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Turbomachines are made of assemblies of different bladed disks An interesting exploitation of the periodicity of the structure is to consider cyclic symmetry sectors, instead of the whole 3D structure. When the industrial application requires the machine to be made of different stages, each with a different number of sectors, special care is required when connecting those sectors to ensure a smooth junction between the two stages. When the periodic structure deviates from its regular axisymmetry shape like at the propellers with few blades, a simulation in a rotating frame can no longer be avoided. The limitations on the non-rotating parts apply for such calculations: indeed, the non-rotating parts (stator and bearings) must be isotropic, which can be a rough assumption for industrial applications. For such cases, the Simcenter Nastran solver for Rotor Dynamics proposes a method of avoiding model limitations with the use of Coleman transformation. Indeed, with this method, bladed rotors assembled to an anisotropic stator and bearings can be computed! While this is a challenge to complete, it allows you to model the complexities and explore more possibilities when simulating and modeling rotating structures. For industrial applications like turbochargers, steam turbines, or jet engines, the assembly is made of multiple stages of bladed disks, and the assumption that the rotor has axisymmetry is not always true. Therefore, Simcenter 3D 2506 for Rotor Dynamics has expanded the use of the Coleman transformation to assemblies of multiple stages of cyclic symmetry rotors.ontagens de múltiplos estágios de rotores com simetria cíclica.   Coleman transformation for multiple stages of cyclic symmetry rotors The Coleman transformation is a solution for producing time invariant matrices in the fixed structure for cyclic symmetric rotors, as described by Kirchgassner 2016 . Campbell diagrams and stability analysis are then used to calculate the critical speeds at which resonance occurs. This method is equivalent to the Floquet method when the structure is strictly cyclic symmetric, as described by Skjoldan 2009 . . Simcenter 3D advanced capabilities for bladed rotors Simcenter 3D has taken a step further in the simulation of advanced bladed rotors applications, allowing an assembly to be computed satisfying the hypothesis of rotor dynamics calculations based on the axisymmetry or unsymmetry of the different parts of the system. It allows easy postprocessing including the production of Campbell diagrams and presents modes as output in a fixed reference frame for easy interpretation Top right, model preparation of different cyclic symmetry sectors connected at the junction; bottom right: the Campbell diagram output in fixed reference frame; left: complex mode; 57Hz at 600rpm, backward whirl. Complex modal analysis in 5 steps Step one In Simcenter 3D , prepare an associative model, where the different stages can be linked to the geometry. It will allow any changes in the geometry to be communicated to the finite element model and adapted in such a way that only the simulation has to be computed again, to account for the changes. This method is called the master model concept. Step two Prepare the finite element model of the cyclic symmetry sector of each stage, by identifying the sector as a portion of the structure that can be repeated about the rotor axis. It can contain a single blade or multiple blades. Step three Assemble the different stages at the junction Identify each junction between two connected stages. The solver will take care of the continuity of results at this junction by automatically adding the higher order harmonics. Step four Prepare the simulation Set up a complex modal analysis in the rotating frame and activate the Coleman transformation. The rotating parts modeled in cyclic symmetry will be calculated in multi blade coordinates for different cyclic waves (harmonic index). The Coleman transformation will compute time-invariant matrices that will allow results to be output in a fixed reference frame. The bearings and the stator can be anisotropic and are calculated in the fixed reference frame, allowing for the simulation of the whole assembly. Step five Post-process results in Simcenter 3D The Campbell diagram shows the evolution of the eigenfrequencies with the rotation speed, highlighting the gyroscopic effects for the relevant modes. The advantage of the Coleman transformation is the simultaneous consideration of multiple harmonic indices, which is usually not the case with simulations using cyclic symmetry. Modes at 0-diameter, or 1-diameter are output in our example. What else can cyclic symmetry do? For all these applications that show a periodic structure, cyclic symmetry is an interesting alternative to full 3D models, since it enables the use of model reduction and makes the simulation time more reasonable. But what else? let’s review what Simcenter 3D Rotor Dynamics can do with cyclic symmetry models: Consider hybrid models, that is, a model that consists of sections that are 1D, 2D, and/or 3D, it is now possible to model a rotor made of a cyclic symmetry sector, in one or multiple stages, with a connection to a 2D Fourier portion of the rotor, a 1D shaft, as well as a 3D portion of the structure. Bearings, springs, dampers, etc can be used to connect the rotor to the ground with stiffness and damping properties or to a casing. If you want to go further in the model reduction, you can create a super-element of the cyclic symmetry sectors, for one or multiple stages, using Component Mode Synthesis methods. This super-element can then be used in an assembly with bearings, in rotor dynamics solutions. The postprocessing enables you to recover the results for the original cyclic symmetry sectors and for the whole recombined structure. For bladed rotors that can have large deformations due to centrifugal loads or other types of solicitations, which might occur when the blades are long and thin, it is possible to compute a modal basis of the structure with a preliminary nonlinear prestress. The nonlinear prestress of the structure computes the equilibrium state due to large deformations, and the modal basis is computed around this equilibrium state. Afterwards, you can use that tangent modal basis in a modal frequency response to compute the vibrations of the system due to external loading. For Campbell diagram, stability study and complex mode calculations, this blog shows that Simcenter 3D Rotor Dynamics can now be used to solve multi-stage rotors modeled in cyclic symmetry, with anisotropic bearings and output results in a fixed reference frame. Want to know more about simulating rotors in Simcenter 3D ? Schedule a meeting with CAEXPERTS and see how to apply these cutting-edge technologies to gain performance, reduce simulation time and deal with real geometries and conditions much more efficiently. