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Dear CAEXPERTS readers, This Christmas, embark with us on a magical journey, full of innovation, overcoming and the charm of Engineering. In a year marked by unique challenges, even the iconic Santa Claus has had to rethink his approach to ensure the Christmas magic continues to shine. ✨ Modern Challenges at the North Pole With the rise of home working even among elves, Santa Claus faced a revolution in his workshop at the North Pole. Many elves were reluctant to leave the comforts of their elven homes, and who could blame them? But the good old man couldn't rely solely on remote work to ensure that all the gifts reached the good boys and girls (after all, taking care of 7 adult flying reindeer is not an easy task, especially when they insist on participating in virtual meetings! 🦌 ) . Innovation in Gift Delivery The solution came through Siemens, CAEXPERTS' technological partner. Inspired by the versatility of Simcenter Amesim, which transformed traditional reindeer into robotic reindeer using computer-aided engineering, simulating an efficient and agile delivery system. 💻 It's okay that they don't fly yet, but who knows next Christmas? 🚀  Santa Claus and his team decided to innovate in delivering gifts. 🎁 Boston Dynamics robots are getting into the Christmas spirit, pulling sleds with robotic precision. It looks like a real Christmas miracle! 🤖 Engineering: The Magic Behind Christmas Just as the elves adjusted their tools and skills to adapt to modern times, CAEXPERTS stands as a pioneer in the technological transformation of engineering, turning challenges into incredible opportunities. 💡 Our multidisciplinary team of CAE experts not only embraces the latest innovations in simulation and computer-aided engineering, but also leverages them, offering advanced, specific solutions to our clients' technological challenges. We are committed to providing a unique experience, where engineering becomes a journey full of discoveries and extraordinary solutions. 🛠️ Wishes for Innovation and a Bright Christmas This magical Christmas, we wish all CAEXPERTS readers health, happiness and the promise of a future full of innovation. May the light of new ideas continue to shine in each future project, just as it lit the path of Santa's sleigh! 🎅 Merry Christmas and a New Year full of achievements ❗ Sincerely, The CAEXPERTS Team

CAEXPERTS, a specialist in advanced engineering and computer simulation, is revolutionizing the naval industry in partnership with SIEMENS, which acquired FORAN technology. Acquisition of FORAN by Siemens: The Xcelerator Maritime Solution Siemens, a global technology leader, has acquired FORAN technology to create a comprehensive solution for the marine industry. This strategic partnership fills crucial gaps for shipyards around the world, offering everything from increased lifecycle management to more innovative design processes. FORAN is now part of Siemens Xcelerator, establishing itself as the market CAD/CAE/CAM standard for the marine industry. Complete Solution: From Conceptual Design to Production By integrating FORAN with Siemens Xcelerator, the resulting solution covers all aspects of the marine industry, from conceptual design to final production. Rafael de Gongora, Senior Director of Naval Construction at Siemens, highlights that this collaboration offers a unique and complete solution for shipyards, driving innovation and efficiency. “Siemens' Xcelerator marine solution is a winning solution for the market. It is a unique solution capable of offering the only combination required by shipyards. This offers a complete solution for our customers.” Rafael de Gongora – Senior Director of Naval Construction at Siemens Digital Industries Software “The acquisition of FORAN enhances our maritime digital twin by adding prominent capabilities to our integrated ship design and digital thread engineering solution... By adding FORAN's extensive tools to the Xcelerator portfolio and leveraging the team's industry expertise, we will be able to offer commercial and marine shipbuilding customers better control of their ship design and manufacturing process as they transition to Shipyard 4.0.” Tony Hemmelgarn – CEO and president of Siemens Digital Industries Software. No Worries for FORAN Customers Previous FORAN customers can rest assured during this transition as Siemens technology enables the migration of legacy data, ensuring continuity of design, manufacturing and data management. Siemens also offers FORAN customers the opportunity to migrate to the Xcelerator marine solution, providing access to the best software for all their shipbuilding needs. Commitment to a Sustainable Future In addition to offering a complete solution, CAEXPERTS and Siemens are aligned with the industry's sustainability ambitions. The Xcelerator marine solution facilitates the transition to greener practices, enabling rapid prototyping, waste elimination and more sustainable ship design, contributing to decarbonization and a greener future. Main Industries of the FORAN System: Merchant, Passenger, Ro-ro: FORAN has been successfully used to design ro-ros, passenger ships, bulk carriers, chemical ships, container ships, cement carriers, oil tankers etc. Military: FORAN meets the most demanding requirements of military shipbuilding, offering control over configuration, analysis of design options, interface with PLM tools, advanced form definition capabilities and criteria customization. Specific Vessels: FORAN has been successfully used to design tugs, work boats, hotel boats, trawlers, fish transport boats, oceanographic vessels, etc. Offshore: Widely used in the offshore industry, FORAN is used for floating, anchored and fixed platforms, as well as personnel transport boats, anchor ships, supply ships, rescue boats, firefighting boats and anti-pollution vessels. Project Phases with FORAN: Initial/Conceptual Project: Volume adjustment, layout definition, hull generation. Basic Project: Quick generation of 3D model, definition of the structure and positioning of equipment. Ship Detail: Detailed definition in all disciplines, with fast and flexible tools. Manufacturing and Assembly: Using the model to extract necessary information adapted to the needs of each shipyard. Operation: Continuous importance during operation for conversions, repairs, etc. FORAN – Advantages Flexible:  Highly adaptable, it offers customized solutions for shipyards of any size, with a modular structure for flexible and gradual implementation. Ideal for Modeling and Report Production:  Features full 2D drawing functionality and exclusive 3D model capabilities, with efficient management of product information in a single database. Easy to Use:  Simple graphical interface and specific focus on shipbuilding, allowing designers to learn, implement and use easily with minimal support. Reliable:  Generates accurate and reliable information for manufacturing and assembly, contributing to the reduction of material and labor costs. Efficient:  Integration of all disciplines and design phases, reducing costs and improving production quality. Automatically generates personalized purchasing models. Collaborative Engineering:  Facilitates collaboration between shipyards, engineering companies and subcontractors, eliminating risks of incompatibility and simplifying design team coordination. Open:  Simple data exchange with other technical and management systems due to the open structure of the FORAN database and system architecture with standard or customized interfaces. Technology FORAN's 3D boat design capabilities are based on specific shipbuilding technology. The system architecture combines an internal modeling and visualization kernel tuned to meet the needs of the shipping industry. The data management system is scalable, reliable and efficient. FORAN supports collaborative engineering in various environments, being able to exchange data in various formats, such as DWG, DXF, IGES, STEP, VDA, VRML and XML. It incorporates advanced features to optimize manufacturing and can be customized for any production equipment in the marine industry. In a scenario where innovation is essential, the partnership between CAEXPERTS and Siemens, with the integration of FORAN into Siemens Xcelerator and the assimilation of this technology in the market, redefines the standards of the naval industry. This collaboration offers a complete solution from conceptual design to production, addressing the specific needs of shipyards around the world. By adopting FORAN, you have access to a state-of-the-art CAD/CAM system that is flexible, adaptable and capable of boosting efficiency in all phases of naval design. From conception to operation, FORAN provides innovation, reliability and sustainability, aligning with the future demands of the industry. If you are looking to transform your approach to shipbuilding, schedule a meeting with CAEXPERTS now. Discover how advanced engineering and simulation solutions, combined with FORAN technology, can propel your company into the future. Let's shape the next chapter of shipbuilding together. Contact us and start this journey of innovation and efficiency. The future of your company starts here!

Digital transformation has evolved from a mere option to an imperative for organizations seeking to secure their competitive advantage and prosper in the market. The integration of digital technologies has become essential for streamlining operations, elevating customer experiences and optimizing overall efficiency. Counting on Siemens' technological partnership, CAEXPERTS is committed to guiding you towards resounding success on your digital transformation journey. This article presents ten essential tips to achieve this success. 1. Set Clear Goals and Define Success Metrics Before embarking on the digital transformation journey, it is crucial to define clear objectives and identify KPIs aligned with business goals and vision. Companies with well-defined objectives are 1.6 times more likely to achieve successful results. “...when companies achieve transformation success, they are more likely to have certain leaders with digital expertise. Less than a third of all respondents say their organizations have hired a chief digital officer (CDO) to support their transformations. But those who do are 1.6 times more likely than others to report a successful digital transformation.” Excerpt taken from the article about the survey “Unlocking success in digital transformations” from October 29, 2018 by McKinsey 2. Promote a Culture of Innovation Digital transformation goes beyond the adoption of technologies; It is essential to promote a culture of innovation. According to PwC, organizations with strong innovation are 3.5 times more likely to achieve significant revenue growth. 3. Invest in the Right Technologies Choosing the right technologies is crucial. Conduct a thorough assessment to identify the technologies that best align with your goals, from cloud computing to artificial intelligence and data automation. 4. Adopt Agile Methodologies Agile methodologies allow quick iterations and adaptability to changes. They promote collaboration, continuous improvement and rapid response to market dynamics. 5. Empower Employees Through Training Ensure your employees are adequately trained to use new technologies. Empower them with the knowledge and skills they need to thrive in the digital age. 6. Protect Your Digital Assets The average cost of a data breach is $4.45 million, according to the IBM Cost of Data Breach Report 2023. As your digital footprint expands, cybersecurity becomes crucial. Implement robust measures to protect your digital assets, data and intellectual property. 7. Emphasize Customer Centricity Digital transformation is not just an internal process; it also affects interaction with customers. Emphasize customer-centricity by leveraging data insights to deliver personalized experiences and tailored solutions. 8. Collaborate with Strategic Partners Strategic partnerships can accelerate the digital transformation journey. According to a study by Accenture, 76% of executives believe that partnerships are essential to driving the success of digital transformation. Collaborate with technology providers, consultants, and industry experts for valuable support. 9. Monitor Progress and Continuously Adapt Digital transformation is continuous. Monitor progress against predefined metrics and be prepared to adapt your strategies based on ongoing data and insights. 10 Communicate Transparently Effective communication is vital. Keep all stakeholders informed about progress, challenges and results. Transparency builds trust and gains support from employees, customers and partners. In conclusion, a successful digital transformation journey requires planning, a culture of innovation, and adequate resources. CAEXPERTS, as a Siemens technological partner specializing in computer simulation and advanced engineering, offers consultancy services and projects for product development, cost reduction and studies aimed at the operation of industrial enterprises . By adopting the tips presented, your organization will be able to thrive in the digital age, unlocking new opportunities for growth and success. CAEXPERTS, committed to the digitalization of engineering, works with computer simulation to virtually test projects, concepts and processes in the most critical scenarios, allowing companies to remain competitive in the market. Schedule a meeting with us right now. We are ready to help your organization reach new heights in the digital era and help your company with digital transformation.

