows. Watch the short illustrative video below. A workflow is shown starting from a detailed thermal model being added from Package Creator utility as a source into a library and then the steps within EDA Bridge are shown for replacing a selected simple component using the “replace matching” option. The model is then PCB and replaced component are then transferred from EDA Bridge to Simcenter FLOEFD for NX . Additional new option awareness: When transferring from EDA Bridge to Simcenter FLOEFD , you can now select to import components from EDA Bridge as parts or bodies, depending on your preference. You can therefore specify if you want a single multi-body part or an assembly of parts based on your preference. Component Explorer: 2R thermal and LED component model creation When you have a board level model with a high number of components and you want to create and edit the component thermal representations, this can be time consuming and there is also potential for errors in a repeated manual entry approach to this task. You can now specify LED or 2R components using Component Explorer directly in Simcenter FLOEFD 2412 quickly and also leverage Excel import of tabular feature data. 2R models procedure Specify the power in the 2-resistor column Select the component type Or for a list of components you can fill in the Component List Excel spreadsheet with 2 resistor parameters and then import it into component explorer to create all features instantly. This is illustrated in the following video: How to export all component temperatures quickly – a reminder on using the Heat Flux Plot While on the topic navigating and selecting multiple components, a reminder for users that you can use the heat flux plot to select multiple components or all components and then export in tabular format to Excel component temperatures. Learn how in this video below. It starts from a solved model looking at surface temperatures and flux plots enabled and shows the steps to export component temperatures: LEDs : specifying features individually or for multiple models The following video shows the selection of all LED components in Component Explorer and then either editing amp value and LED type directly for selected LEDs or importing a table of updated information via Excel spreadsheet. Component Explorer: Surface source power listing and summation It is advantageous in thermal management studies to have a clear way to see different sources power across a board and to consider overall power budget contributions. Within component explorer a new column has been added to reflect surface source power. The following video compares viewing a single source vs viewing a group of sources in Component Explorer. Smart PCB and Simcenter FLOEFD API Automation: SVG export/import To aid automation of script driven, automated what if analysis by leveraging the Simcenter FLOEFD API , it was identified by users as advantageous to be able to modify copper regions on a Smart PCB after it is imported into Simcenter FLOEFD 2412 . To support this, functions have been added to the API to export and import a Smart PCB to and from a set of SVG files. Export, creation script commands and SVG tags so you can fill areas with copper are detailed in documentation on Support Center for users. Material handling in CFD simulation of small gaps in assemblies: Fill Thin Slots feature Thin gaps are often in CAD assemblies. For example, these may be between heated components and heatsinks on a PCB or they are gaps between glued parts may exist in models. These gaps are meant to be filled with Thermal Interface Materials (TIMs) or glue during assembly procedure. For simulation models with unfilled gaps, their presence may impact thermal results. In Simcenter FLOEFD 2412 , you can opt to fill mesh cells inside a gap with specific solid material automatically in accordance with thickness criteria value. This is faster than creating specific geometry parts in the CAD model to fill the gap. The mesh and shape created is viewable with postprocessing tools stage. The following video illustrates the steps involved to leverage this new feature. Structural analysis: 2nd order elements This release introduces 2nd order elements to reduce reliance on overmeshing for some models to maintain accuracy e.g models such as thin plates with significant temperature gradients across them as a temperature load. This is selected in calculation control and setting the Nastran element type to 2nd order. 2nd order elements enable you to leverage a more Coarse mesh while approaching close accuracy to within an order of magnitude of a fine mesh 1st order solution. Minimum and maximum goals locations It is useful to have coordinates of minimum or maximum parameter points in a simulation model when exploring the design space or operational scenarios. Equally valuable are specifying coordinates for a specific objective function in a parametric study. You can now designate a point of volume or a surface goal within the Equation Expression field. Functions to locate a point of volume or surface goal are available in Equation Goal expression are written in the following ways: GoalLocationX({Goal Name}) GoalLocationY({Goal Name}) GoalLocationZ({Goal Name}) Where Goal Name is the name of minimum or maximum surface or volume goal. In the following video you can observe the set up. In this simple demonstration video below there is a lens and the aim is to leverage radiation modeling in a transient study around the source movement to look at the hotspot intensity and position created on a surface. The goal is to track the hotspot movement across the surface using a plot of position of the maximum value of radiation flux. Exploring results: Bubble chart for parametric studies You can now compare design points of resulting parametric study for multi-parameter optimization using a bubble plot. This means you can evaluate up to 4 parameters in this clear way on one chart. Simulation automation: new examples for the Simcenter FLOEFD API The new Simcenter FLOEFD API was introduced in version 2312, with Python support added in version 2406. In latest Simcenter FLOEFD 2412 release new examples and enhancements have been added. You can now find the following examples: Porous Media Equation Goal Smart PCB Export/Import Radiation Surface and sources LED and Two Resistor examples Solid Material You can also find out about controlling number of cores for a calculation, importing power maps, setting subdomains and a whole list of other enhancements. Information on these scripting commands and enhancements is located in the help reference and support center supporting document. Batch results processing for intermediate results If you are using any command line driven operations for batch analysis or server based batch analysis and you want to export intermediate graphical and Excel based results data, then please note you can do this as of Simcenter FLOEFD 2412 . This has advantages for tracking parameter fields during a simulation run on the server side and avoids processing and time overheads of copying huge data binary files frequently back locally. Introducing NX CFD Designer Siemens Digital Industries Software supports clients in the creation of a digital twin of their product and also promotes flexible open ecosystems for streamlined development by providing tools to suit the wide variety of engineering user personas and applications. In a move to further democratize CAE tool use and to foster earlier and wider use of CFD simulation in particular, NX CFD Designer has been introduced as of December 2024. NX CFD Designer is a new accessible CFD simulation tool specifically for designers working in NX that empowers earlier decision making. Designers access a set of common fundamental fluid flow and thermal simulation capabilities without leaving NX . Results are viewed on parts geometry and assemblies directly and you can explore performance of different design options. NX CFD Designer features guided simulation set up, automatic meshing, a unique solver technology from Simcenter FLOEFD , and easy results post processing and viewing options provide simulation driven insights for design improvement. NX CFD Designer is included within the NX installation kit and is accessible through NX Value Based Licensing. It is based on Simcenter FLOEFD technology so as a design requires more advanced analysis types at any point, such as transient simulation, then models are fully transferable to Simcenter FLOEFD for NX for further study. Schedule a meeting with CAEXPERTS and discover how the new version of Simcenter FLOEFD 2412 and the recently released NX CFD Designer can revolutionize your CFD and thermal simulation workflows. Take the opportunity to explore the enhancements and advanced features that drive analysis efficiency and accuracy, optimizing product development. Contact us now and take your engineering to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

3 ways to fight contamination in public buildings, transportation and production facilities Until 2020 Computational Fluid Dynamics aka. CFD for clean air was clearly not something the general public took notice of. But when the coronavirus pandemic spurred the global community into action unlike any other time in recent history, the need for clean, healthy air suddenly became more evident and a bigger matter of public attention than ever and with it CFD made it into mainstream media. And while COVID-19 seems long gone, and with it the public attention, the general need for clean purified air in public facilities, offices and transportation remains an important factor of life quality. But it’s is not only humans that need a certain air quality standard for a healthy living, likewise there are many products for whose production hygienic standards are very high and air contamination can pose a large harm to their respective industrial production facilities. In this constant challenge for healthy and uncontaminated air, computational fluid dynamics (CFD) simulation can be a crucial asset. CFD simulation software from Siemens ’ Simcenter portfolio is used by industry in various applications to ensure air gets purified and we can breathe safely and manufacture correctly performing products efficiently. 3 ways CFD simulations help to ensure clean air It goes without saying that CFD simulations cannot (and should not) be used as public health guidelines. But CFD simulations, if applied right, can help in 3 ways: Understand transport of unwanted human or non-human source exhalations, concentration and mitigation CFD simulation offers multiple approaches to model droplets and aerosols and their transport in space and time. CFD simulations can show where these particles travel to, how long they stay in air and what surfaces they impinge on. This would generally be useful in a small indoor setting with some sort of controlled airflow (rooms, cars, trains, planes, clean rooms, food production facilities, etc). Outdoors, the number of variables increases and forming any conclusive Improve/Redesign indoor environments for safety CFD simulations have long been used to understand indoor air flows and design HVAC systems and indoor environments for comfort and safety. CFD combined with design optimization can help analyze hundreds of ‘what-if’ scenarios for indoor environments. For example, you can analyze multiple venting configurations and air curtains to ensure most of the droplets and aerosols in a room are removed. Air purifiers and disinfectants can be designed and arranged to focus on areas of high droplet concentration. Design equipment to remove hazardous substances and purify air How do you sterilize and purify indoor spaces? Here, CFD simulations can help in designing purifying equipment. Today companies use such CFD methodologies to predict the spread of contaminating particles, hazardous gases or even viruses in almost anything, from public buildings, like airports, offices, schools or train stations, through public transport, like in airplanes, buses or trains to industrial facilities for wafers, food or pharmaceuticals. The following examples show how Siemens customers have used CFD simulations in the fight for clean air. CFD for clean air in (public) transport For the transportation industry (planes, trains and automobiles….ships too), COVID-19 has brought the indoor spaces into greater focus. Since then continued efforts have been made to ensure clean air in any type of vehicle – especially in those that move a large number of people. Airbus – Understanding cough droplet propagation in aircraft cabin Siemens and Airbus are using Simcenter STAR-CCM+ to understand the transport of particles/droplets from a human cough in an aircraft cabin. Using CFD simulations, the team has modeled the transport of cough droplets in an aircraft cabin. The impact and effectiveness of face masks in reducing droplet transmission in an aircraft is modeled too. The joint team developed the CFD methodology that tackles three things: Simulation of cough droplets from an average human Challenges in modeling aircraft cabin environment Steps involved in understanding risk of virus transmission from cough droplets Norton Straw (now element Digital Engineering) – ventilation