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Accelerate surface wrapping. Refine SPH accuracy. Assess transient passenger thermal comfort. Plus, many more. New enhancements in Simcenter STAR-CCM+ are aimed at helping you: Model the complexity Explore the possibilities Go faster Stay integrated The Simcenter STAR-CCM+ 2510 release introduces a suite of powerful new features designed to elevate your simulation workflows. You can now model greater complexity with enhanced transient thermal comfort analysis, enabling more realistic vehicle thermal management simulations. The latest capabilities empower you to explore engineering possibilities faster and more reliably, thanks to dynamic penalty update for topology optimization. Workflow speed is dramatically improved with parallelized surface wrapping, more GPU-accelerated applications, and local particle refinement for SPH, all designed to help you achieve results in less time without sacrificing accuracy. Together, these new features enable you to drive productivity, accelerate development, and make better engineering decisions with confidence. Model the complexity Capture all aspects of HVAC systems and human comfort over time Modern electric vehicles face the challenge of maximizing energy efficiency across all driving scenarios, especially during extreme temperatures, where heating or cooling the cabin and battery can significantly reduce range. Traditionally, simulating passenger comfort during transient cabin cycles has required cumbersome co-simulation with third-party tools, which limits automation and fidelity. With the new release of Simcenter STAR-CCM+ 2510 , you can now use advanced thermal comfort models, including Fiala and Berkeley models, directly in transient analyses. This means you can simulate any unsteady cabin cycle, capturing the full dynamics of HVAC systems and human comfort over time, all within a single, seamless workflow. The solution integrates natively into existing Vehicle Thermal Management (VTM) processes, eliminating the need for external coupling and enabling full automation. As a result, you gain the ability to assess trade-offs between HVAC energy use and passenger comfort duration, leading to more informed design decisions. The primary benefit is a significant improvement in user experience, enhanced automation capabilities, and increased simulation fidelity, enabling you to deliver vehicles that are both comfortable and energy-efficient. Explore the possibilities Run up to 3x faster and stable Topology Optimization with reduced user intervention Achieving optimal performance in adjoint topology optimization is often hampered by the difficulty of selecting the right penalty strategy, which can lead to overshooting constraints, optimizer instability, and increased simulation time. Manual fine-tuning of penalty factors is tedious and detracts from the usability of the optimization tool. With Simcenter STAR-CCM+ 2510 , you benefit from a Dynamic Penalty update that automatically adjusts penalties during the optimization process, leveraging the augmented Lagrangian method for each constraint. This enhancement ensures smooth convergence without overshoots, even in constraint-heavy problems, and eliminates the need for manual adjustments. You can now focus on critical design aspects rather than penalty tuning, achieving up to three times faster and more stable optimization runs. You benefit from a streamlined workflow that accelerates innovation and improves the overall user experience. Enable quick and lean analysis of Transient 3D data Large transient simulations generate massive data sets, making it impractical to interactively analyze results or capture unexpected phenomena unless analysis bodies are pre-placed. Previously, storing and analyzing full-resolution 3D results required significant storage and memory, limiting flexibility. With the latest Simcenter STAR-CCM+ 2510 release, you can now store resampled volumes in Solution History files, drastically reducing data and memory footprint while retaining all relevant qualitative information at every time step. This allows you to interactively analyze 3D transient data, leverage results in screenplays for insightful animations, and investigate any plane in the full domain after the simulation is complete. The solution combines the benefits of Solution Histories and resampled volumes, allowing for qualitative analysis without the need for the original mesh. Perform quick, lean, and comprehensive analysis of large-scale transient simulations, supporting deeper insights and faster decision-making. Go faster Leverage faster surface mesh preparation Preparing a closed, manifold surface from complex or “dirty” CAD geometry is essential, often time-consuming, especially for large models. Even with previous parallelization efforts, runtime remained a bottleneck for many users. With Simcenter STAR-CCM+ 2510 , you can now take advantage of Phase 2 of the MPI Surface Wrapper, which brings further speedup compared to earlier versions. The process is parallelized across multiple processors, reducing wrapping time by up to ~50% compared with previous releases. The performance gain enables you to increase simulation throughput or create a more refined wrapped surface for improved mesh quality. The solution delivers consistent results regardless of processor count and can prepare complex surfaces in minutes. You can move from CAD to simulation much more quickly thanks to streamlined surface mesh preparation. Speedup rotorcraft simulation with GPU power Simulating complex rotating machinery, such as rotorcraft, is computationally intensive and often constrained by project timelines. Traditional CPU-based approaches, while accurate, can occupy a lot of resources and extend turnaround time. With the 2510 release of Simcenter STAR-CCM+ , you can now leverage GPU acceleration for Virtual Disk simulations, achieving drastically shorter runtimes while maintaining equivalent accuracy. This enables you to execute multiple simulation variants or entire design exploration studies much faster, reducing energy consumption per simulation. The solution empowers you to evaluate hundreds of geometry variants or apply finer resolutions within