Simcenter System Analyst is a collaborative solution that creates industry-specific applications to drive system simulation models in collaborative environments to increase model use in actual product development. Empower your project teams with a system variant performance assessment tool that requires no computer-aided engineering (CAE) experience. With this solution, design engineers can efficiently focus on evaluating and analyzing the performance of mechatronic systems while decreasing the cost of model development. CAE experts are standardizing and sharing their system simulation models and submodel libraries with project teams. Complexity is hidden in Simcenter System Analyst's simple GUI, eliminating the need to rework submodels when connecting Simcenter Amesim, Modelica, FMUs, or Simulink submodels. Address a variety of performance attributes and ensure system simulation model continuity. Simcenter System Analyst capabilities include: Define, share and track system variants: End users can easily define their system variants from the predefined variants available in the company database, modifying components as needed. Quickly create system variant management and simulation tools: CAE experts define model architectures with Simcenter Amesim, Modelica, or Simcenter System Architect, importing them into Simcenter System Analyst with associated submodel libraries. Multi-Criteria Batch Analysis Tool: End users can perform multiple runs in parallel to explore various system variants, using HEEDS software as a complementary tool to conduct the Exploration Project. Gather and deploy your enterprise knowledge: CAE experts can reuse or create Python pre/post-processing scripts to generate enterprise-specific pre/post models by importing existing validated models and submodel libraries created with Simcenter Amesim, Modelica, FMUs or Simulink. “Address a variety of performance attributes while ensuring system simulation model continuity” Electrification and control augmentation strategies expand system and architectural complexity. Original equipment manufacturers (OEMs) and suppliers need to quickly evaluate numerous variants to meet local market specifics without compromising quality and performance. Systems simulation provides the answer to these challenges. Design engineers need a fast, easy-to-use solution to perform multi-variant analysis to evaluate design options and balance the performance of mechatronic systems. Siemens Digital Industries Software offers Simcenter™ System Analyst software, a collaborative solution that creates industry-specific applications to drive systems simulation models. Running variant analysis in an easy-to-use simulation environment Simcenter System Analyst is a versatile platform designed for design engineers or program engineers. It is a multi-industry platform that can be deployed by automotive and transportation, aerospace or heavy equipment companies, integrating system simulation model architectures and libraries into a database. Easy to use even for design engineers without CAE skills, Simcenter System Analyst allows you to quickly configure simulation models. Instead of directly assembling and configuring simulation models, engineers select system architectures, components, and scenarios from the database. A multi-attribute analysis can be configured with just a few clicks, allowing design teams to evaluate hundreds of system variations in an easy-to-use environment with customized pre- and post-processing. “With Simcenter System Analyst, design engineers can focus on matching technology to end product requirements rather than creating models.” Deploying a simulation factory Simcenter System Analyst completes a broader system simulation factory scenario. To help deal with complexity, systems architects prepare model and library architectures as well as specific pre- and post-processing before deployment to the database and handoff to system analysts such as design engineers or program engineers. System architects standardize models and structure the system simulation framework for design engineers' analysis activities. Model architectures can be prepared with Simcenter Amesim software or Simcenter™ System Architect software. The Simcenter System Analyst tool-independent simulation framework supports model libraries based on Simcenter Amesim, Simulink, or any other tool that supports the Functional Mockup Unit (FMU) standard. In the context of deploying large systems simulation, Simcenter System Analyst is essential for deploying a simulation factory across your enterprise. Through easy-to-use system simulation processes, Simcenter System Analyst helps expand model use across your entire project team. For more information about Simcenter System Analyst, watch the video: As SIEMENS technology partners, we at CAEXPERT , experts in simulation and advanced engineering, are committed to offering solutions that transform your approach to engineering! Don't miss the opportunity to unify your systems and accelerate product development! Schedule a meeting now and take your technological innovation to the next level!

The newest Simcenter Systems Simulation version 2310 update is here, and it's packed with great new features to help you easily meet your daily challenges. Save time at every step of the engineering process by optimizing your workflow and managing complexity to keep pace with innovation. This post highlights some of the new features in this release, which include Simcenter Amesim , Simcenter Flomaster , and Simcenter System Analyst. Battery With the shift to electrification, the need to simulate efficient batteries, fuel cells and pipelines is critical. In Simcenter Amesim 2310, a refined and updated particle mesh method and a dedicated variable for lithium plating detection accurately estimate voltage and assess lithium plating risk in various scenarios, including fast charging at different temperatures. Electrochemical model of battery The new battery thermal management system demo suite available in Simcenter Amesim 2310 is perfect for teams designing thermal management systems. It offers a clear workflow to design your end-to-end thermal technology by leveraging advanced built-in tools and capabilities. Battery thermal management system demonstration Energy transition In version 2310, we are taking it a step further and expanding the Simcenter Amesim hydrogen injection demo by including a detailed injector and pressure regulator model for hydrogen injection systems. This model demonstrates that existing pneumatic component design libraries can be successfully used to create detailed models and gain important insights into specific behaviors of hydrogen injection systems. Hydrogen injection demonstration With the introduction of version 2310, solar engineers can perform accurate analysis on solar photovoltaic units, including energy production and revenue forecasting, thanks to the improvements introduced in the solar panel. These improvements include a simple definition of model parameters that can be found in the datasheet information. Additionally, engineers can transform photovoltaic cell units into solar panels and arrays effortlessly. Solar panel improvements Simcenter Amesim now offers a new reversible solid oxide fuel cell structure that includes reformers and a basic demonstrator. This framework is a comprehensive solution to address the challenges faced by the energy sector in terms of decarbonization. Predictive models embedded in the framework allow engineers to evaluate the performance of reversible solid oxide cells under various pressures, temperatures and species concentrations, including startup and shutdown sequences. With the new components, engineers can build Power-to-X systems with infinite possibilities. They can size components, optimize architecture, and define control strategies that take into account variability in green energy supply, costs, and CO2 emissions. Solid Oxide Fuel Cell Automotive and transport An important challenge for vehicle engineers is understanding how vehicle designs will perform in real-world scenarios, whether on or off-road. The Simcenter Amesim Track Import tool introduces a new 3D road generator; Powered by the HERE map database, it allows the generation of 3D roads from GPS data shared around the world for simulation with different driving profiles. Tracking Import Tool Simcenter Amesim 's new visual terrain model editor provides a tool for designing proving ground models for cars and off-road vehicles. This allows chassis design engineers to easily create various types of proving grounds, including obstacles of different shapes, heights and lengths. Additionally, the tool can run batch simulations to increase the value and ease of testing. Soil model visual editor The new Electric Vehicle database in Simcenter System Analyst is beneficial for CAE methodology groups who want to understand how the software works and what information it provides. The database contains several electric vehicle configurations that can be used for testing and using Simcenter System Analyst in a practical and conventional application. This means the user can simulate an existing electric vehicle with realistic parameter values, query the vehicle configuration with a single click, compare simulation results with publicly available information, and perform what-if scenarios by switching components between vehicles, changing driving scenarios. or adjusting parameter values. Electric Vehicle Database Aerospace and defense Simcenter Amesim 2310 offers turbine and burner pre-design applications that help gas turbine and engine performance engineers extract the information they need before CAD models are available. This allows engineers to evaluate design weight penalties, generate critical data, and understand engine transient behavior. These applications also provide clear views of turbine and burner layouts, making the design process more accessible and efficient. Turbine and Burner Pre-Design Applications Although the first attempts to send probes to MARS date back to the 1970s, the last decade has seen an increase in successful missions that have sent probes, rovers and even helicopters to the soil of Mars. To support what we might call a race to MARS, we have implemented in Simcenter Amesim 2310 a pre-calibrated Martian atmosphere that allows flight dynamics engineers to accurately evaluate the vehicle's flight performance on MARS. More specifically, engineers can precisely calibrate their guidance, navigation and control systems and find optimal trade-offs. User-defined environment definition Simcenter Amesim 2310 features CAD import sketch generation for tube handling filling . Engineers can now generate a ready-to-use thermofluid model directly from the CAD geometry of a fuel system. This reduces manual steps and model building time, allowing engineers to focus on analysis and explore a broader design space. The capability is aimed at the aerospace industry, but can be used in other industries such as automotive, marine and energy. CAD import sketch generation Fluid and thermal applications The new sketch animation extension in Simcenter Amesim 2310 offers direct temperature analysis, making it perfect for thermal teams sizing or integrating heat exchangers. In seconds, you can visually monitor the temperature of all constituents of your heat exchangers at all times. Sketch animation In real-life two-phase systems, it is sometimes possible for the liquid and vapor phases to flow at different speeds. This effect is not captured by a homogeneous model, but may have an impact on system performance. In Simcenter Amesim 2310 we have introduced new slip ratio correlations that offer the possibility of capturing velocity differences between the liquid and vapor phases. Using these new correlations along with state-of-the-art heat exchange modeling, engineers can achieve predictability in estimating load and pressure drops for their two-phase systems. Two-phase flow drift flow For those in the energy and process sectors, a new tube aging feature has been introduced in Simcenter Flomaster 2310 to understand solid material deposition changes in diameter, roughness and heat transfer behavior over time; helping you identify when maintenance and cleaning are needed to reduce operating costs. Pipe aging The latest Simcenter Flomaster update has improved the application of incompressible flow balancing by introducing additional components such as tubes and constant height reservoir for more options when balancing a system. Flow rate can now be maintained and a filter has been added to exclude pipes from view to help focus on critical components. A compressible flow balancing application was also introduced. These applications provide an automated workflow to balance systems more efficiently, allowing easy transition from flow balancing simulation to static or dynamic simulation. Incompressible Flow Balancing Application Platform improvements that increase productivity Making CAD software and system simulation applications more integrated is critical to an efficient digital thread, and the new model update in Simcenter Amesim 2310 provides the ability to apply design changes from the new 3D CAD part revision to a model existing system, improving this process. CAD model update The latest FMI enhancements in Simcenter Amesim 2310 provide strong and reliable startup capabilities and also offer a closer connection with machine learning applications. As a result, both experienced and novice users can now make the most of their Simcenter Amesim models with greater ease. Improvements to the IMF This new release also brings improvements to Teamcenter connectivity in Simcenter Amesim 2310 and Simcenter Flomaster 2310. This feature allows requirements management in Teamcenter to be linked to simulation models for easy verification of system requirements. Teamcenter Connectivity The new Merge with Conflicts feature in Simcenter Client for Git is ideal for users who work with branches and want to merge modifications from another branch. It provides the ability to resolve merge conflicts manually, making it easier to perform merges for most collection types with or without conflicts. Merge feature in Simcenter Client for Git Another improvement in the Simcenter Client for Git is what we call Delete collection from server . This new feature allows server administrators to delete unused collections from the server. This way, collections that are unused or that were uploaded by mistake can be removed by users with appropriate permissions. Collections can only be deleted when they are not associated with any other collection. This means that you cannot delete a collection required by other collections. Delete collection from server Learn more about Simcenter Systems simulation version 2310 Watch the video below to see the main new features of this version: At CAEXPERTS, we understand the importance of innovation and efficiency in computer simulation. In light of recent improvements to Simcenter Systems Simulator 2310, we offer specialized solutions for companies looking to virtually test their projects in critical scenarios. Our experienced team is ready to help your business make the most of new capabilities, from simulating batteries for electrification to evaluating hydrogen injection systems and fuel cell performance. If you are looking to optimize processes, reduce costs and drive innovation, schedule a meeting with us. Together, we can explore the possibilities that simulation offers to take your projects to new heights. Contact us now and discover how CAEXPERTS can be your strategic partner in the pursuit of engineering excellence. We are ready to boost your results.