in trains A UK Rolling Stock Owning Company (Trains, for readers from the rest of the world) contacted Norton Straw (now element Digital Engineering ) to help minimize transmission on-board the trains. Using CFD simulation with Simcenter STAR-CCM+ , the engineers at Norton Straw analyzed the airflow in the train cabin resulting from many mitigation strategies – windows open, plastic shield between passengers, different ventilation air flow, etc. The simulation results, also presented with Simcenter STAR-CCM+ Virtual Reality (VR), helped the manufacturer assess the ventilation effectiveness of different cabin configurations UES/USAF – Evaluating biological agent transport in aircraft How do you identify bioaerosol contamination hot spots in a medical aircraft and confirm decontamination after exposure? UES, Inc. partnered with the US Air Force Research Laboratory’s 711th Human Performance Wing (HPW) to find the answer using Simcenter STAR-CCM+ . Simcenter Engineering Services helped the team to simulate a cough from an infected passenger in a C-130 Hercules aircraft with multiple passengers. The results from the CFD simulations will be used in guiding improved procedures and sampling strategies for bioaerosol detection and surface decontamination. This helps the US Air Force make critical decisions regarding transport of infectious patients. CFD for clean air in buildings Another area where CFD simulations can be of great use is immobile indoor spaces where again the airflow and ventilation can be controlled. HOLT Architects/ME Engineering – Creating safer office spaces with CFD HOLT Architects , in association with M/E Engineering , published some interesting results on their strategies for reducing airborne transmission of viruses. M/E Engineering are well known for their expertise in CFD modeling. Using Simcenter STAR-CCM+ , they have helped HOLT architects study droplet transmission in their Ithaca, NY office. This study and the redesign of the ventilation system is helping employees work safely in the office. CAD models of the actual office space were used. When COVID was at its height CFD simulations of multiple coughs with and without a face mask were analyzed with Simcenter STAR-CCM+ . The analysis considered office arrangement, furniture, air flow patterns, barriers and location of people. Even if masks in office spaces are a thing of the past again, this kind of environment-specific cough simulation can help redesign HVAC and indoor ventilation systems. The smaller the space (and indoors), the lesser the variables that control droplet transmission. What if an infected patient coughs? What if the HVAC system is changed? What if a window could be open? Where to face sterilization/disinfection devices? These and other questions, CFD projects can answer. JB&B – CFD simulations show opening windows is key to healthy schools Simcenter simulation from JB&B shows how opening a window in a classroom dilutes contaminants from an infected student Jaros, Baum & Bolles (JB&B) , an engineering consulting firm, worked closely with the New York Times to show how schools can reduce COVID-19 exposure in classrooms by opening windows. The engineers at JB&B used CFD simulations to show how contaminants from an infected student circulate in a classroom for three scenarios – windows closed, windows open and with a fan and air cleaner installed. The story in New York Times is a must-read and is a brilliant visual representation of how to generally keep infected contaminants to a minimum in a classroom setting. CFD to design sterilizing and purifying devices Excelitas Noblelight – Developing UVC air purifiers with CFD simulation Excelitas Noblelight GmbH has been developing specialty light sources since the invention of the quartz glass lamp in 1904. Light, whether ultraviolet (UV), infrared (IR)or middle wave range, is at the heart of everything they do. The company has harnessed the power of light to solve a wide range of challenges in the manufacturing and process industries. With the help of CFD they also design and manufacture consumer products like the Soluva® air purifier, for removing airborne viruses from healthcare settings, public transport and classrooms. Engineering simulation is not only used during the product development phase, but also to understand the best way to deploy products in the field. We use CFD simulation to help our customers understand their processes and where to locate our UV or IR emitters to make them most effective. Dörte Eggers, simulation engineer at Excelitas Noblelight Norton Straw (now element Digital Engineering) – Novel air sterilization device In a similar fashion, abovemnetioned Norton Straw (now element Digital Engineering ) used Simcenter STAR-CCM+ to develop a novel concept of air sterilization device. Using CFD simulations and design optimization, the company has designed a small, light and energy efficient sterilization device. The device won the Innovate UK Covid Response Grant . The easily manufacturable device is currently being produced with additive manufacturing. Treating the recirculated air in indoor environments with such an air sterilization device is a solution for rail, automotive and building applications. Velocity contours inside a plate and fin heat exchanger inside the award-winning air sterilization device. CFD simulation using Simcenter STAR-CCM+ CFD for clean air in production facilities The application of CFD for clean environment does not stop at providing healthier environments for human beings. Also production lines of products that require high hygiene and material purity standards need to be kept free from hazardous gases, particles, mist or dust. FS Dynamics establishes a CFD method to assess contamination in lithography machines At the 2024 Realize Live Conference Europe , CAE Simulation Consultant FS Dynamics presented a high fidelity CFD methodology to assess contamination in lithography machines a key production facility in the Semiconductor Industry. Their work addresses the longstanding challenge of contamination modelling in lithography machines with a moving wafer using Computational Fluid Dynamics (CFD) simulations. Traditionally, the morphing and remeshing technique had been employed for capturing wafer motion while assessing airflows and contamination routes, despite its inherent slowness due to the remeshing bottleneck. FS Dynamics developed a refined methodology that leverages the overset meshing technique, a previously overlooked approach due to assumptions of its unsuitability for contamination modelling with low contaminant species concentration. Exceeding expectations, the novel CFD approach not only proves to be suitable for contamination modelling with moving wafers but also turned out to significantly reduce the computation time, enabling faster development cycles and more agile design iterations for super-clean Lithography machines. Creaform Engineering uses CFD for contamination-free vaccine filling lines Cleanroom for vaccine manufacturing. All the features were accounted for in the CFD simulations performed with Simcenter STAR-CCM+, including the walls, furniture, HEPA filtration, HVAC system, physical barriers with gloved access (windows surrounding the production line), conveyor, filling needles, capping machine and many measurement instruments control panels, as well as the Restricted Access Barriers with the accumulation table for vials The design of cleanrooms for manufacturing vaccines and other medications is precisely the topic of one of Siemens articles: Life Sciences Special Report, a compilation covering a range of applications of CFD, from medical device design to pharmaceutical manufacturing processes. In that article, companies Creaform and Laporte collaborated to perform a highly-detailed simulation of a cleanroom for vaccine manufacturing, to gain predictive insight to complement the traditional smoke tests in the process of cleanroom commissioning. The CFD simulations were used to demonstrate the effectiveness of the aerodynamic barriers and ensured proper flow path around non-sterile components of the machines. As pointed out in the article, “Not only was the regulatory compliance of the cleanroom at stake but with the high production rate of the line (hundreds of vial fillings per minute), contamination would represent a considerable financial and time loss because it leads to the waste of vaccine doses.” Clean Air uses CFD to virtually test fume cupboards Fume cupboards are essential for laboratories that generate airborne hazardous substances during experiments, processes and scale-up. They are designed to capture and remove gases, vapors and aerosols to reduce the risk of exposure to a safe level. In the 30 years that Clean Air Limited (Clean Air) has been designing, manufacturing and installing fume cupboards, protecting people has always been its priority. One of Clean Air’s unique selling points is its commitment to lead the fume cupboard industry in environmental safety and sustainability. Sulfur hexafluoride (SF₆) is used to prove the effectiveness of a fume cupboard during testing, but it has been identified as the most damaging greenhouse gas. The equivalent of approximately three tons of carbon dioxide (CO₂) is released during type testing, and another ton is released during onsite testing. Most fume cupboards are tested with the on site test, so roughly 1t CO₂e per cupboard then 3t per ‘type’ of fume cupboard. To reduce the carbon footprint Clean Air worked with Siemens partner Maya HTT and developed a new process that replaces design testing with computational fluid dynamics (CFD) simulation, ensuring that performance and safety is guaranteed without impacting the environment. Schedule a meeting with CAEXPERTS and discover how computational fluid dynamics (CFD) can transform your projects and ensure clean and safe air quality in critical environments! Whether optimizing industrial facilities, redesigning ventilation systems or designing purification devices, our experts are ready to help you implement innovative solutions with Simcenter STAR-CCM+ . WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Simcenter STAR-CCM+ In-Cylinder Solution , an add-on to Simcenter STAR-CCM+ , offers an in-cylinder-specific workflow, which involves minimal inputs, streamlined pre-processing and automated post-processing capabilities, all built around a fully automated grid generation, which relies on a morph-map approach. Complemented by class-leading models (spray, liquid film, ignition, combustion, emissions) and embedded design exploration capabilities, it helps you realize ICE CFD simulations in a productive way, enabling you to numerically predict the next, more efficient and more powerful engine design. In one of the most interesting examples, illustrated below, ammonia and diesel jets at various injection timings and angles have been studied and, for certain conditions, insufficient or too strong interaction of the two fuel sprays, for instance misfiring, can occur. Being able to rely on CFD for an accurate prediction of the phenomenon, reduces the need for extensive testing and allows for detailed understanding on how to design an engine, in which the scenario can be avoided. Simcenter STAR-CCM+ In-Cylinder Solution While we all continuously hear about the electrification of automotive powertrains, the reality is that the internal combustion engine will not disappear anytime soon and will be a staple of powertrains for decades to come. The push to downsize the internal combustion engine and the integration into hybrid powertrain platforms present many new challenges for engine development which can only be overcome using extensive CFD simulation. The In-Cylinder Solution , add-on to Simcenter STAR-CCM+ allows you to perform accurate in-cylinder CFD simulations of engines easily. Default settings and automatically-created post-processing output aim at giving the engineer a “running start”: you don’t need to be a CFD expert to set up and carry out one of the most challenging CFD simulations around! Simple Problem Set Up The In-Cylinder Solution add-on opens up a minimal interface which shows only those inputs required for setting up an in-cylinder simulation, presenting a top-down workflow: you start at the top and work your way down through various levels. You do not have to be an expert Simcenter STAR-CCM+ user to set up and run in-cylinder simulations using the add-on, as it uses an application-specific workflow and simplified interface. However, expert users can use those in-cylinder simulations as the starting point for performing more complicated multi-physics engine simulations that exploit the full range of Simcenter STAR-CCM+ simulation capabilities. The Simcenter STAR-CCM+ In-Cylinder Solution has been specifically developed to make setup quick & easy and leave time for the analyst to spend on engineering the solution rather than setting up the problem with lots of mouse miles and button clicks. From fast setup of typical multi-hole injectors which can easily be customized for spray targeting, to quick selection of fuels, to automatic setup of common post-processing outputs like liquid and vapour penetration plots and fuel mass tracking, the add-on has been designed