standard project timelines, transforming design-space exploration and providing robust insights early in the design process. By speeding up rotorcraft and rotating machinery simulations, this approach helps unlock resources and boost innovation. Enable faster and easier setup of DEM multiphase interactions Setting up complex particle flow simulations with multiple DEM phases and boundary types traditionally required repetitive and error-prone configuration of interaction pairs. This process became increasingly cumbersome as the number of phases grew. With Simcenter STAR-CCM+ 2510 , you can now use user-defined templates for contact models, applying them to multiple interaction pairs simultaneously. This enhancement increases productivity and improves the user experience by allowing multiple default interaction settings, each associated with distinct sets of interaction pairs. The solution streamlines the setup process, reduces errors, and provides greater flexibility for customizing interactions. Faster and easier setup of DEM multiphase interactions allows you to focus on simulation objectives rather than configuration details. Boost simulation accuracy and efficiency with SPH particle refinement Ensuring high accuracy in Smoothed Particle Hydrodynamics (SPH) simulations previously required refining the entire domain, leading to high runtimes. This was especially challenging for applications such as gearbox lubrication or tire water splashing, where only specific regions required high resolution. With Simcenter STAR-CCM+ 2510 , you can now enable local particle refinement for fluid particles in targeted areas of your domain. You define adaptive refinement shapes, such as blocks, cylinders, or spheres, and particles are refined only when inside these shapes, then coarsened outside. This approach delivers higher accuracy where needed, with a negligible runtime penalty compared to globally fine simulations. This enables faster turnaround times without compromising accuracy, making high-fidelity SPH simulations more practical and efficient. Stay integrated Unlock more design possibilities with expanded e-machine type support Radial flux machines (RFMs) account for more than 95% of the e-machine market, and previously, simulation workflows for these machines were complex and fragmented. The lack of a uniform file format across Simcenter EMAG and e-machines solutions made it difficult to ensure seamless workflow and numerical continuity between teams. With Simcenter STAR-CCM+ 2510 , you can now import radial flux machine designs through the SimCenter Data eXchange (SCDX) file format, which is compatible across all e-machine and EMAG Simcenter tools. This solution allows you to carry both CAD and physics data in a single file, ensuring a seamless workflow and consistent results across teams. This advancement brings more design options and a smoother simulation experience, with particular relevance to the automotive sector thanks to expanded e-machine support. These are just a few highlights in Simcenter STAR-CCM+ 2510 . These features will enable you to design better products faster than ever, turning today’s engineering complexity into a competitive advantage. Discover how Simcenter STAR-CCM+ can revolutionize your simulation workflows, accelerating innovation and increasing the accuracy of your designs. CAEXPERTS can help you explore the full potential of this new version and strategically integrate it into your engineering processes. Schedule a meeting with us and see how to transform complexity into competitive advantage. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Aging affects most things on Earth, and batteries are no exception to this phenomenon. In a very peculiar way, batteries behave like "living" beings; their particles flow between two electrodes, there are chemical reactions, and even mechanical changes, such as an effect similar to "breathing" (expansion and contraction of the electrodes due to the intercalation and deintercalation of lithium during charge/discharge cycles). They are simply in operation, which generates natural wear and tear. You Can't Stop Aging: Electric Vehicle Fleets Will Age (and so will their batteries) However, when people consider buying an electric car, the number one criterion for them is range, followed by price and charging logistics (infrastructure and charging time). Concern about battery life only appears in 6th position. Today, this ranking may not be surprising, considering that most electric vehicles are bought new and aging seems to be a rather technical topic, with a wide variety of possible evolutions in battery life depending on the use of the electric vehicle. But, as electric vehicle fleets age (and with them their batteries), the used car markets begin to grow and, suddenly, for good resale prices, the lifespan and health of the batteries will certainly increase in importance (as predicted). Similarly, recycling battery cells that have reached the end of their useful life is still a very expensive and energy-intensive activity. And therefore, longer-lasting and more sustainable battery cells are already a competitive factor for those who design and sell electric vehicles and batteries. So, the time has come for OEMs and battery manufacturers to understand battery aging and develop cell designs that provide maximum lifespan. Engineers must understand not only when, but also where aging mechanisms occur. Using simulation with aging models can significantly help accelerate the prediction of the degradation trend of a given battery. Typically, 1D level simulations are used in this case, as they allow for very fast execution and can produce years of simulated data in a few hours. Throughout this article, it will be exemplified how this can be done in a simulation environment where physical phenomena are modeled, but first let's learn a little more about the causes of battery aging. The Toxic Ingredients That Cause Battery Cell Aging So, what triggers and affects aging? Battery aging has its root causes in several factors. First, unsurprisingly, time: whether the cell is being used or remains idle, time is at work allowing some internal chemical reaction to induce some performance degradation. Second is temperature: temperature has a significant impact on the battery lifespan degradation process. Storage and use at high temperature (high range of safe temperature limits) would accelerate aging. Low temperatures are better, but combined with fast charging can be recipes for other degradation effects. This leads to the third main criterion, the current applied to