Hysteresis modeling in Simcenter MAGNET™ software allows engineers and scientists to model a real-world scenario incorporating the effects of iron losses into the simulation of low-frequency electromagnetic devices. Accurately representing a ferromagnetic material by the full BH loop instead of the SV BH curve affects the local quantities, i.e., the magnetic field distributions. As a result, the device operating point and other global quantities such as input power, torque/force, etc. also change and this can be critical for multi-objective device optimization to find the best design. The incorporation of hysteresis is also a crucial step towards accurate modeling of these materials in multiphysics simulations of electromagnetic devices in the Simcenter© environment, where the magnetic properties of these materials are also affected by mechanical stresses and high temperatures. Introduction The finite element (FE) method is widely used in the commercial computer-aided design (CAD) software industry to analyze and design low-frequency electromagnetic devices such as actuators, motors, and transformers. Maxwell's equations are discretized to calculate magnetic fields in complex geometries, which would otherwise not be possible to simulate. Advanced numerical techniques have been developed to improve the accuracy of solutions for better prediction of the performance of these electromagnetic devices. However, field solutions will not be accurate if the magnetic properties of the ferromagnetic materials, from which these devices are manufactured, are not properly modeled in CAD simulations. In commercial software the magnetic properties of ferromagnetic materials are typically modeled by a single-valued nonlinear magnetization (SV) curve (known as the BH curve, an example is shown in Figure 1) for several reasons, including numerical stability, limited computational resources available and the lack of material data. Such an approximation leads to simulations without magnetic losses, which means that the overall results, for example the motor torque, do not include any magnetic (iron) losses. These are subsequently calculated in a post-processing phase, often with empirical loss formulas developed at the beginning of the 20th century. The following equation (1) represents the energy balance in this scenario. The terms Eohmic and EStoredMag in (1) represent the ohmic loss (I²R) and the magnetic energy stored in the material, respectively. It is important to note that there is no iron loss term in (1), indicating that the SV simulations do not incorporate iron loss in the field solutions. Figure 1: Single-value BH curve of 35WW300 non-oriented electrical steel. Incorporating hysteresis In reality, ferromagnetic materials do not exhibit a single-valued BH curve, but a BH loop (like the one shown in figure 2). Energy is dissipated within the material in the form of heat when the intensity of the applied magnetic field H changes. The loss resulting from this is called hysteresis loss. The inclusion of hysteresis in the FE simulation modifies the energy balance equation (1) as shown below. The term E hys in (2) represents both the hysteresis loss and the magnetic energy stored in the ferromagnetic material. For this reason, the stored magnetic energy and coenergy tab in Simcenter MAGNET is disabled for hysteresis simulations. This is demonstrated in detail in the Single Sheet Tester (SST) sample example in the next section. Figure 2: 35WW300 Non-Oriented Electrical Steel BH Loop Despite the advent of powerful computers and advanced numerical techniques, the inclusion of hysteresis in commercial software remains a rare practice. Although academic research has produced many hysteresis models, such as the Jiles-Atherton⁽¹⁾ and Preisach⁽²⁾ models, commercial FE software companies have generally not adopted them to accurately represent the magnetic behavior of ferromagnetic materials in electromagnetic simulation. modern. devices, e.g. actuators, magnetic storage and recording devices, power transformers, variable speed electric motors, etc. Now that simulation times have been reduced (as a result of faster processors), computationally expensive hysteresis models can be employed on a large scale in complex geometries of these devices. Simcenter MAGNET from Siemens Digital Industries Software is a general-purpose 2D/3D electromagnetic field simulation software used for virtual prototyping of simple to complex electromagnetic and electromechanical devices. Using Simcenter MAGNET , engineers and scientists can design motors, sensors, transformers, actuators, solenoids or any component with permanent magnets or coils, saving time and money. This article focuses on applying a new advanced feature of Simcenter MAGNET , which allows users to incorporate hysteresis into field solutions using the Jiles-Atherton (Hys) hysteresis vector model ⁽³⁾. The feature can be enabled when the simulation is solved using the Transient Solver in 2D (with and without movement). Application examples ​ In this section, we will discuss the effects of incorporating hysteresis on local magnetic fields and iron losses and global results such as currents, voltages, force/torque, and transients for a wide range of electromagnetic devices. Comparison with the conventional SV model will also be presented. 1. The Single Sheet Tester (SST) ⁽⁴⁾ The magnetic properties of steels are measured in the laboratory using steel strips (dimension: 30 mm x 250 mm x 0.35 mm) in magnetic testers, for example, a single sheet tester (SST), an Epstein structure, etc. the unique SST sample itself. The Simcenter MAGNET model of the SST sample is shown in figure 3 (a). An excitation coil surrounds the sample and the voltage on the coil can be adjusted to obtain the desired flux density B in the sample. Figure 3: Simulation model of a single strip of 35WW300 unoriented electrical steel (a) Solid view, uniform B-field calculated using single-value (SV) model (b) and hysteresis (Hys) model (c) a 15 milliseconds (peak sinusoidal excitation). The model is solved using the SV and Hys models for the non-oriented electrical steel 35WW300. B-field plots using both models are shown in Figures 3(b) and (c) at t = 15 ms. In the case of the SV model, iron losses are calculated in the post-processing stage using the empirical loss formula in Simcenter MAGNET , presented below. Where Khys , α and Keddy are the material loss coefficients that are identified using the user-supplied power loss curves. When using the Hys model, the hysteresis loss term in (3) i.e. KhysƒBᵃ is replaced by (4) which calculates the area of ​​the BH loop. The calculated coil currents corresponding to Bmax = 1.13 T in the sample using the two models are shown in figure 4 (a). A comparison of the measured and calculated (using the Hys model) BH loops is presented in figure 4 (b) to reflect the accuracy of the Hys model. A sinusoidal voltage of different amplitudes was applied to calculate the iron loss at different induction levels using the SV and Hys models, and the results are shown in Figure 5. Figure 4: (a) Coil current calculated using SV and Hys models at Bmax = 1.13 T (b) BH loops calculated and measured at Bmax = 1.13 T Figure 5: Iron losses measured and calculated using the SV and Hys models. The frequency is 50 Hz. The stored magnetic energies calculated by Simcenter MAGNET for the SST sample using the SV and Hys models are shown in figure 6. As explained previously, the hysteresis loss calculation using the Hys model also includes the stored magnetic energy, which continues to accumulate over time. over time. For this reason, the magnetic energy stored in the Simcenter MAGNET is disabled for the Hys case. However, hysteresis loss is not incorporated into field solutions when using the SV model, and the stored magnetic energy can be calculated directly from the SV curve. Figure 6: Stored magnetic energy. In the case of the Hys model, it represents the energy being dissipated as hysteresis loss that continues to increase over time. Table 1 shows the power balance using both models for a complete excitation cycle. It can be seen that the time-averaged stored magnetic energy is zero for the SV case. However, time-averaged stored magnetic energy (hysteresis loss) is part of the power balance equation. The small difference that arises in both cases is due to numerical integration error and can be ignored. Table 1 – Power balance (one excitation cycle, frequency = 50 Hz) 2. Team Problem 32⁽⁵⁾ The test bench is a three-member ferromagnetic core, as shown in figure 7 (a). The core is made of five laminations of 3.2 wt% Fe-Si, 0.48 mm thick, with conductivity σ = 1.78 MS/m and mass density δ = 7650 kg/m³. Two 90-turn windings are placed on the outer members; the DC resistance of each winding is 0.32 ohms. These windings can be connected in series or powered by two independently controlled voltage sources. Here we will only consider the case in which the two windings are excited by two independent sinusoidal sources with amplitude of 14.5 V, frequency of 10 Hz and phase differences of 90°. In this way, we will have a rotation of fields in the upper part of the central arm of the device (at point P in figure 7 (a)). The Simcenter MAGNET model of the problem is shown in figure 7 (b). The simulation was run for 125 milliseconds (for 1.25 excitation periods with 40 points per period) using the SV and Hys models. Shaded plots for B-fields calculated at t = 75 ms using both models are shown in figure 8 (a) and (b), respectively. It can be seen that for the Hys case (shown in figure 8(b)), almost no streamlines are present in the rightmost limb, and the streamlines are closing at the corners of the same limb. Arrow plots for fields B and H are shown in Figures 9 and 10, respectively, to investigate this phenomenon. It can be seen that the H field varies between 0 A/m (outer corner) to almost 100 A/m (inner corners) in the rightmost member. In the SV case shown in figures 9 (a) and 10 (a), the sign of B changes with H, that is, the SV BH curve passes through the origin (H = 0, B = 0). However, in the Hys case, the ferromagnetic material has coercivity, and the reversal of B happens when H reaches coercivity, so the field nodes have different signs from B in the same corner, that is, although H does not change sign, B changes. Figure 7: (a) Geometry of the 3-member transformer ⁽⁶⁾ (dimension in mm) (b) Simcenter MAGNET model. Figure 8: Shaded field plot B at t = 75 ms calculated using the (a) SV, and the (b) Hys models. Figure 9: B-field arrow plot at t = 75 ms calculated using the (a) SV, and the (b) Hys models. Figure 10: Arrow plot of H field at t = 75 ms calculated using the (a) SV, and (b) Hys models. The voltages and flux connections of both coils using both material models are shown in figure 11 (a) and (b), respectively. The phase difference in the Hys case is obvious due to the phase delay between fields B and H. The results for calculated and measured coil currents and magnetic flux densities at point P are shown in figure 12 (a) and (b) , respectively. The results for the first quarter of the excitations are not shown due to the initial magnetization curve. A good agreement is reached when using the Hys model, which is a good argument for its use in electromagnetic simulations. Figure 11: (a) Voltages in two coils and (b) flux connections in two coils using the SV and Hys models. Figure 12: (a) Calculated and measured coil currents, and (b) Flux densities Bx and By at point P. 3. An actuator: In this example, a load-driven electromagnetic actuator is simulated using Transient 2D with motion solver in Simcenter MAGNET . The actuator simulation model is shown in Figure 13 (a). The coil in the actuator is driven by a capacitor charged to 12 V. A spring holds the plunger against the top stop. At time t = 0, a switch closes to connect the charged capacitor to the coil. Both the body and the plunger are made of M47 – 24 Ga steel. The shaded plot for the B fields calculated at t = 26.9 ms for the SV and Hys models is shown in Figures 13 (b) and 13 (c), respectively. There's not much noticeable difference here. However, it is desired to accurately predict the position of the piston as a function of time. Figure 14 (a) illustrates the difference between the computed positions as a function of time using both models, and a lag can be observed between the SV case and the Hys case. This can be important for critical applications where precise position knowledge is desired. The coil currents calculated using both models are also shown in Figure 14(b). Figure 13: (a) Simcenter MAGNET model of an actuator. Shaded B field and arrow plot at t = 26.9 ms calculated using the (b) SV, and the (c) Hys models. Figure 14: (a) Actuator position and (b) Excitation coil current calculated using the SV and Hys models. 4. An induction machine [6] A Simcenter MAGNET simulation of a voltage-driven induction motor is presented here. Test engine nominal specifications are given in table 2. The complete Simcenter MAGNET model of the untilted motor is shown in figure 15. For simulation purposes, the quarterly model was solved for 25 power cycles (frequency = 50 Hz) using the 2D Transient solver with motion. Shaded plots for B fields calculated at t = 500 ms are shown in figure 16 for both the SV and Hys models. The difference in rotor position at 500 ms for both models can be noted. Table 2 – Induction machine specifications Figure 15: Simcenter MAGNET model 36-slot, 28-bar, 4-pole induction machine Figure 16: Shaded plot of the B field at t = 500 ms calculated using the (a) SV), and the (b) Hys models. The flow connections and currents of phase A are shown in figures 17 (a) and (b), respectively. It can be seen that there is a transient in the solution. The Hys model predicts higher overshoots in the current waveform, but the transients disappear more quickly than the SV model due to energy dissipation in the ferromagnetic material, changing the time constant of the system. This also implies that the steady state is reached earlier and hysteresis simulations can be performed for a smaller number of time steps in this case. An induction machine is a rotating transformer. Therefore, similar results can be expected in transformer simulations. Figure 17: (a) Flux linkage and (b) A-phase phase current calculated using the SV and Hys models. The speed and torque characteristics of the induction machine are shown in Figures 18 (a) and (b), respectively, and similar transient behavior is observed. There is no significant difference in the steady state values. Figure 19 presents the time-averaged power losses (hysteresis loss, eddy current loss and ohmic loss) in various parts of the machine calculated using the SV and Hys models. The hysteresis loss in the rotor is not presented here because the slip frequency, 0.5 Hz in this case, is very small, and obtaining the time-averaged hysteresis loss for a complete rotor frequency cycle in the Hys case will require many solution steps. Figure 18: (a) Speed ​​and (b) Torque calculated using the SV and Hys models. Figure 19: Power loss in different parts of the machine calculated using the SV and Hys models. 5. A Surface Mounted Permanent Magnet Fractional Slotted Internal Rotor Machine⁽⁷⁾ This example illustrates the current-driven simulation of a surface-mounted permanent magnet (SMPM), lumped winding, fractional slot synchronous machine, which is used for traction applications. Engine specifications are shown in table 3. Table 3 – SMPM machine specifications The complete Simcenter MAGNET model of the SMPM synchronous machine is shown in figure 20 and was solved in the low speed (frequency = 50 Hz) high torque region for five power cycles using the 2D Transient with motion solver. Shaded plots for the B fields calculated at t = 0 ms using the SV and Hys models are shown in Figures 21 (a) and (b), respectively. It can be seen that the stator teeth are in deep saturation (around 2 T) in the SV case, which means that the extrapolation of the SV BH curve overestimates the field values. Figure 20: Simcenter MAGNET model of a surface-mounted fractional PM slot machine with 12 slots and 10 poles. Figure 21: Shaded plot of the B field at t = 0 ms calculated using the (a) SV, and (b) Hys models. The A-phase flow connections and stresses calculated using the SV and Hys models are shown in Figures 22 (a) and (b), respectively. The flux bond in the Hys case is smaller than in the SV case, and the effects of the slots on voltage can be seen when using the Hys model. The torque calculated using both material models is shown in Figure 23. Since iron losses are incorporated into the field solution in the case of the Hys model, the resulting torque is smaller than that of the SV model. The iron losses calculated using both models are not very different and are shown in Figure 24. Figure 22: (a) Flux connection and (b) Phase A phase voltage calculated using the SV and Hys models. Figure 23: Torque calculated using the SV and Hys models. Figure 24: Power losses in different parts of machines calculated using the SV and Hys models. Timing performance The temporal performance of the Hys model is important to users. A solution that takes a lot of calculation time is generally not desirable for design engineers. Therefore, the total simulation times for solving the examples mentioned above using both the SV model and the Hys model are shown in Table 4, and their relationship is plotted in Figure 25. It is important to note that this graph provides an estimate of the temporal performance of the Hys model compared to the SV model and can vary greatly depending on the number of time steps per cycle, mesh density, polynomial order, etc. to collect the data provided in Table 4 are time steps per cycle = 100, polynomial order = 2, Newton tolerance = 1 percent. Reducing the Newton tolerance to very small values ​​increases the number of nonlinear iterations, which significantly increases simulation times. Table 4 – Relationship of simulation times for the SV and Hys models Figure 25: Temporal performance of the Hys model compared to the SV model. When exploring the application of hysteresis modeling in Simcenter MAGNET™, it became evident how incorporating this feature is crucial for more accurate and realistic simulations of electromagnetic devices. The ability to capture nuances such as iron losses at low frequencies offers a more complete view of the behavior of these systems, directly impacting device design and optimization. In this context, CAEXPERTS stands out as a strategic partner for companies seeking to improve their capabilities in computer simulation and advanced engineering. With an experienced and multidisciplinary team, CAEXPERTS is prepared to offer innovative solutions and boost the competitiveness of its customers. If your company is looking to maximize product development efficiency, reduce operational costs and gain valuable insights through advanced simulations, CAEXPERTS is the ideal partner. Our experience ranges from projects and consultancy to studies focused on reducing costs and increasing operational reliability. We see the integration of hysteresis modeling as a crucial step in the search for assertive and intelligent results. By combining CAEXPERTS expertise with the powerful solutions of SIEMENS Digital Industries, we offer a complete approach to boosting the performance of your products and processes. Schedule a meeting with us to explore together how we can optimize your operations and reach new levels of engineering excellence. CAEXPERTS is ready to be your strategic partner in the search for innovation and efficiency. Get in touch now and take the next step towards success. References D. C. Jiles and D. L. Atherton. “Theory of ferromagnetic hysteresis”, J. Magn. Magn. Mater., vol. 61, no. 1–2, pp. 48–60, 1986. F. Preisach. “Über die magnetische Nachwirkung”, Zeitschrift für Phys., vol. 94, no. 5–6, pp. 277–302, 1935. A. J. Bergqvist. “A simple vector generalization of the Jiles-Atherton model of hysteresis”, IEEE Trans. Magn., vol. 32, no. 5 PART 1, pp. 4213–4215, 1996. S. Hussain, Development of advanced material models for the simulation of low-frequency electromagnetic devices, Ph.D. Thesis, McGill University, Montreal, Canada, Feb. 2017. O. Bottauscio, M. Chiampi, C. Ragusa, L. Rege, and M. Repetto. “Description of TEAM Problem: 32 A test case for validation of magnetic field analysis with vector hysteresis”, 2010. [Available online] www.compumag.org/jsite/images/stories/TEAM/problem32.pdf S. Hussain, V. Ghorbanian, A. Benabou, S. Clénet, D. A. Lowther. “A study of the effects of temperature on magnetic and copper losses in electrical machines”, Proc. 2016 XXII Int. Conf. Elect. Mach., pp. 1277-1283, 2016. T. Rahman, R. C. P. Silva, K. Humphries, M. H. Mohammadi, D. A. Lowther. “Design and optimization of fractional slot concentrated winding permanent magnet machines for class IV electric vehicles”, Proc. IEEE Transp. Electrific. Conf. Expo. (ITEC), June 2016.