and developed to make the simulation setup easy and allow engineers to derive the most value out of the simulation process. The geometry shown was obtained from the DYNAMO (Dynamic Analysis Modelling and Optimisation of GDI Engines) project which has been partially funded by the Advanced Propulsion Centre, UK. You are now being offered the tool and capabilities to setup a full-, half- or sector model, to simulate both a four- and two-stroke engine configurations, all within only a couple of minutes. Automated Meshing The In-Cylinder Solution add-on employs a simulation driver to run a transient mesh motion process. You only need to create a single initial mesh, comprised of trimmed cells and prism layers to capture boundary layer flow features. The entire mesh movement is automatically taken care of by the code, which automatically morphs & maps the grid to account for the motion of the piston and valves. The tool performs quality checks on the mesh as it morphs, automatically creating a new, undistorted, mesh when necessary and mapping the simulation results onto it. The mesh is automatically refined in critical areas in line with best-practices: around the valve, the valve seat, the valve throat, up into the ports and around the gasket gap. This is performed automatically for every simulation and does not require any manual intervention by the user. On the other hand, users have complete control over the mesh setup and can add additional regions of refinement, e.g. around a spark plug, as dictated by the scope of their analyses. The employed morph-map approach has been extensively tested and is highly conservative of mass for all practical applications. Using version 2206 or newer also allows you to reuse generated & stored meshes. This effectively eliminates grid generation time in the second engine cycle onwards, particularly useful in LES studies were many cycles have to be simulated to accurately capture cycle to cycle variability. Each mesh station in the cycle is saved as a file with .CCM extension in a pre-specified output directory and the °CA in the filename acts as the mesh identifier. This approach is particularly beneficial in LES studies, where a higher number of engine cycles need to be simulated in order to accurately capture cycle-to-cycle variability. In the latest version of Simcenter STAR-CCM+ , you can also include geometry parts, which will be meshed in a static way, thereby saving the time the morpher would spend to morph and map the grid. Be it intake plenum, body of a pre-chamber or else, this functionality comes handy whenever cell vertex movement is not important. The grid can be coarser in those areas, something that previously posed a few challenges. Note you can now benefit from a streamlined specification of initial & boundary conditions, for those parts, which enables you to arrive at the same setup that would normally require up to 3x more clicks, i.e. to manually include static-meshed parts. Liquid film activation with static parts is not currently supported, but will be addressed in future releases. Cold Flow  An in-cylinder simulation is amongst the most complex CFD simulations you can perform.  The combination of high-speed flows, mesh motion requiring an extremely high level of mass conservation , and very small time scales (fractions of a crank-angle degree typically need time steps in the order of 1E-6 [s]) means a lot of work goes into the setup, and the numerics must be carefully selected to accomplish stable runs with reasonable turn-around times.  This is even before we start layering on complex physics models upon including liquid fuel injection, e.g. Lagrangian spray, droplet-wall interaction, wall fluid film, and combustion, e.g. ignition, flame propagation, emissions formation, knock. For this reason, a lot of simulation performed early in the development process is concentrated on so-called cold-flow . This involves modeling the transient process of the airflow in the cylinder, typically with the objective of maximizing the trapped air mass and examining the bulk motion – swirl and tumble – that this flow induces. Often we also look at the evolution of turbulence to better understand the potential for fuel and air mixing and, specifically in spark-ignited engines, what the turbulence levels around the spark plug are at the intended time of ignition / start of combustion. Transient Intake Port Performance Evaluation in Cold-Flow Conditions Simcenter STAR-CCM+ In-Cylinder Solution allows you to set up cold flow simulations for multi-valve engines with the automated setup of mesh motion, letting you go from raw CAD geometry to running simulation in just minutes. Either URANS or LES can be employed, depending on the exact scope of your simulation projects and the effects to be captured numerically. Charge Motion / Mixture Preparation With Simcenter STAR-CCM+ 13.04, we took a big step forward with capabilities to set up and run charge motion simulations. This builds upon our previous cold flow capabilities by including the setup of liquid fuel injection and modeling the ensuing mixing process. Charge motion simulations allow engine manufacturers to improve combustion quality, by controlling the mixing of inducted air with injected fuel by identifying and rectifying rich or lean mixture regions, especially in critical parts of the cycle, such as when the piston approaches TDC and during spark ignition. The latter is especially important in today’s direct injection designs, in which the injection of fuel directly into the cylinder greatly impacts the bulk flow and turbulence level – the insight provided by simulation is more important than ever. Another critical role, a charge motion simulation usually plays, is the assessment for potential formation of harmful emissions. Again, ideally, we want to achieve high-quality mixing of fuel and air, especially challenging in direct injection systems, in which, at high load operating points, there is fuel injection during large portions of the engine cycle. Simulation tells us not only where we have lean and rich pockets of charge, but how fast the liquid fuel is evaporating, how much is impacting on surfaces in the cylinder, and whether it’s forming films or pools on those surfaces. All of these act as indications of the magnitude of harmful emission formation which, unless somehow mitigated, will need to be “cleaned up” downstream of the engine using expensive aftertreatment devices in the exhaust line. Over the years OEMs have developed large databases for design guidelines based purely upon charge motion, using just the bulk motion inside the cylinder, metrics of fuel and air mixing quality, and levels of turbulence around the spark plug which tell them whether combustion is going to be good or not, saving them valuable engineering time, especially in the early design stages of a combustion system. A wide variety of break-up, droplet-wall impingement, as well as liquid film models provide the needed toolset for users to be successful in this kind of simulations, prior to carrying out more advanced, combustion, studies. Moreover, temperature-dependent properties applied by default in versions 2210 or newer, significantly reduces manual interaction: As far as high fidelity in simulating fuel sprays is concerned, adopting constant properties is far from sufficient. The burden on the user, however, to manually switch properties of the Lagrangian phase to temperature-dependent values, is quite high. Using Simcenter STAR-CCM+ In-Cylinder Solution , this step is fully automated by making use of data stored in a database, being shipped with the software. The benefit becomes more apparent in plots like the one on the right. Capturing mixture preparation accurately is of utmost importance in order to have the correct mixing of fuel & air before proceeding to the stages of in-cylinder combustion. Fuel mass is depicted here with constant versus temperature dependent properties. Combustion, Knock & Emissions Simcenter STAR-CCM+ In-Cylinder Solution offers combustion capabilities, e.g. ECFM-3Z and ECFM-CLEH, an advanced (ISSIM) as well as a standard ignition model, knock models (Tabulated Kinetic Ignition – TKI), complemented by emission models, such as CO, NORA NOx and soot emission models, for example Soot Sectional Method. With three releases per year, we are continuously increasing the breadth of capabilities with further high class combustion model options and sub-models to capture knock & predict emissions. The increasing interest in modeling of alternative / non-carbon fuels being the driving factor, all combustion models offered in the code, are fully compatible with any fuel of type CxHyOzNw, such as hydrogen (H₂) and ammonia (NH₃). At more extreme operating points and pressure / temperature conditions, certain assumptions that were valid previously, such as the ideal gas law, may not hold any longer. To stay on the safe side, real gas using the Redlich-Kwong model, offered in the tool, helps users to accurately predict effects the ideal gas law can’t, such as Van der Waals forces, compressibility & non-equilibrium thermodynamic effects, variable specific heat capacity etc. To generate useful combustion chamber design information without relying on a detailed model, the Specified Burn Rate (Wiebe) model can be employed, specifying the burn rate via a form factor together with the duration of combustion. The approach can be, as well, used to generate heat transfer boundary conditions for use in an engine CHT analysis. The graph shows a cylinder pressure curve from an industrial 4-stroke diesel engine. Here the application of the real gas model improves the prediction of the cylinder pressure for both a part and full load operation condition, with the peak pressure more closely matched to test data. Propagating Flame Simulated with Simcenter STAR-CCM+ In-Cylinder Solution Modelling combustion & emissions in some cases requires libraries of pre-tabulated chemistry. Instead of generating those using either DARS or third-party tools – or making use of tables for standard fuels available on Support Center – users can now take advantage of the ECFM table generators, in versions 2210 or newer: the capability to generate tables for laminar flame speed, engine knock, soot, equilibrium, the latter needed in simulations with ECFM-CLEH combustion model. This provides additional flexibility by removing reliance on external tools. Conjugate Heat Transfer (CHT) Going beyond standard simulations, with downsizing for efficiency in today’s engine designs, effective thermal management is critical. Designs reaching peak levels of thermal efficiency without exceeding thermal design limits are studied using full-engine conjugate heat transfer simulations. Simcenter STAR-CCM+ In-Cylinder Solution also provides a single user environment to simulate both the fluid and the solid side, i.e. the in-cylinder & engine CHT models. The exchange of heat transfer boundary conditions between the two models, as well as the ability to automate the workflow, are points that can be realized in a straight-forward manner: Users are offered an automated way of calculating and exporting cycle- averaged boundary heat transfer data (averaged spatial heat transfer coefficients and reference temperatures), which will, in turn, enable thermal boundary conditions to be applied in a subsequent engine CHT analysis. The workflow therefore becomes significantly more streamlined and efficient. Traditionally, the in-cylinder / CHT approach required usage of multiple CFD packages. Different file formats required and data mapping between software packages have always posed operational challenges. Now that the coupled in-cylinder/engine CHT analysis can be carried out entirely within Simcenter STAR-CCM+ , the overall process is greatly simplified and allows for automation of the combined simulation cycle through JAVA scripting. Workflow / Post-Processing The application-specific workflow of Simcenter STAR-CCM+ In-Cylinder Solution requires minimal inputs by the user, thereby decreasing the overall turnaround times. Several functionalities enable a seamless pre- and post-processing of in-cylinder simulations. Only a small subset of those is depicted here. The add-on presents users a grouped list of UI objects decreasing “mouse scrolling miles” to find the object of interest. Usage of subfolders enables categorization based upon the domain part (cylinder, ports/valves) or nature (mesh, solution, physics). The benefit of this will be even more apparent in multi-cylinder cases, planned to be incorporated into future versions. Generating / exporting plot & scene hardcopies, the capability allowing users to fully customize the filename, results in a file list already in chronological order, with °CA / degCA as a direct indicator. Hence, the list can also be used for video generation without manual conversion of image filenames. Another useful functionality, Cyclic Mode of plots, allows visualizing data in a cyclic pattern, particularly useful in in-cylinder analyses. Leveraging the feature allows users of the add-on to compare engine cycles by plotting the corresponding 2D (X-Y) curves on top of each other, highlighting differences out of the box; that is without