the cell. Basically, referring to the type of load applied to the battery. If it is used gently with smooth and low power demands, the current applied to the cell will be smooth and slowly affect aging. However, if the battery is used more aggressively, with more frequent fast charging, particularly under low temperature conditions, the accelerated degradation mode will be activated. A deeper analysis of battery cell degradation mechanisms What happens within the battery due to these effects is a combination of several degradation mechanisms: Solid Electrolyte Interface film growth: This is the slow growth of a thin, porous layer on the surface of the active material, which consumes Lithium atoms to grow. As it grows the inventory of available lithium, used for the cell operation, decreases, reducing the cell’s capacity. Also, the thickness of the SEI film creates a barrier to the Lithium ions and electrons trying to go in and out of the active material, which increases the cells’ overall electrical resistance Lithium plating. In this case, there is formation of lithium metal film on the surface of the active material, which also consumes the lithium inventory impacting the cell capacity. Loss of active material by dissolution: Active material responsible for storing lithium is dissolved into the electrolyte due to some undesired side reaction. The loss of this active materials further decreases the cell capacity. Loss of active material by mechanical cracking . The Lithium intercalation and de-intercalation process generates at each cycle some mechanical stress. Overtime parts of the active material can break down and be separated from the main electrode. This has the effect of losing ability to store lithium and further decreases the cell capacity. The bottom-line consequences of these effects are simple, your battery’s capacity will decrease, reducing your vehicle range compared to its brand-new range. And it will be less able to sustain aggressive power demands, reaching more rapidly the lower and maximum voltage safety limits, leading to the battery’s shut down. Aging takes time – that engineers don’t have That's why battery and vehicle manufacturers dedicate time and effort to characterizing these aging phenomena. But here's a challenge: the effects of aging can only be observed after several years of operation. Therefore, as you can understand, conducting tests to capture the correct degradation behavior requires an enormous amount of time and money to test the battery over the years of operation! Of course, there are some accelerated aging testing techniques, but the first results can only be seen after at least 6 months of accelerated aging tests. But to gain a competitive advantage, engineers analyzing these aging challenges need more detail; they need to further optimize the battery cells and understand not only when, but also where the aging mechanisms occur, so that they can better address degradation problems locally. EV battery cell formation (the initial charge) is a critical manufacturing step with respect to battery cell aging risks (Image: Chroma ATE). Inspection & Identification The first stage of the process. Battery cells are inspected and identified before entering the formation cycle. Formation This is where the initial charging of the cell happens — known as the “formation” process. A critical step that defines the cell’s electrochemical properties and directly impacts its lifespan . Ambient Aging Cells rest in ambient temperature conditions. This step allows the materials inside the cell to stabilize after formation. High Temperature Aging Cells are kept at elevated temperatures to accelerate aging and detect early defects. Ensures only stable cells move forward in the production line. OCV & ACR Testing Electrical testing to measure: OCV (Open Circuit Voltage)  – voltage when the cell is not under load. ACR (Alternating Current Resistance)  – internal resistance of the cell. These tests assess the performance and quality of each cell. Sorting Cells are classified based on results from electrical and aging tests. Cells with similar performance are grouped together to form consistent battery modules or packs. And it's not only aging that needs to be studied during operation: equally relevant is the initial charging process, known as formation, which is the critical final stage of manufacturing before the cells are shipped. It forms the crucial protective layer of the Solid Electrolyte Interface and therefore has a huge impact on the subsequent lifespan of the battery. Battery aging simulation There are several approaches to leveraging simulation to predict aging and the formation process. Firstly, our Simcenter Amesim systems solution, using 1D models, can be extremely efficient in rapidly generating years of aging simulation data under various operating conditions. The main advantage here is time acceleration. Physics-based aging models in Simcenter Amesim have been available since version 2410, in addition to the existing empirical aging models. In this type of simulation, each cell is represented by blocks that describe its electrical and thermal behavior—capacity, internal resistance, and heat exchange with the environment. By connecting multiple cells in series and parallel, it is possible to predict how performance and temperature evolve over time, simulating battery aging and allowing for design adjustments before moving on to more detailed 3D analyses in Simcenter STAR-CCM+ . Second, to address the need for spatial information, Simcenter STAR-CCM+ 's 3D Cell Design solution can predict aging evolution in a 3D cell geometry with resolved electrode layers. Of course, in this case, the execution time is much longer than in 1D simulations, but the user will have access to local information about where aging occurs and can mitigate these effects by changing the design or operating conditions. Thirdly, it is possible to combine 1D and 3D simulations. The 1D simulation is used to generate the very long-term aging simulation over years of physical time. Users can then extract from this discrete point the cell's State of Health (SOH) over the aging period, for example, every year. This SOH for each year can then be a starting point for a 3D simulation, where the cell is aged only for a short period, for example, 1 month of physical time, but long enough to generate the distribution of the various aging mechanisms, such as Solid Electrolyte Interphase (SEI) growth or lithium plating, as implemented in more recent versions of Simcenter STAR-CCM+ . Obviously, the 1D and 3D aging models are