Get 3D insights into lithium-ion battery cell performance. Export CFD study results to create Reduced Order Models (ROM). Automate sophisticated simulation workflows. Evaluate the thermal comfort of the passenger cabin. Plus, many more features. With the release of Simcenter STAR-CCM+ 2310, we provide engineers across industries with computational fluid dynamics (CFD) capabilities to accelerate complexity modeling. Leverage exciting new capabilities to explore engineering possibilities and turn complexity into a competitive advantage. Quickly get detailed 3D insights into battery cell performance To virtually design reliable and high-performance lithium-ion cells, it is necessary to consider three-dimensional anisotropic effects in battery cell layers. Currently available simulation approaches neglect such effects or make crucial compromising simplifications, reducing the problem to representative descriptions of the two-dimensional battery layer. With the Simcenter STAR-CCM+ 2310, we are launching a unique new 3D cell design capability to design lithium-ion battery cells with high geometric and physical fidelity. This new high-fidelity cell design model enables the design of complete 3D lithium-ion cells, with geometrically resolved electrode layers, separators and flaps. Modeled simulation leverages dedicated, easy-to-use custom trees and the new Stages feature for a customized, tailored workflow for cell designers, with industry-standard terminology and units. It provides simplified mesh setup with a few inputs and clicks, and supports dedicated industry-standard post-processing to facilitate analysis of simulation results. Capability is driven by simulation models for industry standard cell formats. With the Simcenter STAR-CCM+ 2310 we launched the stack cell model; Cell models with cylindrical and prismatic windings will be available soon. Along with this automated workflow, the 3D cell design capability provides highly accurate electrochemical models through an improved physics-based model of the initial Newman-Doyle-Fuller formulation. The 3D cell design feature provides detailed information about cell performance at a glance. Investigate in-plane and through-thickness ion concentration to understand local and edge effects, or predict the effect of flaps and surface cooling to design better battery cells faster. The full potential of the tool requires the complementary battery license. Set up gas thermal runaway ventilation simulations in minutes Setting up gas vent thermal runaway simulation for a battery with hundreds of cells is a time-consuming and error-prone process. Therefore, in Simcenter STAR-CCM+ 2310, we start with consecutive launches of a dedicated workflow to speed up thermal runaway propagation simulation setup time. With the release of version 2310, we continue this effort with the integration of the gas vent configuration. As far as pre-processing is concerned, the new capability allows for very quick setup with easy selection of cell ventilation surfaces. Additionally, a dedicated field function manages the energy balance between the energy released by ventilation and that generated by the cell's internal parts, eliminating the need for complex field functions and monitors. Trigger and gas release conditions are now also simplified for some inputs. Ultimately, the workflow requires only one set of input parameters to deploy it across all battery cells. Integrated automation controls gas vent actuation upon reaching the trigger condition and post-processing is automatically managed with dedicated gas vent quantities in the “Battery Module Reports” tool. Overall, with the Simcenter STAR-CCM+ 2310, you will continue to benefit from rapid setup and analysis of thermal runaway simulations, now even including gas venting with minimal effort. The workflow can only be accessed in the Simcenter STAR-CCM+ Batteries add-on and therefore requires the associated add-on license. More efficient aerovibroacoustic simulation workflow Reduced CGNS file size and import time into Simcenter 3D through new mapping method for loosely coupled aerovibroacoustic workflow . Example: Assessment of side mirror-induced noise Vibroacoustic simulations are typically performed in two steps: After a CFD simulation in Simcenter STAR-CCM+, Simcenter 3D is used for vibration and acoustic field analysis. The legacy workflow consisted of exporting a very large CGNS file with the CFD mesh and force information, importing this file into Simcenter 3D, and mapping the results onto a coarse acoustic mesh. With Simcenter STAR-CCM+ 2310, we offer a new option to map a fine CFD mesh to a coarser acoustic mesh directly in Simcenter STAR-CCM+ before data export. This conservative maximum distance mapping ensures consistent results for the legacy process using the same mapping algorithm as Simcenter 3D, but significantly reduces the size of the resulting CGNS file. Depending on the case, the new CGNS file can be between 35% and 90% smaller with this new method, and the added mapping step has virtually no impact on the overall Simcenter STAR-CCM+ simulation time. Whenever you are looking to couple a fluid solution in Simcenter STAR-CCM+ with a structural analysis in Simcenter 3D, you will benefit from significantly more efficient process and data transfer. Improve the accuracy and speed of water management simulations Many multiphase applications require precise yet efficient handling of droplets sliding across surfaces. Typical use cases include tracking raindrops sliding across the surfaces of moving vehicles, including car windshields, mirrors, and sensor surfaces. Although it is in principle possible to use the high-fidelity Volume of Fluid (VOF) method, it is very expensive and for large numbers of sliding drops, VOF simulation is computationally prohibitive. To predict the dynamics of these droplets on surfaces, a Lagrangian approach is very efficient, but it is of fundamental importance to take into account the effects of surface tension with high precision. With Simcenter STAR-CCM+ 2310, we therefore introduce a new type of Lagrangian phase, so-called wall-bound droplets, and a new particle shape model called Spherical Cap Particles. The latter provides a more accurate prediction of particle drag and heat transfer. Droplets attached to the wall can also be absorbed into a fluid film to accurately model filament formation. A new adhesion force model allows capturing the typical adhesion and sliding motion for wall-attached droplets using the concept of contact angle hysteresis. This is of particular importance in applications such as cleaners. The entire new modeling structure, with its first submodels, allows you to run simulations with accurate and fast tracking of drops and sliding flows. This results in greater accuracy and speed of water management simulations. Accelerate multiphase EMP simulations with minimal loss of accuracy Acceleration of large-scale Eulerian multiphase simulations (EMP-LSI) via implicit multisteps. Nuclear industry application where cooling water is introduced, leading to a countercurrent of displaced gas with slug flow. The acceleration is shown with an increasing number of substeps along with the flow field at the end of the simulation. Source: Gas-liquid countercurrent flow in PWR [Deendarlianto et al., NED, 39 (2012)] Multiphase simulations are often computationally expensive or not sufficiently accurate. While smart hybrid multiphase solutions offer the ability to apply the most effective approach in each state of the multiphase, all respective submodels need to perform at their best to achieve maximum throughput. For this reason, in Simcenter STAR-CCM+ 2310, we have added several implicit steps for Eulerian Multiphase (EMP) targeting large-scale interface (LSI) simulations, mirroring equivalent capacity previously added for VOF and MMP. This leads to more efficient EMP-LSI simulations, reducing simulation time for a given level of accuracy; or increasing accuracy for a given runtime (budget). Significant reductions in execution time can be achieved by running N substeps within the flow time step and then increasing the flow time step by a factor N. This maintains the substep time scale associated with transporting the fraction of volume at the same level (CFL number), but because the computational cost of a substep is a small fraction of the cost of a full flow time step, there is a significant cost savings. Alternatively, this feature can be used to improve accuracy with a small additional computational cost by adding substeps for a given flow time step size. Optimize cabin design through standardized passenger thermal comfort assessment in a fully integrated manner Passenger thermal comfort is a significant factor in end customer satisfaction in any vehicle. While vehicles powered by internal combustion engines have made the work of HVAC (Heating, Ventilation and Air Conditioning) engineers and system energy management considerably easier thanks to the large amount of surplus heat, electric vehicles require much more diligent handling of the energy and heat, in exchange for comfort, safety and autonomy. With Simcenter STAR-CCM+ 2310 , you can now optimize vehicle cabin design and HVAC systems through a fully integrated suite of industry-standard passenger thermal comfort assessment models. A new state-of-the-art thermoregulation model is now available to calculate the thermal response of the human body as a function of cabin conditions (radiation, convection). The model also takes into account physiological factors, such as the level of metabolic activity, and uses them to accurately calculate skin temperature across the body. These temperatures are then used to calculate the Dynamic Thermal Sensation (DTS) and Predicted Percent Dissatisfaction (PPD) global comfort indices, as well as the Equivalent Homogeneous Temperature (EHT) local comfort indices. These are widely recognized industry standard metrics that are crucial for evaluating passengers' overall perception of comfort through DTS and PPD, as well as locally for each major body part through EHT. All new models mentioned are fully integrated with the latest Simcenter STAR-CCM+ automation features . This allows you to create leaner, more efficient end-to-end workflows for cabin design studies. Simulate more applications on GPUs The benefits of GPU-enabled CFD simulation acceleration are undoubtedly; Significantly lower simulation cost in the cloud, massive reduction in power consumption and replacement of hundreds of CPU cores with one GPU node. Over several release cycles, the excellent performance of Simcenter STAR-CCM+ on GPUs has been demonstrated. It is of fundamental importance to expand the ability to leverage GPUs for more models and, consequently, more applications. With Simcenter STAR-CCM+ 2310 , we therefore continue porting solvers and resources to make them equally available for native GPU and CPU simulations. With this release, you can leverage a GPU-native coupled solid energy solver , a GPU implementation of the Equilibrium Air equation of state, and the Gamma-ReTheta transition model. This means, for example, more efficient conjugate heat transfer, e.g. turbine blade cooling simulations, faster supersonic and hypersonic aerospace aerodynamics, and laminar-turbulent transition flows. Continuing our philosophy of a unified codebase for CPUs and GPUs, you can be confident that GPUs will provide CPU-equivalent streaming solutions. Access virtually unlimited computing resources in your simulation environment Running CFD simulations in the cloud offers greater flexibility and scalability on on-premises hardware, with on-demand access and unlimited capacity. However, configuring and accessing the cloud using third-party providers often requires significant time and expertise in cloud and HPC technologies and disrupts existing workflows. Directly from Simcenter STAR-CCM+ , Simcenter Cloud HPC provides instant access to the optimized Amazon Web Services (AWS) infrastructure, configured and managed by Siemens, with no additional configuration required. With the launch of the Simcenter STAR-CCM+ 2310 , we are expanding the availability of Simcenter Cloud HPC from the Americas to Asia Pacific, with the service expected to launch in Europe, the Middle East and Africa soon. For more information on how to access and try Simcenter Cloud HPC for free, contact CAEXPERTS at the link at the end of this post. Prepare large, complex geometries faster with Parallel Surface Wrapper Meshing time is a critical factor for fast overall CFD simulation response time, especially for complex assemblies. The Surface Wrapper has proven to be a very powerful tool for automatically preparing watertight surfaces for subsequent surface re-wrapping and volume-wrapping. Until now, the surface wrapper has employed shared memory parallelism. In Simcenter STAR-CCM+ 2310 , we are introducing the first phase of the distributed memory parallelized surface wrapper (MPI). In this first version, the pipeline from surface wrapping to gap closure has been parallelized. Overall, the speedup of the new algorithm is up to 2.4 times. Compared to the legacy surface wrapper , there is a reduction of approximately up to 43% in wrapping time for various industrial cases. Although the new MPI surface wrapper yields consistent results across various core counts, it locally provides enhanced positioning of gap-closing faces for improved mesh quality and generally exhibits better adherence to user input, such as gap-closing size. Create reduced order models (ROM) from CFD design exploration studies in just a few clicks Reduced-order models represent great opportunities to quickly explore the design space and create fast-running models