any form of manual interaction, since the mode is by default active in all relevant plots. Finally, with another useful and recently introduced post-processing feature, visualizing integrated heat release rates, mass fraction burned (MFB) 10-50 or 90%, as well as the combustion duration, is zero clicks away, thereby taking productivity to the next level. Stop exporting heat release curves and carrying out tedious computations manually, in spreadsheets, in order to assess the performance of your engine design. All tree objects needed to evaluate those quantities are generated automatically. Automated Design Exploration Unleashing the power of the Simcenter STAR-CCM+ as a platform, with the embedded tool Design Manager, users can leverage the automation capabilities, scalability, and flexibility of the platform to easily and quickly execute design studies in order to optimize their engines for the next generation. Additionally, since the In-Cylinder Solution add-on automatically creates a parametric model, you are only a few mouse clicks away from easily sweeping multiple operating conditions to understand the bulk motion and turbulence at different speeds & loads. A swap of geometries has also been introduced allowing for easy setup of geometric design variation studies and the re-use of existing simulation setup on another geometry. Validation against experiments Both the In-Cylinder Solution and Simcenter STAR-CCM+ have been extensively validated for engine simulations, using both proprietary and public domain engine designs. One example is our validation of the University of Michigan Transparent Combustion Chamber-III (TCC-III) Optical Internal Combustion Engine, which is a 2-valve head, 4-stroke, spark-ignition engine with a pancake-shaped combustion chamber. The results demonstrate excellent correlation with global thermodynamics variables, including cylinder trapped mass, pressure, and temperature, and, compared with visualization from the experimental rig, the major features of the flow field are also well captured. Another detailed validation study has been conducted with the Research and development department of Daimler AG , proving excellent correlation between high-speed / high resolution PIV measurements and Simcenter STAR-CCM+ predictions in a state-of-the-art GDI engine configuration. Committed to the IC Engine Market Siemens Digital Industries Software is completely dedicated to the IC engine simulation market, recognizing that internal combustion engines are here to stay and that only advanced simulation can deliver the cleaner more efficient engines that society deserves. As part of Simcenter STAR-CCM+ , the In-Cylinder Solution add-on receives updates three times per year and we will continue to add features that address these simulations. At any time, a dedicated team of outstanding engine CFD experts will be there to support you, solving even the toughest problems in in-cylinder CFD. Would you like to take your engine engineering to the next level? Schedule a meeting with CAEXPERTS experts today and find out how the In-Cylinder Solution , integrated with Simcenter STAR-CCM+ , can revolutionize your in-cylinder engine CFD simulations. With a streamlined workflow, fast setup, and powerful automated post-processing capabilities, we are ready to help you numerically predict the most efficient and powerful engine designs. Don’t miss out on your innovation transfer – contact us today! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

MRA uses Siemens solution to achieve $44,000 in annual fuel savings per vessel and increase top speed by .93 knots Maritime Research Associates null Maritime Research Associates, LLC (MRA) is a naval architecture and engineering company located in Ann Arbor, Michigan, United States. MRA works for clients in the areas of basic and applied numerical hydrodynamic research and development (R&D) that span all sectors of the marine industry. MRA developed an optimized propulsion system for the Global Response Cutter (GRC) 43-meter (m) patrol boat by Westport Shipyard, using Siemens Digital Industries Software’s multi-purpose Navier Stokes solver, Simcenter™ STAR-CCM+™ software, to design the propellers, struts, and rudders. The design enabled the shipyard to achieve fuel savings, increase top speed, eliminate erosive appendage cavitation and reduce noise and vibration. The propeller and struts were built by Michigan Wheel Marine and the rudders by Westport Shipyard. The propulsion system design effort was funded jointly by MTU Detroit Diesel and Westport Shipyard. The GRC 43m is a state-of-the-art patrol vessel, which was built to comply with the American Bureau of Shipbuilding (ABS) standards for high-speed craft. The vessel, built in response to the anticipated worldwide demand for a fast response cutter, is constructed entirely from composite materials. Innovative design and manufacturing techniques have resulted in a vessel that is on-time, on-budget and as promised. Computational fluid dynamics (CFD) played a major role in the vessel’s design. CFD was used to design an optimized strut-rudder-propeller system that efficiently interacts with the GRC hull form. Figure 2: Comparison between selected experimental and numerical results for TY Offshore applications 1 and 2. Marine propulsion system design The main components of a propulsion system are the power plant, transmission, and propulsor. With an ever-increasing demand for both larger and faster vessels, optimized propeller design is integral to maximizing performance that increases efficiency. This increase in efficiency for given vessel speeds leads to lower fuel costs by minimizing power consumption. Conversely, for a given power, increased efficiency maximizes vessel speed. In addition, the demands of noise and emission control regulations require better selection and interaction of the propellers with the ship as a system. Propellers are designed for efficiency, noise and vibration control, avoiding erosive cavitation and achieving minimal environmental impact. By taking a comprehensive approach to all these areas, there is a reduced risk of poor performance. However, in today’s climate of global energy challenges, fuel savings are often the predominant consideration for ship operators. Driven by commercial pressures, modern propulsion systems design has relied less on traditional model tests and moved to a combination of computational design-by-analysis methods and systematic validation tests at both model- and full-scale. With the technological advances in computer-aided engineering (CAE) in the marine industry, more propulsion systems are being designed solely by analysis prior to experimental testing of the final model. This can potentially result in enormous time and cost savings by either reducing or eliminating expensive physical tests. The role of physical model tests is evolving into more of a validation mechanism. In addition, CAE offers a fast, economical method to analyze propeller configurations and their interactions with the entire vessel at full scale, hence avoiding the effects of model scaling. The aim of any vessel design is to produce a hull form with minimal resistance subject to seakeeping constraints, a propulsion system that operates efficiently in the wake of the vessel and a rudder that maneuvers the ship safely. However, these three components cannot be viewed in isolation, as each component also influences the performance of the others in various ways. Reynolds Average Navier Stokes (RANS) solvers can be used for open water propeller analysis, appended resistance tests and self-propulsion analysis, which includes the effects of cavitation. Figure 3: Computational mesh on the free surface and the vessel. Westport GRC propeller: design by analysis The Westport Global Response Cutter is a vessel for littoral and offshore security and patrol. The vessel has a maximum speed of 32.8 knots and a range of 1,000 nautical miles (nm) at top speed. The vessel uses two MTU 16V400 engines coupled with five-blade propellers, all proven for fast vessels with high-load factors and maximum mission availability. The propellers, struts, and rudders were all designed with a wake-adapted approach by MRA using Simcenter™ STAR-CCM+ . The design enabled GRC 43 owners to reduce predicted annual fuel consumption by 11,000 gallons, thereby achieving $44,000 per year in fuel savings per vessel, increasing top speed by 0.93 knots, reducing radiated pressure pulse amplitudes by 40 percent, eliminating strut and rudder cavitation and reducing noise and vibration. To gain full confidence in computational analysis, it is paramount to validate the numerical methodology with experimental results. Simcenter™ STAR-CCM+ has a robust computational code that has been well validated in various marine applications. MRA also has built an in-house computational methodology using Simcenter™ STAR-CCM+ after validating the performance of the code against experimental results for various problems. An example of validation is shown in figure 2. Here, the ability of Simcenter™ STAR-CCM+ to accurately predict the performance of the strut-propeller-rudder system was validated against a comprehensive model test program in the depressurized towing tank at the Maritime Research Institute of the Netherlands (MARIN). This enabled MRA to gain confidence in and validate the computational solution strategy. The MARIN program was funded by TY Offshore and MTU Detroit Diesel. The image shows a sample comparison between selected experimental and numerical results for the TY Offshore application. Due to the confidence gained from validation studies, the design of the strut, propeller and rudder system was largely performed using Simcenter™ STAR-CCM+ . The final stages of the design process consisted of 10 propeller designs and five design iterations for the struts and rudders. They were all simulated in Simcenter™ STAR-CCM+ . These combinations were analyzed in the “behind” or self-propulsion configuration. This computational model consisted of the ship hull, appendages, propellers, rudders and struts. A computational domain was built around the model to represent the fluid domains of both liquid and air, with a free surface at the junction of the two fluids. The volume of fluid (VOF) method in Simcenter™ STAR-CCM+ was used due to the presence of two immiscible fluids. The method solves for the volume fraction of each fluid in each cell. The free surface is the location where the volume fraction is between 0 and 1 for capturing the interface between the two fluids. The free surface waves are specified in Simcenter™ STAR-CCM+ using the VOF waves capability. The computational domain was discretized into cells of polyhedral and hexahedral shape and the Navier Stokes equations were solved within each cell for both fluids. The mesh near the free surface was refined sufficiently to resolve the wave height and wavelength. The self-propulsion analysis required a stationary outer domain of trimmed hexahedral cells and an inner rotating domain of polyhedral cells. The inner polyhedral domain defined the propeller geometry, allowing the propeller rotation, and had about 1.5 million (M) computational cells. The outer hexahedral domain defined the ship hull, appendages and the surrounding fluids with 1.5 to 2M cell volumes. A boundary layer mesh consisting of prismatic cells was used to capture the boundary layer of the flow near the solid surfaces. The propulsion tests were conducted by iterating different combinations of the designs until all performance requirements were met. The tests were conducted at the maximum speed of 32.5 knots. The wave amplitude around the vessel at a design point for the final geometry is shown in figure 4. Figure 5 shows the initial and final optimized geometry of the propeller, rudder, and struts. The cavitation on the components has been greatly reduced from the initial design on all components. Also seen is the final optimized V-strut geometry, adapted to the wake profile from the hull. Figure 7 shows the comparison of the initial and final designs of the rudder. The optimized design has reduced cavitation and is designed for minimal influence from the propeller wake on cavitation. Figure 4: Wave amplitude for the final geometry. Final Design The final optimized design was found to offer excellent fuel savings with an estimated reduction of 11,000 gallons of fuel with a cost savings of $44,000 per year per vessel when compared with typical commercial off-the-shelf (COTS) technology. There was also an increase of 0.93 knots at top speed, which would have required an additional 180 kilowatts (kW) per engine if a standard COTS approach had been taken. One of the main parameters used to characterize a ship performance is the quasi-propulsive coefficient (QPC), which is the ratio of the effective power to the delivered power of the engines. Performance comparisons were made with COTS propulsion hardware options at three speeds: loiter at 12 knots, transit at 22 knots and flank at 32.5 knots. The fuel cost savings per year from the new design was approximately $11,000 per 1 percent QPC, leading to a total of $44,000 fuel savings from 4 percent QPC savings. The QPC savings also led to a speed gain of 0.22 knots for loiter, 0.72 knots for transit and 0.93 knots for flank speed. The optimized design further eliminated cavitation on the strut and the rudder with wake alignment. Additional improvements included a decrease of 40 percent in radiated pressure pulse amplitudes, leading to quieter vessels. A fully optimized strut-propeller-rudder system was designed solely based on computational methods with excellent returns on cost, performance, and efficiency. The final vessel is a high-quality, cost-effective platform for demanding patrol boat service. In recent years, there has been a growing emphasis on using computational domains that mirror the towing tanks and cavitation tunnels of traditional experimental facilities for numerical development of the hull form and propulsor systems. As processor costs continue to decrease coupled with stable, validated and verified tools such as Simcenter™ STAR-CCM+ , the trend toward greater emphasis on numerical development of marine systems will continue to expand. Figure 5: Comparison of original and final designs, showing cavitation on the components. Figure 6: Comparison of the initial and final design of the wake-adapted V-strut. Schedule a meeting with CAEXPERTS now to discuss how we can apply the most advanced solutions in naval architecture and engineering to improve the performance of your marine project. Our expertise in numerical hydrodynamic R&D, together with cutting-edge tools such as Simcenter™ STAR-CCM+ , ensure fuel savings, noise and vibration reduction, and regulatory compliance. Get in touch and explore how we can take your marine operation to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br https://resources.sw.siemens.com/en-US/case-study-maritime-research-associates?bc=eyJzaXRlIjoid2ViaW5hcnMiLCJwYWdlIjoiMmlMTkJ5bklpTmdvTWhobjB6NzBoWiIsImxvY2FsZSI6ImVuLVVTIn0=