coupled with thermal models to capture the thermal effect on the evolution of degradation mechanisms. Finally, 3D simulations can be used to assist in predicting the initial Solid Electrolyte Interphase (SEI) layer during the manufacturing formation process. In fact, the SEI growth model can be used in the first charge of a battery cell and predict the growth of this critical protective layer. The 3D Cell Design feature can then help the user evaluate the uniform evolution of the SEI layer growth and determine the optimal point at which the layer is sufficiently thick and the amount of lithium consumed to generate it. This will help further refine the estimate of cyclable capacity. High fidelity battery aging simulation with Simcenter STAR-CCM+ Aging through parasitic side reactions with Sub-grid Particle Surface Film model Available since the release of Simcenter STAR-CCM+ 2406 , the “Sub-grid Particle Surface Film” model in the Battery Cell Designer allows simulating the cell's response to a duty cycle in relation to two of the main degradation mechanisms: The growth of the Solid Electrolyte Interphase (SEI) film The growth of the lithium metal plating film An Active Material Particle, presented at NordBatt Conference These are both parasitic side reactions which occur during the cell operation. Lithium plating is the deposition of Li-metal on the particle surface. And SEI is the film created from the reaction between the particle and the electrolyte. Due to the side reactions, the amount of cyclable lithium reduces, you can simply track the remaining lithium in the electrolyte and the active material. This should allow to check the effect on the capacity. The film resistance area (resistivity times thickness) is also a field function which can be tracked and contributes to the overall internal cell resistance. Mechanical-induced degradation with the Sub-Grid Particle Aging model Simcenter STAR-CCM+ includes "Sub-Grid Particle Aging," which focuses on degradation effects of a mechanical nature. In this case, the loss of active material due to mechanical stresses is characterized by alternating stresses during charging and discharging, i.e., the cyclic insertion and extraction of lithium from the active material particles, which can lead to the formation of cracks in the electrodes. This can cause loss of electrical contact and reduction of usable active material, leading to an overall loss of cell capacity and an increase in internal resistance. Active material particles undergoing surface cracking and loss of active material There are two types of cracks forming, represented with two model options under the “Sub-grid Particle Aging” model: First one is the “Loss of Active Material” model. It is characterized by the cracking of particles or electrode “blocks”, leading to an electrical contact loss of active material particles, making those particles electrochemically inert and no longer participating in the electrochemical reactions. These particles represent therefore a loss in cell’s capacity The second effect is the “Surface Crack Growth” model. The cyclic insertion and extraction generate cracks within the particles themselves. Those cracks expose a new surface for the Solid Electrolyte Interface (SEI) to grow, leading to Lithium consumption and therefore an overall capacity loss and internal resistance increase. Note that this model option is compatible with the “Sub-grid Particle Surface Film” model enabling the SEI growth effects simulation. Also note that, some publications on the topic suggest, that the tortuosity should increase when the surface cracks grow. A trustworthy battery aging simulation framework The abovementioned aging models were validated against experimental measurements generated during the EU commission funded project MODALIS² , which was focusing on developing physics-based aging models for the latest generation of Li-ion battery cells. This work was performed with key industrial partners specialists in the field of batteries, such as a cell maker, cathode supplier and electrolyte supplier. All that said, thanks to high physical modeling fidelity and the unique three-dimensional implementation of the models, these aging models offer you the ability to localize areas of the cell which most impacted by all types of aging. This is in theory. So let’s look at those models in action. Simulating aging cycles in 3D This first example was presented at the NordBatt conference in 2022 by my colleague Stefan Herberich from SIEMENS . A prototype cell used in the EU-funded MODALIS² project was used, and the cell is tested over several cycles with aggressive aging conditions to locate the weak areas where degradation is most dominant. The cell considered consists of 15 electrochemical layers. The discretized cell is shown below, along with some results. In total, there are approximately 200,000 finite volume cells. In particular, the thickness direction is discretized using 10 cells per anode and cathode layer and 2 cells for the separator and current collectors. The drive cycle consists of the following steps: first, charging is performed with constant current (CC), applied at a rate of 2C. C-rates indicate the ratio between the charging current and the battery capacity—at 1C, a fully discharged battery (0% state of charge, or SOC) is fully charged in 1 hour; at 2C, the current is doubled, and charging is completed in approximately 30 minutes. If the voltage exceeds 4.2 V, the process switches to constant voltage charging mode, remaining at 4.2 V until the state of charge reaches 95%. The 4.2 V limit is reached quickly. Then, the battery remains at rest for a little over 3 minutes and is then discharged to 60% state of charge, also at a rate of 2C. After another rest period, the complete cycle is repeated ten times. Interpretation of results The study provides insights into the effects of the two aging mechanisms that occur: SEI growth and the influence of lithium plating side reactions. The images show the average thickness of the SEI layer around the particle and the equivalent average thickness of the plated lithium on a particle, respectively. The corresponding results were observed on the anode plane and in a cross-section in the direction of the cell thickness. In addition to the analysis of the SEI, this study also provides important information about LAM (Loss of Active Material), which refers to the degradation or inactivation of the electrode material that participates in the electrochemical reactions. In the plane: the thermal boundary conditions are such that the highest temperatures