for real-time feedback. However, to obtain valid conclusions, such models need to be provided with sufficient, validated and – without derogation – well-organized data. Compiling these training and validation datasets from CFD results to create the Reduced Order Model (ROM) can be a tedious and error-prone process if the interface for data transfer is not handled properly. With the release of the Simcenter STAR-CCM+ 2310 and the recently released Simcenter Reduced Order Modeling software, we enable the most seamless approach to go from scalar field screenshots of your steady-state CFD results directly to a static ROM. You can now export study data from Designer Manager with one click, ready to be used as a training and validation dataset in Simcenter Reduced Order Modeling. The current capability supports snapshots of scalar scenes with a fixed color scale from any type of design study. After export, Simcenter STAR-CCM+ creates a comprehensive package including all images from your snapshots. Simcenter 's reduced order modeling will then generate the ROM prediction using proper orthogonal decomposition (POD) and report a ROM fidelity index. Although the data export feature can generally be used for any type of parameter, the POD method works best for moderate parameter variations when rotation effects are negligible and geometry movement is small enough. Overall, the new ROM data export allows for rapid ROM construction from CFD simulation studies. You can now create fast-running models from CFD simulations with confidence and benefit from improved collaboration between CFD analysts and system designers thanks to immediate previews of scenario variants via ROMs. Included in Design Manager, exporting CFD data does not require a license. For subsequent ROM generation, a Simcenter Reduced Order Modeling license is required. Explore and share engineering results in your browser Launching in early 2022, Simcenter STAR-CCM+ Web Viewer allows you to easily explore and share your engineering results directly from your browser. This powerful tool offers fast, interactive data analysis capabilities for free and from virtually any device with no installation effort, ultimately improving the communication of CFD results. However, when working with a scene file in Simcenter STAR-CCM+ Web Viewer, you need to be able to work as autonomously as possible without needing to go back to Simcenter STAR-CCM+ . So in version 2310, we're taking a big leap in that direction with the Simulation Framework feature. By providing the ability to freely hide and show objects across multiple view layers, it is easy to understand how a scene is configured and better understand the source simulation configuration. Frequent users of Simcenter STAR-CCM+ will immediately identify the similarities with displayers and their desktop client hide and show concepts. Users new to Simcenter STAR-CCM+ , on the other hand, become familiar with the different visualization layers through easy-to-understand nomenclature. The degree of control over visibility is very granular, ranging from high-level control of the display down to the surfaces of individual parts. This gives you unrestricted control over what should be shown and what should be hidden. Quickly automate sophisticated simulation workflows with Stages and the Automation node To model the complexity of today's products and simulate them under real-world conditions, you need to implement sophisticated multiphysics CFD simulation workflows. Traditionally, this task requires the use of scripts or the complicated and error-prone transfer of data from one simulation model to another. Simcenter STAR-CCM+ is designed around a simplified CAD-to-results pipeline, providing fully integrated native automation capabilities. Building on this foundation, the Simcenter STAR-CCM+ 2310 further extends simulation automation intelligence with Stages. Stages allow you to handle multiple physical configurations in a single simulation, reducing the need for scripts. With a single click, you can prepare different physical models, conditions – such as interface or boundary conditions and other settings. A staged object can have different settings for each stage. Objects that are not staged will maintain the same values ​​at all stages. Applications that immediately benefit from Stages are vehicle thermal absorption, the recently released battery cell design model, and more. Combined with Simulation Operations, this enables fast and consistent management of complicated simulation sequences. You can now manage complete stages of simulation configurations and orchestrate their execution without manual intervention or Java macros, and share these workflows with your colleagues in a single simulation file. To further increase your productivity, we are introducing a new node in the simulation tree: the Automation node. You will now benefit from one location in the simulation tree that contains all automation aspects of the simulation workflow. This allows you to generate automated workflows faster and increases the discoverability of already defined simulation workflows with better node organization and less clutter. Together, Stages and the automation node take the concept of an intelligent simulation file, enabling end-to-end automation, from CAD to results, to the next level. Enabling you to explore more projects and solve complex multi-physics problems faster. These are just a few highlights of the Simcenter STAR-CCM+ 2310 . These capabilities will enable you to design better products faster than ever before, turning today's engineering complexity into a competitive advantage. In short, the Simcenter STAR-CCM+ 2310 represents a significant leap in computational simulation capability, providing notable advances in battery cell modeling, thermal simulations, aerovibroacoustic simulations, and more. With features like Simcenter Cloud HPC, parallelized Surface Wrapper, and workflow automation, we give engineers powerful tools to accelerate product development and explore new frontiers of innovation. If your company seeks to stand out at the forefront of engineering, the specialized team at CAEXPERTS is ready to collaborate, applying these advanced solutions in simulation and engineering. Schedule a meeting with us to boost your competitiveness and transform challenges into opportunities.

Models are the core of all model-based techniques for design, control, optimization, simulation, etc. Detailed models are the core of design activities and can be complex and slow to compute. How to create simplified, multi-purpose versions to scale and deploy your usage? The answer is Reduced Order Models ( ROMs). ROMs are an efficient way to reduce the complexity of models and expand their range of applications. They are key components for various applications, such as integrating 3D models into 1D models, accelerating simulations, enabling digital twins and real-time applications, creating virtual sensors and protecting IP (Intellectual Property). Today's application will show you how to scale down electrical power systems using Simcenter's Reduced Order Modeling . The system represented in Figure 1 represents a transmission system in which the generated power (here represented by the input voltage source) is amplified by the transformer and transmitted to the load (battery) through the transmission line. Here, the complexity comes from the transmission line model. Basically, to capture transient phenomena well, the transmission line model is discretized in space where each section (here 50 sections per 100 km) is represented by a simple circuit, as shown in Figure 1 . When the number of snippets increases, the model will be more accurate, but the number of state variables will increase. This makes the entire model quite large and consumes a lot of memory. In this context, the objective behind creating a ROM is: Reduce the total number of state variables by simplifying the transmission part model (transformer + transmission line) Faithfully reproduce the transient phenomena resulting from different interconnections This will be done using Simcenter Reduced Order Modeling . The tool offers several ways to make ROMs: either from simulation data using, for example, Neural Networks and Response Surface Models (RSM) techniques or models such as state space matrices of a linearized model as in our application here. Figure 1 The entire process can be summarized in a few steps: Isolate the transmission part Use Simcenter Reduced Order Modeling to Create a ROM Connect the ROM to the rest of the system Check the accuracy of the results Let's start. Step 1: Before making a ROM, the transmission part is disconnected from the rest of the system as shown in Figure 2 and linearized using Simcenter Amesim. The input variables are the transformer input voltage as well as the voltages at the end of the transmission line. It was decided to consider the voltages at the connection points as inputs and the currents as outputs. This helps establish a physical connection between the ROM and the rest of the physical model. Figure 2 Now we are ready to start making a ROM. Step 2: The second step consists of loading the linearization data (matrices) into Simcenter Reduced Order Modeling , computing a reduced model, evaluating it and exporting it. Let's see how this works. The first step is to open Simcenter Reduced Order Modeling and create a state space project as illustrated below. Then load the linearized model you created before using the Add Data button. When selecting the Simcenter Amesim model of the transmission part, all computed linearized models are proposed. Let's choose the one calculated in 1 second. Figure 5 shows the properties of the loaded model. Figure 5 Now, let's go to the model tab and make a ROM. When you click on the New template button, different types of templates are proposed. Here we are dealing with a medium-sized model with 104 state variables. In this case, Balanced Truncation is a good candidate. When you click the start button , a ROM is automatically computed and evaluated. A truncation order of 58 is proposed here based on the Hankel singular values ​​of the model. The tool indicates an overall loyalty rate of 86% . Looking at the frequency response graph, it can be seen that the ROM covers a large frequency bandwidth (up to 2.6 kHz) of the original model, which is good enough for our application. The next step is to save the computed model using the Add Model feature as illustrated below. The computed model being saved, we go to the Export tab and export it. Step 3 To handle diverse applications, four targets are proposed when exporting state space ROMs. They allow connection to both Simcenter Amesim and other simulation tools using, for example, FMUs (Functional Mock-up Units) for co-simulation or binary files. Here, the computed ROM is exported as a Simcenter Amesim submodel . Back in Simcenter Amesim, we will now connect the exported ROM (available in the ROM library specified in the export stage) to the rest of the power system, as illustrated in Figure 9 . Two first-order phase shifts with a cutoff frequency of 2.5 kHz are added to keep the signals within the frequency range of interest (up to 2.6 kHz). Our reduced power system now has 62 state variables compared to 106 for the full power system depicted in Figure 1. The total size of the original model is then reduced by 41.5 %. Almost ready! All that remains now is to validate the ROM by comparing the full simulation results ( Figure 1 ) and the reduced power system models ( Figure 9 ). Figure 9 Step 4 Both power systems depicted in Figures 1 and 10 are simulated for 2 s with a variable step solver using Simcenter Amesim. Figure 10 shows the battery input voltage as well as its state of charge. Figure 10 The results show a high goodness of fit with fewer state variables ( 62 compared to 106 ). This is reflected in the loyalty metrics ( 86 % overall loyalty) indicated by Simcenter Reduced Order Modeling . In terms of usability, the ROM obtained can be used as a digital twin of the transmission part. It can also be shared between different partners working on the same application and possibly using different simulation tools. Conclusion It was shown here how to reduce electrical power systems using Simcenter Reduced Order Modeling . It allows you to easily minimize the number of state variables of a power system by creating a ROM of its transmission part. This has many advantages: It widens the scope of the model, making it less memory consuming Allows you to share models with different partners while preserving IP It enables rapid prototyping and design For this, Simcenter Reduced Order Modeling offers great features to easily create a ROM for a large-scale state space model. The workflow is simple and intuitive, with the ability to easily evaluate ROM fidelity based on different fidelity indicators. The tool also offers different export targets to suit all possible uses, so that the computed ROM can be used in different contexts. In summary, Simcenter Reduced Order Modeling offers an effective approach to simplify models in electrical power systems. By following the outlined process, you can achieve computational efficiency and facilitate model sharing, providing the ability for rapid prototyping and design. Leading this innovation, CAEXPERTS stands out as a company specialized in solving industrial challenges through digitalization and advanced engineering. Its experienced and multidisciplinary team uses cutting-edge technology, such as Simcenter Reduced Order Modeling, to offer assertive solutions with a high return on investment. To explore how CAEXPERTS can boost your efficiency and innovation, schedule a meeting with us now!