A global success story – Robustness and broad applicability of rotary positive displacement machines Rotary positive displacement machines are indispensable for various industrial processes due to their robustness and ability to handle highly viscous, abrasive or corrosive fluids and maintain constant flow rates. They can operate under varying pressure conditions, ensuring efficient and reliable operations, and are most efficient when handling lower heads at high flow rates. Screw pumps can transport very high viscosity fluids, such as crude oil, slurries or paper pulp, and are for example deployed in the chemical, petrochemical and process industry. In the area of food and beverage, dairy products, syrups, juices, and other food ingredients need to be pumped while ensuring sanitary conditions. The smooth, continuous flow predestines them also for pharmaceuticals, supporting transfer and dosing of medical compounds and solutions. Gear pumps are typically deployed in lubrication systems, for fuel injection or hydraulic applications. Screw compressors can be used in the process industry to compress various gases, e.g. pressurized air, but also hydrogen or carbon dioxide. Scroll compressors are frequently applied for refrigeration and cooling units, ensuring steady and quiet operation when compressing and pumping refrigerants. Scroll or screw type expanders play an important role in the context of organic Rankine cycles, e.g. in geothermal applications, heat pumps, or waste heat recovery systems, but also for pressure reduction processes – like in natural gas distribution networks – or for compressed air energy storages. Lobe blowers are comparatively cheap, reliable devices for moving air and gases in many industrial applications, e.g. as vacuum pumps or in drying or extraction systems. The range of applications is obviously vast, and while the examples provided here are not exhaustive, they certainly offer a glimpse into the numerous possibilities and the importance and impact of these devices. Pumps under pressure – the challenges of efficient rotary machine design Energy conservation is becoming increasingly important for society and industry. National and international regulations demand higher efficiency across nearly every sector, and companies are trying to reduce their environmental footprint. It is thus worthwhile to reduce energy consumption also for industrial machinery . For rotary positive displacement machines, optimization can be especially challenging: They must handle varying conditions and feature extremely complex geometry. Characterized by complicated, 3-dimensional shapes, they are transporting fluids through the movement of rotating volume chambers. Narrow gaps between the chambers formed by the rotors or between rotors and stationary parts, such as housings, cannot be avoided and are causing leakage flow and losses, leading to efficiency decreases. Gear Pump: Discretized Geometry Improving the design can be challenging due to the difficulty in gaining insight into the flow field. After all, it is very complicated or even impossible to measure detailed fluid behavior directly in closed cavities, as measurement probes either cannot be put in place or would affect and distort the flow significantly. Model physical complexity of rotary positive displacement machines with high fidelity CFD But there is a smart way out: Computational Fluid Dynamics (CFD) simulation can overcome this issue and provide detailed insights into flow variables like velocity fields, temperatures or pressure distributions. The flow through gaps can be analyzed in detail. A lot of understanding can be gained about the liquid and gas flow and complex physical effects. Flow and fluid phenomena such as turbulence, boundary layers, compressibility, or complex, rheological material properties, cavitation, as well as the free surface of additional liquids for sealing, cooling or lubrication (where required) are crucial for the operation of rotary displacement machines. Velocity Vectors and Gap Flow As a multiphysics CFD software, Simcenter STAR-CCM+ provides the required modelling capabilities to accurately simulate these machines with all the typical relevant effects. For instance, the representation of free surface flow or radial clearance cavitation can be achieved using multi-phase flow and cavitation models. The simulation of wetted surfaces is facilitated through the utilization of wall film models. The inclusion of time integration enables the simulation of transient movement, just to mention a few capabilities. Lobe Pump CFD Results: Magnitudes of Flow Velocity Lobe Pump CFD Results: Velocity Vectors, Radial Gap The challenge of high quality CFD simulation meshes for rotary positive displacement machines And while Simcenter STAR-CCM+ offers all the high-fidelity capabilities to enable engineers to model the physical complexity of rotary positive displacement machines, this alone is not sufficient. The success of numerical simulation significantly depends on the quality of the fluid domain discretization – simulation engineers refer to this as “meshing”. The number of cells should be minimized as far as possible, to avoid excessive computation times, while the cells must not be distorted or feature extreme aspect ratios. And for rotary positive displacement machines this is a very delicate challenge: Rotary positive displacement machines obviously contain moving parts. So any CFD method must consider this movement. On top those machines are usually characterized by very small clearance gaps that need to be adequately resolved to capture the underlying physics. And discretizing ever changing small volumes is not a walk in the park. The choice of the meshing scheme is therefore crucial for accurately simulating these machines, while ensuring efficiency and process reliability. The most widely used, proven and tested meshing schemes for simulating moving or rotating geometry parts in CFD can have shortcomings when applied to rotary positive displacement machines. Overset meshes are used to discretize a computational domain with different meshes overlapping each other. To represent movement, the mesh is updated regularly during the runtime of the simulation. Another approach is to use mesh morphing and remeshing. Here, the mesh topology changes according to the moving geometry. Remeshing is triggered whenever the mesh quality drops below user-defined mesh quality criteria. While being valuable for several applications, the methods described above can lead to a high mesh count when small details – like gaps – need to be resolved, especially as they are usually based on tetrahedral or polyhedral meshes. Changing meshes will always exhibit a certain degree of mass non-conservation due to the interpolation. The meshing settings need to be controlled carefully to ensure that the model preparation and numerical simulation process are stable and robust – resulting in effort for the CFD practitioner. In terms of accuracy and simulation time, structured meshing with hexahedral elements is ideal for simulating rotary positive displacement machines. Nonetheless, building such meshes manually is not a practical alternative as it can be extremely time consuming – the mesh generation could take days or longer, depending on the user’s experience. This is where TwinMesh™ comes into play: CFX Berlin Software GmbH has developed a method to overcome all the challenges offering an automated meshing solution tailored to rotary positive displacement machines. Go faster: Automated meshing workflow with TwinMesh™ The preprocessing software TwinMesh™ by CFX Berlin Software GmbH allows to automatically mesh the time-varying flow volumes in the working chambers of rotating positive displacement machines. TwinMesh™ generates meshes for the chambers and gaps for each rotational position and performs a mesh quality check. The meshes are block-structured and hexahedral which helps to limit the total number of grid cells while at the same time sufficient resolution of boundary layers and gap clearance can be realized. In combination with a consistent mesh topology for each rotational position – i.e. node numbers and connectivity stay the same – the overall simulation time can be kept reasonably brief. Screw Compressor Mesh Lobe Pump Mesh Gerotor Pump Mesh Scroll Compressor Mesh And so with high fidelity CFD modeling capabilities and a robust high quality meshing technology, all the elements you need to develop innovative rotary positive displacement machines are on the table. There is only one revolution missing: combine the two. Stay Integrated with TwinMesh™ and Simcenter STAR-CCM+ Together Siemens and CFX Berlin Software GmbH developed a seamless workflow to leverage TwinMesh™ in combination with Simcenter STAR-CCM+ . This allows you to take advantage of the benefits of modern 3D CFD for many different types of rotary positive displacement machines. High-quality meshes, with defined quality in terms of distortion and aspect ratio, are exported as a set of files together with all the required setup information, so that the simulation can be directly started in Simcenter STAR-CCM+ . This automated procedure ensures process reliability during product development and helps to accelerate the design process significantly. With 3D simulation providing detailed insights into the internal flow properties, new ways for innovative design and rotary positive displacement machines performance enhancement are opened up. Enterprises, small to medium-sized businesses as well as research institutions can achieve faster development cycles, create innovative designs, improve energy efficiency, and meet stringent industry requirements, thus gaining competitive edge in the market. With this the next revolution of rotary positive displacement machines has only just begun. Schedule a meeting with CAEXPERTS and discover how our advanced simulation and optimization solutions for positive displacement rotary machines can transform your industrial projects. Combining cutting-edge CFD technology with tools such as TwinMesh™ and Simcenter STAR-CCM+ , we help drive innovation, improve energy efficiency, and shorten development cycles. Contact us today! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