are observed in the center of the battery cell. At this location, the temperature dependence of multiple material parameters leads to higher SEI growth rates. LAM is pronounced near the battery tabs, where the highest rates of voltage variation are observed. In thickness: As expected, SEI and LAM growth are greater near the separator. The operating conditions are such that the lithium metal, with an initially specified homogeneous profile, is dissolved more quickly than deposited, especially near the separator. SEI during the formation step The second study will be on SEI during the initial charge, also known as formation. Using the results presented in “Andrew Weng et al. 2023 J. Electrochem. Soc. 170 090523”, Simcenter STAR-CCM+ and the “Sub-grid Surface Film” model were used to replicate this study. The article describes the formation of SEI, i.e., the accumulation of a passivation layer on the graphite anode of a battery during the first charge cycles. The film layer is formed due to a side reaction of the solvent components S, ethylene carbonate (EC) and vinyl carbonate (VC), with Li+, which produces the film components P, lithium ethylene dicarbonate (LEDC) and lithium vinyl dicarbonate, and gaseous byproducts Q. Only the first 4 hours of the formation process were simulated, which is when the rapid dynamics occur and the transition from the kinetically limited regime to the diffusion-limited reaction regime takes place. The results reasonably correspond to the reference: The results demonstrate the ability to use Simcenter STAR-CCM+ in an approach to understand the SEI formation process, but also to be able to better control it and brings the potential to reduce its overall duration, which in some cases can last up to ~20 days. Want to understand how to predict and mitigate battery aging with high accuracy and efficiency? Schedule a meeting with CAEXPERTS and discover how Simcenter Amesim and Simcenter STAR-CCM+ solutions can revolutionize the development of longer-lasting and more sustainable cells for electric vehicles. WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter SPEED Design and analyze engines and generators analytically. Industry's most used rotating machine design software. Permanent magnet and synchronous, induction, reluctance, DC with brushes, commutated with field winding and axial flux machines, among others. Simcenter SPEED Design and analyze motors and generators analytically in Simcenter SPEED, which provides access to theoretical and physical models of most major classes of electrical machines (for example, electrically excited synchronous and permanent magnet machines, induction, reluctance, DC with brushes, switched with field and axial flux winding), along with their drives. In addition, Simcenter SPEED writes a predefined set of specific parameters and maps that can be imported into Simcenter Amesim, supporting system-level simulation of the electronic machine integrated into its environment. Contact an Expert Electric machine rapid design software Simcenter SPEED features Industry's most used rotary machine design software Link with Multiphysics software Automated Design Space Exploration and Optimization Simcenter SPEED software supports engineers in virtually validating design choices through detailed analytical simulation, fast and intelligent use of 2D finite element magnetostatic analysis. It includes all the theoretical and physical models needed for rapid electrical machine design with a flexible approach and an interface with links to even more accurate and detailed analyzes and simulations such as 2D and 3D Multiphysics Finite Element/Finite Volume (FE/FV) magnetostatic or magnetotransient, thermal, mechanical or vibroacoustic. Electric machine model: set up an electric machine model quickly; Multiphysics software link : model export to finite element software ; Design Exploration: Evaluate the influence of parameters or optimize machine performance for one or more objectives; System-Level Simulation: Export model data for a system-level simulation in Simcenter Amesim. Simcenter SPEED supports the most common machine types including motor, generator and also inverters. The user can benefit from predefined templates for the following machines: Synchronous machines (PC-BDC) Induction machines (PC-IMD) Switched reluctance machines (PC-SRD) Brushed PM-DC machines (PC-DCM) Wound Field Switched Machines (PC-WFC) Axial flow machines (PC-AXM) To improve simulation accuracy, Simcenter SPEED provides links to several general purpose electromagnetic finite element solvers such as Simcenter STAR-CCM+, Simcenter MAGNET or to Simcenter SPEED's dedicated tool, SPEED FEA Solver. They make it possible to model and study the electrical machine more accurately and under specific conditions, with saturation and the occurrence of faults. In general, users can connect Simcenter SPEED with other tools needed for complete electrical machine solution using various scripts or programming languages. More specifically, automation makes use of scripting capabilities , driving Simcenter SPEED alone, or in conjunction with other programs such as STAR-CCM+. This automated workflow follows the scripting approach and uses STAR-CCM+ and its multiphysics solvers for electromagnetic, thermal (full 3D conjugated heat transfer) and mechanical stress analysis, along with Java scripts to provide and provide additional information. Vibroacoustics can also be studied by combining FE models of the stator and frame subsystem with a BE model of the surrounding free space to assess the sound quality of the electrical machine. The objective is to eliminate noise through simulation in Simcenter 3D Acoustics. What is the expected end result of this Model-Based Systems Engineering approach? Answer: a support for making the best design choice , and by “best” I mean the optimized viable choice, again through an efficient and continuous workflow. As mentioned, Simcenter SPEED delivers results almost instantly thanks to its analytical approach, which makes it very suitable for Design Space Exploration programs, supporting clients with “What if” studies and optimization runs. HEEDS is a powerful software package in the Simcenter portfolio that automates this process of exploring the design space, and Simcenter SPEED provides an integrated graphical user interface for accessing HEEDS. ⇐ Back to Tools