Siemens Digital Industries Software offers a wide range of modeling and simulation solutions to help engineers understand and predict the functional behavior of mechanisms. One of the existing tools in Simcenter 3D is Simcenter 3D Motion Simulation, which provides a series of modules intended to increase design confidence and reduce risk. Let's explore these modules concisely: Simcenter 3D Motion Simulation Simcenter 3D Motion is an integrated part of the broader Simcenter 3D multidisciplinary simulation environment . It offers capabilities for advanced quasi-static, kinematic, and dynamic analysis. This solution helps engineers evaluate the performance of mechanisms, increasing confidence in the project by being able to measure forces, torques and reactions in operating situations of the mechanisms that govern the project. Accuracy in Predicting Mechanism Behavior Simcenter 3D Motion provides accurate results for reaction forces, displacements, velocities, and accelerations for rigid and flexible bodies. Platform for Multidisciplinary Simulation Simcenter 3D Motion is part of an integrated multidisciplinary simulation environment. It allows the integration of motion simulations with other disciplines, with the possibility of integrating measured force data to perform finite element analysis and flexible body analysis. Solution for Designers and Analysts Simcenter 3D Motion is flexible enough to serve both designers and analysts. Analysts can create mechanism models from scratch, while designers can quickly convert CAD models into functional motion models, saving modeling time. Systems and Controls Integration Simcenter 3D can be integrated with leading control design tools and supports model switching and cosimulation methods to solve mechanical system equations simultaneously with controller or actuator equations. This helps you understand how the controls will affect the overall performance of the engine. Industry Applications Simcenter 3D Motion is useful in a variety of industries, including automotive, aerospace, marine, industrial machinery, electronics, and consumer products. It helps understand the behavior of complex mechanical systems, such as vehicle suspensions, automatic door mechanisms and electronic control systems. Specific Modules Additionally, Simcenter 3D Motion offers a variety of specialized modules. Below, we present a summary of these modules and their respective characteristics: Simcenter 3D Motion Modeling This module provides multibody pre- and post-processing capabilities to model, evaluate, and optimize mechanisms. It is widely used in industries such as aerospace, automotive, industrial machinery and electronics to study the kinematics and dynamics of products during their development. Simcenter 3D Motion Solver Simcenter 3D Motion Solver helps engineers predict and understand the functional behavior of parts and assemblies. It offers complete capabilities for dynamic, static, and kinematic motion simulation. Simcenter 3D Motion Systems and Controls This module helps mechanical engineers predict how control systems affect mechanisms and allows them to optimize mechatronic system designs. It offers a library of control modeling elements and is compatible with MATLAB and Simulink environments. Simcenter 3D Motion Flexible Body Simcenter 3D Motion Flexible Body increases the accuracy of multibody models by considering component deformations during motion simulation. It allows you to combine multibody simulation technology with a representation of body flexibility. Simcenter 3D Motion Flexible Body Advanced This module extends flexible modeling by automating the process of transforming existing geometry into a flexible body for motion analysis. It also allows you to model constraints and contact forces applied to flexible bodies. Simcenter 3D Motion Standard Tire Simcenter 3D Motion Standard Tire allows you to model forces generated by pneumatic tires in contact with the road, including resulting moments. This is essential for analysis of drivability and driving comfort. Simcenter 3D Motion CD Tire This module offers a family of tire models developed by ITWM Fraunhofer. It is suitable for simulating tires of different vehicles, providing accurate analysis of tire behavior. Simcenter Tire Allows accurate modeling of tire behavior and analysis of vehicle performance, directional stability and braking distance. It helps engineers analyze vehicle behavior efficiently. Simcenter 3D Motion Drivetrain This module is dedicated to the simulation of transmission elements, facilitating the creation of detailed models of transmissions and gear systems. Simcenter 3D Motion TWR Simcenter 3D Motion TWR enables the construction of virtual test equipment for frequency and system response analysis. It is useful for simulations involving equipment without physical components. Simcenter 3D Motion Real-Time Solver This module provides the ability to integrate Simcenter 3D Motion models into real-time platforms, reusing models in real time and accelerating analysis and design experiments. Simcenter 3D Flexible Pipe Standard Beam Dedicated to piping simulation, this module allows you to simulate assembly scenarios and calculate initial positions, operating positions and forces/moments inside the pipes. Simcenter 3D Flexible Pipe Standard Shell Similar to the previous one, this module is also used to simulate piping, but with a focus on validating designs and checking collisions. Simcenter 3D Flexible Pipe Linear Dynamic Allows calculation of eigenmodes and harmonic response of flexible pipes, using beam FEM or shell FEM calculation methods. Simcenter 3D Flexible Pipe Nonlinear Dynamic This module allows the analysis of non-linear movement of flexible tubes, being useful for dealing with complex situations. Simcenter 3D Flexible Pipe Optimization It is an extension that allows you to carry out parametric studies and optimize the position and orientation of components to obtain more efficient and economical designs. Simcenter 3D Flexible Electric Cables and Wire Harness option This module is used to calculate electrical harnesses and wires. It helps in accurate designing of harnesses Simcenter 3D Motion Simulation from Siemens Digital Industries Software is a powerful tool for engineers who want to increase confidence in mechanism design and reduce risk. With a variety of specialized modules and advanced features, it offers a complete platform for motion modeling and simulation in a variety of industrial applications. See some direct applications in the video below that demonstrates how to transfer Motion loads to pre/post: CAEXPERTS, with its experience and knowledge in engineering, is the ideal partner in implementing and leveraging technologies such as Simcenter 3D Motion. With a team of highly qualified CAE experts and cutting-edge resources, we are ready to help your company explore the full potential of this powerful tool. Whether optimizing product design, improving industrial processes or tackling complex challenges, CAEXPERTS is committed to driving competitiveness and innovation in your organization. Learn more about Simcenter 3D Motion clicking here . Schedule a meeting right now and let’s turn your challenges into high-impact engineering solutions together!

Using a Digital Twin to Reframe Aircraft Design for Sustainable Flight In this post we will analyze the challenges faced by aerospace engineers in developing sustainable aircraft. We investigate the use of hydrogen-powered jet engines and hydrogen fuel cell technology to power next-generation propulsion systems, as well as their implications on subsystems, resulting in the need to reimagine aircraft configurations. Simcenter™ software from Siemens Digital Industries Software supports Digital Twin technology, enabling aerospace engineering organizations to optimize aircraft performance through virtual and physical testing in the domains of fluids, thermal, mechanical and other systems related to sustainable aviation . Simcenter is part of the Siemens Xcelerator portfolio, which encompasses software, hardware and integrated services. Sustainable Aviation The aviation industry is responsible for nearly 5% of global greenhouse gas emissions,¹ making the transition to low-carbon propulsion systems a priority for aircraft manufacturers. However, this transition is complicated by the constant increase in passenger numbers. Currently, around 500,000 people are on flights at any given time,² and the number of air passengers is expected to double by.³ Aerospace engineers face the challenge of designing next-generation aircraft that have the capacity, speed and range of conventional jet-powered aircraft, but without the environmental impact. Comparing Power Densities of Different Energy Sources To understand the complexity of the task at hand, it is critical to analyze the power densities of leading energy solutions for next-generation aircraft compared to conventional kerosene. Jet A kerosene, which powers most modern commercial and military aircraft, has a remarkable energy density of approximately 12,000 watt-hours per kilogram (Wh/kg). However, kerosene jet engines generate CO2 and non-CO2 emissions and are noisy. A cleaner and quieter alternative is the use of battery-powered electric motors. However, current batteries used in prototype aircraft have energy densities of only 160 to 180 Wh/kg,⁴ unsuitable for long-haul aircraft. However, they are suitable for smaller aircraft, such as Bye Aerospace,⁵ specializes in electric aircraft, including light aircraft for flight training. Figure 1. Using Simcenter , NX and Fibersim helped Bye Aerospace increase productivity, reducing engineering headcount by 66% when designing all-electric aircraft. Hydrogen Production and Conversion into Usable Energy There are currently two main hydrogen-based approaches to creating long-haul aircraft with zero carbon emissions. One is the use of jet engines powered by liquid hydrogen, and the other involves hydrogen fuel cells that convert hydrogen and oxygen into electricity to power electric motors. Both liquid hydrogen and hydrogen fuel cells are being actively investigated by companies such as Siemens⁶ and Airbus⁷ as environmentally friendly alternatives for air travel. Both approaches produce water as a byproduct. Although there are several ways to produce hydrogen,⁸ generating hydrogen is not a simple task, as it is generally present in compounds, such as water (H2O) or methane (CH4), from which it must be separated. Electrolysis is the most practical method for producing hydrogen, which involves the splitting of water into hydrogen and oxygen using an electrical current, and is considered renewable when electricity is generated from sustainable sources, such as solar and wind. Hydrogen can be stored in gaseous or liquid form. Gaseous storage requires high-pressure tanks, while liquid storage requires cryogenic temperatures, as hydrogen boils at -252.8 degrees Celsius (°C) at atmospheric pressure.⁹ Due to the costs involved in producing, storing and transporting hydrogen, it is currently more expensive than fossil fuels. However, in terms of application as an energy source, hydrogen is conceptually simple. Aerospace engineers dedicated to developing propulsion systems for sustainable hydrogen-powered aircraft consider three main approaches: electric engines powered by fuel cells, gas turbines powered by pure hydrogen, or hybrid solutions that combine fuel cells with gas turbines powered by hydrogen. . In the case of a hydrogen-powered jet engine, which resembles an internal combustion engine, the process involves intake of air, compression, mixing with hydrogen and subsequent ignition to generate a high-temperature flow. In the hydrogen fuel cell scenario, hydrogen and oxygen are routed through an anode (positive terminal) and a cathode (negative terminal) in the cell, respectively. A catalyst at the anode splits hydrogen molecules into electrons and protons. Protons pass through a special membrane, while electrons power the aircraft's electric motors and other systems. Subsequently, protons, electrons and oxygen recombine at the cathode, forming water molecules. Challenges of Hydrogen-Powered Aircraft The main challenge in developing hydrogen-powered aircraft is their relatively unknown nature to most engineers. Designing a burner for a hydrogen gas turbine requires special structures and features, since hydrogen burns faster and hotter than kerosene. For example, a hydrogen burner must be designed to prevent flashbacks . Furthermore, the acoustic frequencies generated by the burner and turbine need to be attenuated to minimize interaction between the flame and aircraft components. Understanding the fluid dynamics and stresses in the thermal boundary conditions of these hydrogen-powered and electric propulsion systems, including operational phenomena such as recoil, thermoacoustics, thermal gradients, and embrittlement, is essential. ¹⁰ ¹¹ ¹² ¹³ Another challenge is that although hydrogen offers three times the energy density of kerosene per unit mass, it requires four times the volume of kerosene to produce the same result. This implies significant modifications to the aircraft structure, such as reducing cargo capacity, number of passengers or a departure from conventional designs. Figure 2. The