We have reached the most anticipated part of our retrospective: the In this second part, we highlight the five best posts of the year , which brought the greatest innovations, learnings and solutions shared by CAEXPERTS . Get ready to discover the best insights we shared this year! But first, it is worth remembering the first part of this post with the posts from 10th to 6th place , which can be checked out HERE! TOP 10: From tenth to sixth place 🔟 Fuel Cell Validation: Case Study Part 1 – CFD Part 2 – FEA Part 3 – Systemic Simulation and Vehicle Integration 9️⃣ Deformation modes of flexible components in mechanisms 8️⃣ Gas turbine simulations 7️⃣ E3 UFSC breaks Latin American record in Shell Eco-marathon Brazil 6️⃣ How to obtain better boundary conditions for engine models And now we come to the most anticipated moment of our TOP 10 of 2024! 5️⃣ Exploring Innovations in Simulation: Transformative Projects in the Oil and Gas Sector 🧪 In the Oil & Gas sector, computer simulation plays a vital role in optimizing operations, safety and sustainability. Tools such as Simcenter Flomaster enable critical scenarios such as pressure surges to be anticipated, while advanced models improve refining processes, predictive maintenance planning and environmental analysis. From ultra-deepwater exploration to operator training, simulation is key to innovations that increase efficiency and safety in challenging projects. 4️⃣ Simcenter FLOEFD EDA Bridge Module ⚡ Simcenter FLOEFD EDA Bridge is revolutionizing PCB (Printed Circuit Board) thermal analysis. With the ability to import detailed PCB data directly into MCAD tools like Simcenter FLOEFD , the module streamlines the thermal modeling process with accuracy and efficiency. Solutions like Smart PCB and support for formats like ODB++ and IPC2581B enable detailed simulations of components and thermal territories, optimizing everything from initial design to complete assemblies. This innovation accelerates analysis time without compromising the fidelity of results, providing an invaluable advancement for electronic design. 3️⃣ Hydrogen Liquefaction: Challenges and Solutions with Simcenter Flomaster 💧 Hydrogen liquefaction is a crucial process for enabling the storage and transportation of this promising fuel, but it faces complex challenges such as spills and critical pressure variables. With Simcenter Flomaster , engineers can simulate and optimize chemical plants, implementing strategic safety valves and controllers that reduce losses by up to 72.5% in the volume of hydrogen lost. This tool not only predicts problems, but also allows them to be controlled in real time, ensuring safe and efficient operations. 2️⃣ Why licensing SIEMENS software with CAEXPERTS is the best choice💻 Licensing SIEMENS software with CAEXPERTS  means choosing a certified technology partner capable of providing advanced consulting, customized implementation and ongoing support. With expertise in engineering solutions and computer simulation, CAEXPERTS  maximizes return on investment by integrating SIEMENS tools with your company’s specific needs, driving innovation and efficiency at every level. 1️⃣ CAEXPERTS / SIEMENS Webinar: Agitated Tank Simulation with STAR-CCM+ 🌀 The CAEXPERTS  webinar showcased how Simcenter STAR-CCM+ is revolutionizing the design and operation of agitated tanks. With integrated digital simulation, it is possible to predict and optimize processes, reduce operating costs and increase efficiency in a sustainable way. The tool offers solutions for challenges such as mixing of non-Newtonian fluids, multiphase modeling and design optimization. ✨ We end the TOP 10 of 2024 with a flourish! The five best posts of the year showed how technology and innovation are transforming engineering, with CAEXPERTS  always at the side of professionals seeking excellence and cutting-edge results. This year was marked by great achievements and shared learning. We thank you, who was with us in 2024, following our initiatives and being part of our history. 🎆 Happy New Year! May 2025 be filled with new opportunities, inspiring projects and much success for all of us! 🚀 👉 Don't miss out on the latest news! Follow our page @CAEXPERTS and keep up with exclusive content and innovative solutions to transform your projects next year! 💡 Schedule a meeting with us and find out how CAEXPERTS can bring innovation to your company in 2025! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

It's time to look back at the content that most impacted and engaged our audience this year! CAEXPERTS brought valuable insights into innovation, technology and efficiency in different sectors. In this first part, check out the highlights from 10th to 6th place in our TOP 10 of 2024 and relive the ideas and solutions that marked the year! 🔟 Fuel Cell Validation: Case Study 🔋 In the tenth position of our TOP 10, we present not just one post, but a series of 3 interconnected posts, exploring the validation of fuel cells through advanced simulation analysis. 🔹 Part 1 – CFD Opening the series, we detail multiphysics modeling and CFD (Computational Fluid Dynamics) simulation with Simcenter STAR-CCM+ . This post presents a digital reproduction of the JRC ZERO∇CELL fuel cell, validated against real-world testing, and explores how to integrate fluid flow, heat transfer, chemical and electrochemical reactions. 🔹 Part 2 – FEA In the second post, we focused on structural analysis (FEA) , using Simcenter 3D and Solid Edge . The robustness of the cell was validated considering pressure and temperature conditions imported from Simcenter STAR-CCM+ , with emphasis on the fatigue analysis and mechanical resistance of the system. 🔹 Part 3 – System Simulation and Vehicle Integration Closing the series, this post addresses systemic simulation in Simcenter Amesim , exploring the integration of fuel cells into vehicle systems. The analysis highlighted the dynamic performance, energy efficiency, and scalability of the solution in hybrid and electric vehicles. 9️⃣ Deformation modes of flexible components in mechanisms: Effects on NVH and how Simcenter 3D Motion can simulate them. ⚙️ Studying the impact of deformation of flexible components on NVH (Noise, Vibration and Harshness) has always been a challenge. With Simcenter 3D Motion and its Modal Editing functionality, engineers can now precisely adjust modal frequencies and optimize the performance of systems such as powertrains. This innovation has already demonstrated significant vibration reductions at speeds up to 4,000 rpm, simplifying processes and delivering superior results. 8️⃣ Gas Turbine Simulations Gas turbines represent the pinnacle of engineering, combining complex physics and intuitive design. Behind their intricate beauty, advancements like the HEEDS AI Simulation Predictor are transforming the design and optimization process. Recent studies have shown that integrating machine learning into simulation tools like Simcenter STAR-CCM+ and NX has been able to save up to 49% of simulation time and increase component efficiency by up to 10%. These advancements highlight how technology can reduce costs, accelerate projects, and increase market competitiveness. 7️⃣ E3 UFSC breaks Latin American record in Shell Eco-marathon Brazil 🏆 The E3 UFSC team set a historic milestone at the Shell Eco-marathon Brasil 2024 , reaching 381 km/kWh with its electric battery prototype. The achievement was supported by CAEXPERTS and Siemens technologies, which provided cutting-edge tools such as NX , Simcenter STAR-CCM+ and Simcenter 3D . With innovations such as a new transmission system and carbon fiber wheels, the team optimized its design to achieve maximum efficiency, consolidating itself as a reference in sustainable projects and pushing the limits of energy performance. 6️⃣ How to obtain better boundary conditions for engine models?✅ The accuracy of an engine model depends directly on the quality of the defined boundary conditions. Tools such as Simcenter 3D , Simcenter Amesim and Simcenter STAR-CCM+ allow you to capture critical phenomena such as heat transfer coefficients and thermal fluxes, incorporating proprietary correlations and engineering knowledge. With advanced simulations and integration of 2D and 3D models, it is possible to optimize the performance of gas turbine engines in different operating scenarios, ensuring efficiency, accuracy and longer component life. ✨ This was the first part of our TOP 10 of 2024! Keep following us to find out the 5 most remarkable posts of 2024 in the next part. Take advantage and follow CAEXPERTS on social media so you don't miss the news and insights we are already preparing for 2025. 🚀 Schedule a meeting with us and find out how CAEXPERTS can bring innovation to your company in 2025! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

The impact of geometrical changes on performance and durability Turbomachinery engineers and designers face a significant challenge when it comes to dealing with the geometrical changes that occur between unloaded and loaded operational conditions. These changes not only impact the efficiency and power of turbomachines but also affect their durability and lifespan. To accurately predict and represent the behaviour of a real engine, engineers and designers must consider these geometrical changes and (i.e. cold-to-hot transformations in turbomachinery) when designing affected components. In addition to the typical design challenges, the digitalization and integration of digital threads offer even greater potential for customizing and optimizing turbomachinery components according to customer specifications. The digital thread serves as the foundation and framework for a more integrated and connected engine development process, starting from the initial requirements and architectural specifications and continuing until the engine is in operation. Throughout each stage of development, valuable data is generated. By connecting and analyzing this data in a meaningful way, engineers can gain additional insights and identify areas for improvement. Digital thread cold-to-hot transformations plays a role in turbomachinery A practical example can be seen in the close connection between design, manufacturing, and operation. Nominal CAD model of a turbine and variations of blade scans Due to environmental conditions or other external factors, the final manufactured components may deviate from their digitally designed ideal shape. These deviations can fall within specified limits or exceed them significantly. Components that are outside or at the limit of tolerance can have unknown effects on the engine’s performance and durability. In the case of critical parts, the typical solution is to discard them. However, what about components that are on the border of tolerance? Sometimes, the impact of these deviations is only discovered during final performance tests, which is often too late and requires disassembly to replace related parts. Wouldn’t it be valuable to anticipate the possible impact in advance? Furthermore, wouldn’t it be even more beneficial to leverage these deviations and fine-tune the final assembly by selectively choosing parts to better meet the final requirements? The CAD-centric cold-to-hot transformation approach in turbomachinery In the aero engine industry, it is common practice to inspect and scan critical components, especially those subjected to rotation or thermal loads, after manufacturing and storing the collected data. It would be beneficial to leverage this data by connecting it with the design process to virtually explore real-life components that may slightly deviate from their digital counterparts. To achieve this, one can start by virtualizing the physical “as manufactured” part and comparing it to its digital “as designed” twin. By capturing measurements from a physically manufactured component through scanning or manual techniques, one can manually adjust the CAD representation of the part or utilize reverse engineering techniques, such as morphing a CAD file to match the scanned STL data set. The subsequent step involves a cold-to-hot transformation in turbomachinery, which entails transforming the unloaded “cold” condition of the component to its operational loaded (“hot”) state. This enables a digital assessment and evaluation of the performance deviation compared to the results obtained during the design phases. The Simcenter portfolio, a part of the Siemens Xcelerator business platform, provides a fast and efficient method for determining the geometric changes that occur when transitioning from an unloaded to an operational loaded condition (cold-to-hot transformation). This approach follows a CAD-centric transformation approach, ensuring that the resulting transformed CAD retains its attributes and characteristics, including naming conventions. This allows for seamless integration into existing simulation workflows, such as mechanical and aerodynamic analyses. The process begins with a cold “as manufactured” CAD representation of the component and concludes with a loaded “as operated” CAD representation. The approach follows a nonlinear methodology, which enables the consideration of nonlinear material behavior, viscoelastic deformation, and nonlinear contact conditions. Cold-to-hot transformation complexity in turbomachinery By considering such nonlinearity, it becomes possible to accurately simulate complex geometry transformations, such as shrouded blades with deformation-constraining contacts. This ensures that the generated representations closely mimic the real behaviour of a manufactured part under actual operational conditions. The CAD-centric approach allows for seamless integration of the deformed component representation into simulation data sets, including those obtained during the design phases. As a result, a comprehensive virtual exploration of the real manufactured part can be conducted. In our example, the CAD cold-to-hot transformation is performed using Siemens NX software, utilizing either the NX global deformation functionality or the additional OmniFree application. To enable CAD morphing, the deformation must be known. This is achieved through an iterative coupled numerical approach involving Simcenter STAR-CCM+ , Simcenter 3D , and Simcenter Nastran , ensuring the highest fidelity of results. Workflow demonstration with the NASA Rotor 67 Iterative simulation workflow: cold-to-hot