CAEXPERTS brings together an experienced and multidisciplinary team of CAE experts, prepared to deliver advanced engineering and computational simulation at different scales and levels of maturity. We use high-performance hardware and software resources that are scalable in the cloud. SIMULATION SPECIALISTS We are a team prepared to deliver results , innovation and competitiveness . Resquest for Quotation Areas of expertise Advanced Engineering Digital Twins Knowledge Transfer Assertive Solutions Cost Reduction R&D and Innovation Digitization of Engineering With the advancement of globalization and technological competitiveness, products and their manufacturing processes are increasingly complex , with more restricted life cycles . In response to this, vanguard companies use computer simulation to virtually test their projects, concepts, inventions, products, equipment and processes, in the most critical scenarios, seeking to always be ahead and go even further. SIEMENS Digital Industries takes this seriously and brings the broadest range of software tools for digitization and computer-aided engineering to the market . Know the Tools Discover the Disciplines Why CAEXPERTS CAE implementation As official resellers of SIEMENS Digital Industries software, we help your company build a high-performance CAE team for your engineering, combined with the ideal simulation tools in conjunction with our technical team, so that your production generates assertive results in an intelligent and fast way. We are simulation experts and know how industries can obtain a high return on their CAE investments. Engineering Services We help industries increase their competitiveness and raise their level of innovation. We work with projects and consultancy for the development of products and equipment, as well as conduct studies aimed at reducing Capital Costs and Operating Costs of industrial enterprises, owner engineering, R&D in industrial processes, integrity analyses and increased operational reliability of production assets. In addition, we are official resellers of Siemens software, which allows us to offer the best technological solutions to our customers. Conheça os nossos serviços Discover our Services Softwares ofertados Software Licensing 3D Multiphysics Simulation Simcenter 3D Star-CCM+ FloEFD Femap CAD Design Solid Edge NX 1D Systems Simulation Flomaster Amesim Electromagnetic Simulation and Design Magnet E-machine Design Speed HEEDS Optimization Learn more Why CAEXPERTS Professional Development: Program designed for engineers and professionals who want to master the use of computer simulation tools in real industry applications. Personalized: We work side by side, from the selection of relevant topics, the study of the state of the art, the scientific technical development stages, training until the completion of the project. Real Projects: The training is developed based on real industry challenges, providing applied and practical learning that prepares you for concrete challenges. Recognition: Master computer simulation in practice and become an expert valued by the industry. Discover our specialization program Areas of expertise ACOUSTICS ELECTROMAGNETIC COMPATIBILITY DESIGN OF ELECTRONICS CIRCUI S COMPUTATIONAL FLUID DYNAMICS THERMOFLUID DYNAMIC SYSTEMS WIRING AND WIRING HARNESS ELETRIC MACHINES STRUCTURAL ANALYSIS PROJECT OPTIMIZATION MATERIALS ENGINEERING ADDITIVE MANUFACTURING AUTOMATION Âncora 1 Know more Recent Posts 1 2 3 4 5 See it all Let's start Get in touch and find out why CAEXPERTS and the best solution for your company's engineering to go even further. Name Last name Email enter a message I agree to receive information and news by email To send Thank you!

Computational Fluid Dynamics; thermal studies, radiation; design exploration and optimization; Particle flow; moving meshes; multiphase flow; reactive flows; granular flow of aggregates, food particles, metal powders, capsule tablets and wheat or a turf. Computational Fluid Dynamics Computational fluid dynamics, also known as CFD ( Computational Fluid Dynamics ), is a numerical simulation technique that allows studying the behavior of fluids under different conditions. This discipline can be used from the design of a space rocket to the design of a reactor in a chemical industry. That is, computational fluid dynamics is widely used in a variety of applications, such as the aeronautical industry, chemical processes, food processing, foundry, among others. One of the main advantages of CFD is the possibility of observing, in three dimensions (3D), what happens inside industrial equipment, such as piping, heat exchangers, compressors, among others. This allows the identification of possible problems and the proposal of solutions to improve the performance of this equipment. In addition, CFD can also be used to identify critical points in systems and implement measures to mitigate these problems, thus ensuring the safety and efficiency of systems. Contact an Expert Transport Systems Heat transfer HVAC Fluid Mixtures Separation processes Combustion Particulate Systems Flow in Structures Naval systems They assist in the design and dimensioning of fluid transport systems, such as pipes, ducts, distributors, blowers, compressors and pumps. It is possible to conduct studies with fluids of different physicochemical properties, in different operational conditions, such as pressure, flow and temperature. Through fluid dynamic simulation in these systems, it is possible to identify problems such as head loss, water hammer, obstruction points and fluid segregation, as well as propose solutions to mitigate these problems and reduce operating costs. In addition, this tool can be used to obtain performance data for equipment that is not found in the literature, such as operating curves for valves and pumps. Using this computer simulation technique, it is possible to design, dimension, optimize and analyze heat transfer in industrial systems, such as heat exchangers, condensers, boilers, cooling towers, dryers and evaporators. It allows identifying problems such as hot spots, overheating zones, incrustation in heat exchangers, among others, and proposing solutions to improve energy efficiency. In addition, it is excellent for obtaining equipment performance data, such as the overall heat transfer coefficient, making it possible to integrate the fluid dynamic simulation with process simulators that need this input information. They allow engineers and designers to analyze the behavior of air and coolant in refrigeration systems, such as air conditioning, freezers, refrigerators, cold rooms, among others. In this way, it is possible to identify problems such as undersizing, overheating and propose solutions to improve energy efficiency and the useful life of the system. Fluid dynamic