transformation, exemplarily applied to the NASA Rotor 67 To illustrate the workflow, we can use the widely recognized NASA Rotor 67 as an example. The geometry and boundary conditions for this rotor have been obtained from open literature sources. The process begins with the transfer of a cold CAD part from Siemens NX directly to Simcenter STAR-CCM+ . In Simcenter STAR-CCM+ , operational fluid and thermal loads are applied to the CAD model. As an initial outcome, aerothermal and aerodynamic loads acting on the blade can be obtained. Aerodynamic simulation of the NASA Rotor 67 in Simcenter STAR-CCM+ to obtain aerodynamic and aerothermal loads at operating conditions These blade loads are subsequently utilized as boundary conditions for the next step in the process, which involves conducting a finite element nonlinear structural analysis using Simcenter 3D . The analysis is performed using the Nastran nonlinear solver SOL401 , allowing for the calculation of the deformation of the component. Nonlinear finite element simulation in Simcenter 3D and Simcenter Nastran to obtain operational deformation The output of the nonlinear finite element analysis yields valuable information about the deformed mesh. This information is then exported as an STL file from the Simcenter 3D post-environment and transferred to Siemens NX CAD software. In Siemens NX CAD software, the CAD transformation is performed by morphing the initial cold CAD representation to match the obtained hot mesh deformation. CAD morph in Siemens NX The result of this workflow is a deformed CAD representation, which can be further utilized as a CAD part in a CAD-centric CAE process. Through this fully embedded CAE workflow, Simcenter empowers engineers to conduct rapid and precise sensitivity and comparative studies. It also enables improvements and accelerates multi-design and optimization workflows within the design phases of turbine engine components. To illustrate this, an artificial deviation was introduced to the NASA Rotor 67 by manipulating the blade stagger angle by 2 degrees. The newly generated “as manufactured” CAD representation was then subjected to the iterative simulation approach to quickly assess the performance deviation under the same operating conditions as the originally designed design. This process does not require setting up new simulations; the only step necessary is to replace the CAD part and re-run the simulations. All settings, boundary conditions, post-processing, and workflow connectivity steps can be automated. This allows for a seamless change in geometry with just a single button push, utilizing HEEDS to orchestrate the simulation workflow. Aerodynamic performance impacted on the NASA Rotor 67 by an artificially applied manufacturing deviation The showcased workflow offers engineers, designers, and analysts a rapid and precise method to perform CAD-centric geometry cold-to-hot transformation in turbomachinery while considering realistic boundary conditions and real-life deformation characteristics. It provides a fast and accurate approach for achieving the most accurate representation of the geometry transformation process. Simcenter offers a comprehensive simulation solution portfolio specifically designed for turbomachinery applications. Do you want to maximize the performance and durability of your turbomachinery? Schedule a meeting with CAEXPERTS  and discover how customized and advanced solutions can optimize each stage of development. Get in touch right now! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

The transportation sector is today responsible for more than 20% of the CO₂ global emissions. To achieve climate neutrality, we need to reduce transport emissions by 90% by 2050. While we can see a clear trend of battery adoption for light duty vehicles, fuel cells powered by hydrogen seems to be a promising alternative for heavy duty applications. The storage of hydrogen for mobility, remains however challenging when considering the high volume taken by this ultralight gas. To reduce this volume for transport applications, hydrogen is usually compressed to a pressure level of 350 or even 700 bar. Storage in a liquid form can also be considered but it requires to cool down to a very low temperature level and this technology is currently rather used for rockets and aerospace applications. Considering gaseous storage systems, the high-pressure level requires specific tanks with solid structures but also H₂ leak-proof materials sustaining high temperature variations, especially during defueling operations. We propose in this article to present a model of a high-pressure hydrogen multi-tank system mounted on a truck tractor using Simcenter system simulation. The defueling phase is simulated, and the tank temperatures especially monitored. Description of the system The system considered consists of 5 high-pressure type IV hydrogen tanks, 3 being positioned behind the truck tractor cabin, and 2 at both sides between the front and rear axles, as illustrated on figure 1 below. Figure 1: position of the 5 H ₂ tanks considered Type IV hydrogen tanks have a non-metallic (polymer) inner liner and an outer full reinforced composite wrapping. Both characteristics enable to ensure the hydrogen tightness and to sustain high pressures. Such tank can be illustrated with figure 2: Figure 2: illustration of a type 4 hydrogen tank The characteristics of the tanks are the following: Pressure   700 barA Liner material HDPE = High Density Polyethylene – thickness 5 mm Second layer CFRP = Carbon Fiber Reinforced Plastic – thickness 35 mm Outer layer GFRP = Glass-Fiber Reinforced Plastic – thickness 20 mm Tank length   2000 mm Tank inner diameter – side   505 mm Tank inner volume – side Single tank 400.6 L Total hydrogen mass – side @15°C – 2 tanks 29.9 kg Tank inner diameter – rear   357 mm Tank inner volume – rear Single tank 200.3 L Total hydrogen mass – rear @15°C – 3 tanks 31.9 kg Table 1: H ₂ tanks main characteristics Corresponding model The system described above is modeled in Simcenter Amesim as illustrated by the following figure: Figure 3: H ₂ tank system model in Simcenter Amesim The 2 side tanks are represented in the bottom of the model, while the 3 rear ones are on the top. They are all linked together to a common volume followed by a pressure regulator set to 2.5 barA. The mass flow source at the right side enables to define refueling or defueling (respectively positive and negative hydrogen mass flows as tank boundary condition) scenarios. In this post, defueling scenarios with different constant mass flows and different initial gas temperatures will be presented. These scenarios enable to see in which cases the minimum gas temperature (usually -40 degC), that can potentially damage the tank materials (i.e. liner), is reached. The SOC component, i.e. State Of Charge, calculate during the simulation the remaining hydrogen mass out of the initial one in percentage. Main assumptions & thermal considerations Gas Equation Of State (EOS): At 700 barA and standard temperature, Hydrogen is supercritical and the compressibility factor is higher than 1.4, thus requiring to use a Real Gas Equation Of State to describe correctly its behavior. Different EOS are available in Simcenter Amesim for that purpose: Van Der Waals,  Redlich-Kwong, Redlich-Kong-Soave, Peng Robinson, MBWR and Helmholtz. The Redlich-Kong- Soave EOS (RKS) is used in this example. Thermal considerations: In addition to a good EOS, the modeling of the system thermal behavior is crucial in this example. For the inner part of the tank, free and forced convective exchanges between hydrogen and the inner liner are considered. Nusselt correlations are used to define the heat transfer coefficient. The Nusselt correlation for the free convection is a function of the Grashof and Prandtl numbers, the one for the forced convection, a function of the Reynolds number. Regarding the 3 layers of the tank material, radial conduction is considered using the thickness and the conductivity of each of them Regarding the convection from the outer skin of the tank to the ambient, a classical Nusselt correlation for forced convection around a cylinder is used with an ambient air speed of 5 m/s. Note that the initial gas tank temperature equals the ambient temperature Simulated scenarios 3 defueling scenarios are simulated and compared. The following table summarizes the conditions of these scenarios: # Ambient & H₂ initial temperatures [degC] H₂ mass flow [g/s] 1 15 -3 2 -10 -3 3 -10 -1.5 Table 2: simulated scenarios Note that for these scenarios, the simulation time stops when the SOC reaches 5% or a maximum time of 10 hours. The results of the 3 simulated scenarios are gathered on the figure below (see associated scenario color) – the gas temperature is the one at the mixing chamber linking all tanks: Figure 4: results of defueling scenarios – Gas temperature [degC] Figure 5: results of defueling scenarios – Gas Pressure [barA] Figure 6: results of defueling scenarios – State Of Charge [%] The simulation stops at: 4h 54min for scenario #1 5h 13min for scenario #2 10h 00min for scenario #3 As can be seen in the results, the critical temperature of -40 degC is reached just before 4 hours for the second scenario, starting with an initial temperature of -10 degC with a hydrogen constant flow of 3 g/s. Note that 3 g/s of hydrogen represents (using the Low Heating Value of H₂) about 360kW of constant power. Considering a Fuel Cell efficiency of about 50%, this would mean that we could not sustain a constant power demand of 180kW for more than 4 hours in conditions of scenario #2. At that stage, the SOC is about 31%. The 2 other scenarios are better, thermally speaking, since the minimum gas temperature reached is respectively -21.5 degC and -29 degC for scenarios #1 and #3. We can also have a look at the material temperatures as presented in the following picture: Figure 7: evolution of the tank materials and H ₂ temperatures for scenario #1 Figure 8: evolution of the tank materials and H ₂ temperatures for scenario #2 Figure 9: evolution of the tank materials and H ₂ temperatures for scenario #3 Note that such simulation is very quick since total CPU time for each scenario is much less than 1 second. Conclusion and perspectives This post presents a system simulation of defueling scenarios for a system of 5 high-pressure hydrogen tanks representative of a heavy-duty truck setup. Such simulation can quickly give a good assessment of the temperature and pressure evolutions within the tanks as well as for the different material layers. This could help in sizing the tanks and determining the critical scenarios leading to temperatures that could be harmful for the tank structure. Such model relies on standard thermal exchanges correlations and a recognized Real Gas Equation Of State. Defueling scenarios have been presented but the model can also be used for refueling ones. Refueling scenarios might require additional complexity like replacing the 0D volumes representing each tank by a stratified chamber, also available in Simcenter Amesim . As a valuable complement, more detailed 3D CFD analyses with Simcenter STAR-CCM+ could be leveraged in dedicated short scenarios to refine the heat exchange correlations as well as the H₂ flow pattern. Optimize your heavy-duty transportation projects with the precision of Simcenter Amesim ! Schedule a meeting with CAEXPERTS and find out how our advanced simulations can predict critical scenarios and ensure the safety and efficiency of your projects. Contact us now! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Smart Virtual Sensing is changing the way physical testing is conducted. Testing and validation are crucial steps in product design and the evolution of new concepts. Typically, sensors are instrumented at key locations and the system’s response to required load conditions is measured. This allows verification of whether static and dynamic responses are as expected or whether a system can survive under critical load conditions. Testing technology has evolved rapidly due to the demand for efficient test validation, with new types of physical sensors, higher-precision test pipelines, new integrated post-processing capabilities, XiL testing and more being regularly released… Challenges for testing and validation Test engineers often face the following challenges: How can you make a measurement in locations where physical sensors cannot be placed? Examples of such places include: Strain gauges cannot be installed in hot spots due to complex geometry Tight packing makes accelerator instrumentation in the critical section impossible  Operational loads are difficult to measure because adding an additional load transducer changes the dynamics of the structure Long instrumentation time and great effort to place a large number of sensors to cover the entire structure Conducting full vehicle testing to validate specific vehicle components is time-consuming and expensive  Damage occurs to expensive structure during testing due to unexpected overload  Many engineers have asked themselves: isn't there a new technology that can help address these challenges? Revolutionary Solutions – Simcenter 3D Smart Virtual Sensing Simcenter 3D Smart Virtual Sensing  helps you make field estimations (deformation, stress, velocity, acceleration, and displacement) and load estimations (force, moment). It provides a framework for merging FE model prediction and physical measurement data. This framework delivers accurate results with compensation for both model inaccuracy and test noise. Additionally, in addition to augmenting test results after physical testing, it can export an Executable Digital Twin (xDT) to run on a real-time platform for real-time testing. With Simcenter 3D Smart Virtual Sensing , you can: Measure inaccessible access point locations from physical sensors placed in accessible locations  Measure acceleration from remote locations to avoid crowded places.   