analysis can also be used to study air flow in ventilation systems, such as building HVAC systems, factory ventilation systems, among others. This analysis makes it possible to identify problems such as areas of low circulation and air renewal and propose solutions to improve thermal comfort and air quality. It is the best tool to analyze in detail how the mixing of fluids occurs, including heterogeneous and non-isothermal mixtures, allowing the analysis of the concentration distribution of the components and the temperature of the fluids, which is essential to guarantee safety and efficiency. of the processes. This type of analysis is very important in reaction systems, in thermal exchange systems, in mass transfer systems (such as absorption and distillation columns) among others, as it allows identifying problems such as component segregation, bubble formation, preferential paths and stagnation zones, phenomena that directly impact the efficiency of systems. Computational fluid dynamics is also a valuable tool to optimize the performance of these systems through parametric studies, Excellent for optimizing the design of separation systems, such as filters, decanters, centrifuges, cyclones, distillation columns, among others. This makes it possible to identify problems such as poor particle distribution, sediment accumulation, flow resistance, low contact area between fluids, preferential paths, among others, and propose solutions to improve efficiency and avoid equipment oversizing. In addition, these tools can also be used to simulate and optimize complex mixture separation processes, such as the separation of volatile organic compounds, mixtures of gases and liquids, among others. In the design and optimization of combustion systems, such as burners, furnaces, engines, among others, it is used to analyze in detail the behavior of the fuel fluid and air, as well as the thermal and thermochemical performance of the system. This makes it possible to identify problems such as excessive emission of pollutants, high temperature, poor heat distribution, among others, and propose solutions to improve energy efficiency and reduce environmental impact. In addition, it is possible to test several virtual prototypes and find, through parametric analysis, the best solution for the combustion process. Several mathematical models are used to study the behavior of particulate systems, such as dust, grains, droplets, among others. This tool allows evaluating the transport of mass and energy in particulate systems, such as the movement of particles in a fluidized bed, the dispersion of particles in a fluid, the sedimentation of particles in equipment, among others. In addition, computational fluid dynamics studies can be coupled with another simulation technique, such as the Discrete Element Method (DEM), making the studies even more detailed. This type of system is found in several industries, such as oil refining, cement production, food processing, metallurgy, among others. Structures such as buildings, bridges, telecommunications towers and oil platforms are subject to fluid flow. Through computer simulation of these structures, it is possible to assess how they behave under different conditions, such as strong winds, heavy rains and waves. In this way, it is possible to propose solutions to minimize the effects of these phenomena and guarantee the safety and stability of structures, preventing accidents and avoiding oversizing. In addition, this analysis can be used to evaluate the aerodynamic behavior of structures, identifying problems such as oscillations, vibrations and noise, and proposing solutions to mitigate these problems. Marine systems are complex and require thorough analysis to ensure their safety, efficiency and durability. Computational fluid dynamics (CFD) is a valuable tool in this regard, as it allows simulating and analyzing the behavior of fluids in naval systems, such as ships, submarines and floating platforms. This makes it possible to evaluate the performance of propulsion, exhaust and maneuvering systems, identifying problems and proposing solutions to improve the safety, efficiency and durability of these systems. In addition, CFD tools can also be used to evaluate the flow behavior in different meteorological conditions, such as strong winds and waves, providing solutions to minimize the effects of these phenomena. STAR-CCM+ FloEFD Simcenter STAR-CCM+ is highly respected 3D Computational Fluid Dynamics (CFD) software around the world, trusted by many established engineering companies in diverse industries. This tool is renowned for its ability to capture all of the physics that influence a product's performance over its lifetime of operation. It has advanced mathematical methods and sophisticated mathematical models, including multiphase and interface models, making it a powerful tool to explore and optimize the design of products involving highly complex phenomena. From research and development institutes to equipment and process design companies, engineers use Simcenter STAR-CCM+ as their primary tool, as it is a valuable tool for improving product design and development processes, enabling engineers to perform accurate simulations. and reliable tools that cover a wide range of engineering disciplines. In addition, design exploration and optimization tools, along with automated mesh generation, help make the design process more efficient and make better decisions. The Simcenter STAR-CCM+ integrated environment also provides a complete solution, allowing engineers to work more efficiently and faster. In addition, it allows integration with other engineering tools, such as FEA and DEM, FloEFD is 3D computational fluid dynamics (CFD) software that provides a quick and easy way to perform flow and heat transfer simulations in equipment. It integrates directly with leading design software such as SolidWorks, AutoCAD and Creo, allowing engineers to perform CFD simulations directly within the CAD environment. One of the main advantages of this tool is that it is not necessary to be an expert in computational fluid dynamics to use it, as it is designed to be easy to use and offers an intuitive interface. It is the ideal tool for those seeking solution speed, as it uses efficient solution methods in terms of processing time. Its integration with design tools and Cartesian-type meshing allows the designer to test different scenarios and design alternatives quickly and accurately, making it a valuable tool for designers who want to integrate CFD simulation with their CAD designs. ⇐ Back to Disciplines