Measure operating loads using a few strain gauges instead of adding load transducers.  Use virtual sensors to replace or enrich physical sensors, to speed up the testing campaign and optimize cost Determine the operational loads of the target components of the whole vehicle test and then reproduce the equivalent loads on a test bench.  Monitoring all field stress in real time during the testing process allows input loads to be adjusted when critical stress is reached.   Implementation How can such a challenge be addressed? Simcenter provides an integrated workflow from the Simcenter 3D Smart Virtual Sensing  application to the test environments. This is based on the exported Executable Digital Twin (xDT) that can be deployed to Model-based System Testing  (MBST) platforms , giving it the adaptive capability to solve various test problems.  The starting point of this workflow is always with Simcenter 3D Smart Virtual Sensing , where the smart virtual sensing solution is created and the input and output of the xDT are defined. Inputs are the required physical measurements, output can be virtual sensors at required locations, loads and full field status. The data fusion solver  and reduced-order FE model are embedded in the exported xDT. Off-line vs real-time Model-based System Testing Simcenter 3D Smart Virtual Sensing xDTs can be deployed in Simcenter Testlab RT (real-time MBST) and Simcenter Testlab Neo (offline MBST). Each option enables different user scenarios. You may choose to work offline because you simply want to expand your measured data sets. Since you have already completed testing and data acquisition, virtual sensing data can help you extend your physical measurements to provide you with a complete engineering view, such as operational loads and augmented virtual sensing measurements. It allows you to easily integrate the new method into existing test projects. For example, perform durability analysis on more locations with extended virtual sensors. Implementing Smart Virtual Sensing in Testlab RT provides all virtual sensor loads and estimates in real time, allowing you to monitor and interact with the testing process. For example, when testing a new part or even a prototype, damaging the part can result in high costs and extended development times. Therefore, part protection methods can be invaluable. With Simcenter Testlab RT together with Smart Virtual Sensing xDT, you gain additional insights into your part during testing. If loads exceed acceptable values, the test can be stopped, protecting the part. The off-line Model-based System Testing You can first perform testing and data acquisition, and then scale up the test results by deploying Smart Virtual Sensing xDT within Simcenter Testlab Neo. The inputs of the xDT will be corresponding test data, the outputs are virtual sensor channels and estimated loads. Deployment of off-line Model-based System Testing You can implement Simcenter 3D Smart Virtual Sensing xDT in Simcenter Testlab Neo by simply following the steps below:  Step 1: Performance testing with instrumented physical sensors and Simcenter SCADAS for data acquisition    Step 2: Deploy Smart Virtual Sensing xDT to the test environment using the dedicated Functional Mock-up Unit (FMU) method in Simcenter Testlab Neo Process Designer Step 3: Configure xDT inputs with physical measurement results Step 4: Run xDT to extend your physical measurements with more virtual sensor channels and estimated loads Step 5: Perform further test data post-processing based on the enriched test results to provide you with a complete engineering view.  The complete workflow is designed so that the exported Smart Virtual Sensing xDT can be seamlessly integrated into the test environment. It opens up many new opportunities to facilitate the testing process. For example, some strain measurements made in accessible locations can produce hot spot stresses and operational loads, which can overwhelm the usability of traditional measurement results. With the estimated loads, you can perform strength and durability analyses. Real-time Model-based System Testing  You may think that the offline approach is fine, but you need to complete the test first and then perform the test data argumentation. Therefore, you want to obtain the virtual sensing estimate in real time. With Smart Virtual Sensing xDT embedded in the real-time MBST, you can obtain the extended virtual test channels, visualize the full field stress and strain during the test, and even use this field estimate to optimize the test process. For example, as mentioned earlier, you can decrease the input loads once you reach your safety margin for the critical stress. This can help protect your expensive or irreplaceable test object. Real-time MBST can be configured by deploying Simcenter 3D Smart Virtual Sensing xDT in Simcenter Testlab RT. Simcenter Testlab RT provides the application software and real-time hardware to run Simcenter 3D Smart Virtual Sensing xDT, feed the physical inputs to the FMU input, and get the FMU defined outputs in real-time. This will provide the load estimation and field estimation at the same time as the physical measurement. The results of the virtual channels can be displayed as a graph along with the physical channels, the full field estimation results can also be passed to the visualization tool to get the full field status.  Deployment of Real-time Model-based System Testing Step 1: Instrument sensors and connect them to Testlab RT via a real-time data acquisition system Step 2: Deploy Smart Virtual Sensing xDT on Testlab RT via the web interface Step 3: Configure xDT inputs with live test channels Step 4: Perform physical tests and run Smart Virtual Sensor xDT to get real-time virtual sensing data Step 5: Stream xDT estimation results to the visualization tool for full field status monitoring Conclusion For offline and real-time MBST solutions, it is important to know where to place sensors and how many sensors are needed. Simcenter 3D  provides Optimal Sensor Placement to help with this. With Simcenter 3D Smart Virtual Sensing , even inaccessible points become measurable. This innovative solution not only overcomes the challenge of placing physical sensors in hard-to-reach areas, but also significantly reduces instrumentation time and hardware costs. By creating a Smart Virtual Sensing xDT within  Simcenter 3D , you can export and embed the xDT in Simcenter Testlab Neo for offline test result augmentation. Alternatively, you can deploy the xDT in Simcenter Testlab RT for real-time, model-based system testing. These seamless integrations enhance your testing capabilities by providing accurate data and insights that were previously unattainable. This is not just a small incremental step in your testing capabilities, but a revolution to transform your testing processes. Measure the inaccessible access point from instrument sensors placed at accessible locations Obtain operational loads in a practical way to leverage resistance analysis and durability studies Save time and instrumentation costs when testing large structures using virtual sensors Perform tests of equivalent components on a test bench reproducing operational loads Gain insights into performance and interact with the framework throughout the testing process Schedule a meeting with CAEXPERTS  and discover how Simcenter 3D Smart Virtual Sensing  can transform your testing processes. Innovative virtual sensing technology optimizes testing in hard-to-reach locations, reduces costs, and improves the accuracy of results. Let’s explore together the solutions that can boost your efficiency, reduce instrumentation time, and provide a complete and accurate view of your product’s performance. Don’t miss this opportunity to take your testing to the next level. Contact us now!   WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

HEEDS 2410 is designed to accelerate your design process with the latest enhancements in AI technology, robustness, and visualization. A new AI workflow solution is now enabled to help you re-use past designs to accelerate even more your design explorations. HEEDS AI Simulation Predictor is further enriched with extended capabilities for advanced workflows and leveraging pre-trained AI models to initiate predictions. Seamlessly integrate with Simcenter Reduced Order Modeling, experience robustness enhancements for error-handling, and explore dynamic 3D model visualization to gain new insights. Advanced Modeling HEEDS 2410 includes execution robustness improvements especially for error-handling, making it resilient to interruptions and errors. From automatic re-launching of analyses in unstable conditions to retry options that prevent unnecessary re-runs, these features enhance the overall robustness and reliability of analysis execution within HEEDS . Explore the possibilities With HEEDS 2410 , a new AI workflow provides you an integrated approach to AI from data to consumption. You can now re-use existing data, including data coming from other tools or physical experiments, prepare it for pre-training of HEEDS AI Simulation Predictor models and save training time for HEEDS AI Simulation Predictor optimizations. To enable this new AI workflow, HEEDS AI Simulation Predictor has been enhanced with the ability to supplement simulation with predictions for entire workflows, partial workflows, or individual analyses. Users can also re-use pre-trained AI models for predictive capabilities across different HEEDS studies. This capability allows engineers to leverage existing knowledge and apply it to new problems, saving valuable resources and time. With seamless integration to Simcenter Reduced Order Modeling, HEEDS 2410 enables engineers to maximize data re-use and accelerate analysis by leveraging pre-trained AI models. This feature utilizes large amounts of existing data and pre-existing AI models from either HEEDS AI Simulation Predictor or Simcenter Reduced Order Modeling, providing a wealth of insights from prior work. By combining these pre-trained models with Share Designs, SHERPA can immediately initiate new discoveries, which improves simulation efficiency and effectiveness. Additionally, HEEDS AI Simulation Predictor can now instantly generate predictions for new design variants, delivering faster, actionable insights. These advancements enable engineers to make informed design decisions more quickly, especially valuable in complex, resource-intensive engineering projects. Accelerated performance With HEEDS 2410 , speed is prioritized through advanced AI and efficient data management. Enhancements to HEEDS AI Simulation Predictor now allows for pre-trained models to be applied instantly. Whether it’s Bayesian Neural Networks, Gradient Boosting, or Random Forest algorithms, the tool can maximize data re-use by drawing on previously generated insights. Additionally, the Simcenter Reduced Order Modeling integration provides seamless data exchange, enabling designs to reach their optimal performance levels faster. Engineers can leverage AI and machine learning to drive simulations without building new models from scratch, transforming time-intensive workflows into swift processes that accelerate design space exploration. Integration with the Simcenter portfolio Dynamic 3D visualization in HEEDS 2410 brings designs to life, allowing engineers to interact with 3D model visualizations and compare scenarios in real-time. The integration with VCollab allows users to interact dynamically with geometric and simulation design results for efficient insight and discovery within HEEDS POST, making it easier to review product performance across multiple physics attributes on a single model. Additionally, users can now visualize Simcenter STAR-CCM+ scene files directly within HEEDS POST, gaining valuable insights without the need to inspect them in Simcenter STAR-CCM+ . These visualization enhancements, paired with seamless data exchange capabilities, ensure that engineers have a comprehensive, interactive design review experience. By staying integrated across platforms and tools, HEEDS 2410 provides a unified environment that helps teams make more informed decisions. Schedule a meeting with CAEXPERTS  and discover how HEEDS 2410  can revolutionize your design processes with advanced AI solutions, seamless integration, and dynamic visualization. Take advantage of this opportunity to explore possibilities, accelerate projects and make more informed decisions! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br