increased fuselage space of mixed-wing aircraft can be used to store batteries, hydrogen, or a combination of hydrogen and fuel cells, without sacrificing passenger or cargo capacity. An alternative is the combined wing body (BWB) aircraft, such as the Airbus ZEROe BWB concept,¹⁴ where the wings and fuselage integrate into a single structure (Figure 2). This design, also called "flying wing", is responsible for all of the aircraft's lift. One of the main advantages of a flying wing configuration is the ample space in the fuselage that can be used to carry various types of payloads, including passengers, batteries, hydrogen and fuel cells. Facing the Challenges The complexity of the task of creating hydrogen-powered, carbon-neutral long-haul aircraft makes the evolution of physical prototypes unfeasible due to cost, time and resource constraints. The solution is to resort to multiphysics simulations to investigate the behavior of power generation systems, engines and the entire aircraft in a virtual environment. This endeavor requires an integration of different design domains and effective collaboration between all engineering disciplines involved in aircraft development. This goes beyond propulsion systems, covering areas such as fluid dynamics, thermal, mechanics, dynamics, acoustics, among others. Engineering data from these interconnected systems must be shared efficiently across teams to enable designers to work effectively in their native development environments. One way to achieve this effective collaboration is through the use of digitalization tools available in the Siemens Xcelerator portfolio,¹⁵ which includes integrated software, hardware and services. Simcenter test and simulation solutions , part of this portfolio, are designed to eliminate barriers between disciplines and provide an integrated design suite capable of supporting multidisciplinary aerospace engineering teams. These solutions help model, analyze and test the impact of alternative energy sources and propulsion systems. In short, they allow the creation of a physically based digital twin (Figure 3). Figure 3. Using Simcenter, engineers can build a digital twin to accurately predict aircraft performance, optimize designs, and innovate faster and more confidently. Within the Simcenter environment, systems simulation modeling capabilities enable the evaluation of engine architectures, gas turbines, fuel storage, fuel cells, batteries, and other components, including their weight (Figure 4).¹⁶ Figure 4. The Simcenter Amesim model allows engineers to evaluate the thermodynamic cycle of the hydrogen-powered turbofan. Engineers can leverage parallel fluid simulations, 3D thermal and mechanical simulations, and computer-aided design (CAD) capabilities to design each of these subsystems. In this way, they can deal with challenges such as handling cryogenic fuels, hydrogen combustion and measuring the turbine inlet temperature, as well as the durability performance and dynamic response of the system, among others. Several advanced physics are provided in robust and validated Simcenter models (Figure 5). The design workflow runs on automated workflows and design space explorations to handle conflicts between different disciplines. Components such as burners, blades, assemblies, engines, subsystems, and ultimately the aircraft as a whole can be designed in a similar way to meet different design requirements. Figure 5. This multidisciplinary design exploration rendering of a hydrogen-burning hybrid cryogenic propulsion system was generated using the Simcenter 3D , Simcenter STAR-CCM+ , Simcenter Amesim , and HEEDS software tools, accurately representing the aeroelasticity of the design. Simcenter models – including those developed in conjunction with Siemens partners – are generated and run with real-world fidelity to enable aerospace companies to design and deliver real-world systems (figure 6). Simcenter results can be combined with the Siemens Xcelerator portfolio to also take into account the manufacturing capacity of components and systems. Figure 6. This multi-physics design exploration of an H2 micromix burner leverages NX CAD , Simcenter STAR-CCM+, and Simcenter 3D driven by the HEEDS automated optimization tool . (source: B&B AGEMA, RWTH Aachen and Kawasaki) Conclusion Companies such as Siemens Energy,¹⁷ Rolls-Royce¹⁸ and Airbus¹⁹ are carrying out comprehensive evaluations and, in some cases, designing prototypes of hydrogen-powered and hydrogen-hybrid aircraft. However, it is crucial to understand that the transition to sustainable energy sources goes beyond simply modifying aircraft. This transition marks the beginning of a decades-long journey to reimagine aircraft configurations and address challenges that include supply chains, energy production, distribution and logistics networks, airport fueling systems, and more (Figure 7). Figure 7. Ditching fossil fuels requires modernizing energy production and logistics networks, including fuel distribution systems at airports. The Siemens Xcelerator portfolio and Simcenter tools are focused on supporting the digitalization efforts needed to scale the aviation industry toward a sustainable future. At CAEXPERTS (Siemens technology partner specializing in multiphysics computer simulation), we recognize the urgency of the transition to sustainable aviation. The development of hydrogen-powered aircraft and other low-carbon propulsion systems is crucial to addressing the environmental challenges facing our society. With a team of CAE (Computer Aided Engineering) experts and high-performance cloud capabilities, we are ready to lead this revolution in the aerospace industry. Our computer simulation and advanced engineering services are prepared to face the complexity of sustainable aircraft projects. We help industries increase their level of innovation, increase their competitiveness and achieve more efficient operations. If you are committed to innovation and seek solutions to the challenges of sustainable aviation, contact us. Schedule a meeting with CAEXPERTS and discover how our services can boost your projects and accelerate the transition to the aviation of the future. Let's build a cleaner and more sustainable future together. References https://bit.ly/3CxFPTC https://www.spikeaerospace.com/how-many-passengers-are-flying-right-now/ https://www.bbc.com/future/article/20210401-the-worlds-first-commercial-hydrogen-plane https://aerospaceamerica.aiaa.org/features/faith-in-batteries/ https://www.plm.automation.siemens.com/global/en/our-story/customers/bye-aerospace/78928/ https://www.siemens-energy.com/global/en/offerings/renewable-energy/hydrogen-solutions.html https://www.airbus.com/en/innovation/zero-emission/hydrogen https://afdc.energy.gov/fuels/hydrogen_production.html https://www.energy.gov/eere/fuelcells/hydrogen-storage https://www.plm.automation.siemens.com/global/en/our-story/customers/siemens-energy/93022/ https://www.plm.automation.siemens.com/global/en/our-story/customers/b-b-agema/98716/ https://webinars.sw.siemens.com/en-US/simulation-for-digital-testing-with-bb-agema/ https://webinars.sw.siemens.com/en-US/aerospace-defense-aircraft-propulsion-system-simulation https://www.airbus.com/en/innovation/zero-emission/ hydrogen/zeroe https://www.siemens.com/global/en/products/xcelerator.html https://www.plm.automation.siemens.com/global/en/products/simcenter/ https://www.siemens-energy.com/global/en/offerings/renewable-energy/hydrogen-solutions.html https://www.airbus.com/en/innovation/zero-emission/hydrogen https://www.rolls-royce.com/innovation/net-zero/decarbonising-complex-critical-systems/hydrogen.aspx

How the digitalization of engineering has opened up new solutions to old medical problems. Biomechanics has always sought to understand the complex interactions between biological and mechanical systems, unraveling how organisms move, how their tissues and structures adapt to physical demands, and how these principles can be applied in various areas, including medicine, sport , ergonomics and engineering. Through the analysis of forces, moments, movements and responses of biological systems, biomechanics contributes to improving the understanding of how the body works and to the development of solutions and technologies that benefit health, human performance and quality of life. The challenge of biomechanics in assisting medicine and dentistry has always been great, developing suitable materials, experimenting with geometries, manufacturing prototypes and the final part. To try to do this in an agile way, traditional engineering used strategies such as testing on replicas of human structures, mathematical simplifications of models and “ one size fits all ” solutions. Today, with the digitalization of engineering, it is possible to design a product in a completely virtual way, speeding up the production stages, from design, through testing to manufacturing. Given the limitations, in the past, medical companies were limited to executing only a few design iterations, accepting compromises in their creations. However, the current era is marked by the ability to optimize projects by executing countless iterations, tirelessly seeking the ideal design. With advances in technology, materials and manufacturing methods, the next generation of medical devices are becoming more affordable, comfortable and faster to produce. See the example of the revolution that Siemens products generate in the development of prosthetics. The new frontiers of prosthetics Today's prosthetic devices are undergoing constant advances in complexity and customization. To remain competitive within a highly challenging scenario, companies must seek innovations in products and design processes. It is necessary to consider cost, comfort and customization when improving products to meet customer needs. An example of necessity is the following, as an amputee patient grows, their prosthesis needs to adapt to the increasing size of the limb. This growth is a challenge that can make it difficult for children to access prosthetics from an early age. The current cost of replacing a prosthesis annually is prohibitive for many patients. The solution lies in finding ways to reduce the costs of prosthetics and make these devices more accessible to everyone. Furthermore, we know that each patient has particularities in their anatomy, and, while adjustable prostheses meet patients' needs, the ability to digitize the geometry of the region in which the prosthesis will be fitted and design a customized prosthesis model for each patient makes ensuring that the fit is always good and the prosthesis is comfortable from the first use, not to mention the possibilities for optimization in prostheses subjected to high-performance environments, such as prosthetic blades for athletes. How can we transform this process With its integrated set of tools, Siemens enables companies to reduce prosthetic costs, offer customized features and improve the efficiency of their products. The virtual design approach enabled by NX enables patients around the world to access prosthetic devices without the need for in-person consultations. Siemens software opens up countless opportunities for the development of prosthetics, making the process more agile and accessible for those who depend on these devices. NX offers a variety of easy-to - use tools for surface modeling. NX Realize Shape software is an affordable design solution for advanced shape creation. For athletes, prosthetics can be precisely tailored to fit a specific body shape, improving performance with the help of NX 's flexible design tools . This software allows designers to create refined shapes by subdividing an initial body into specific details, providing precise cutouts and geometry extrusions. Additive manufacturing and other production technologies thrive with Realize Shape 's innovative approach to shape development. NX takes additive manufacturing to a new level, significantly expanding the range of products that can be manufactured. Additive manufacturing in NX makes it possible to create lightweight, durable and breathable prosthetics. Design automation replaces labor-intensive processes that involve translations between multiple design tools. Integrated tools allow 3D scanning to be incorporated directly into the socket design, automating the process and resulting in a high-quality, repeatable and personalized socket for each customer. The integration of NX with CAE (Computer Aided Engineering ) enables highly optimized projects. HEEDS software, for example, is a tool that enables simulation-driven design. HEEDS can connect all CAD and CAE tools, accelerating innovation in the product development process. “HEEDS accelerates the product development process by automating analysis workflows (Process Automation), maximizing available hardware and software computing resources (Distributed Execution), and efficiently exploring the design space for innovative solutions (Efficient Search) , while evaluating new concepts ensuring that performance requirements are met (Insight & Discovery).” Simcenter 3D, meanwhile, is a fully integrated, computer-aided design solution for complex engineering challenges. This software offers advanced 3D modeling and effective simulation capabilities to gain a better understanding and improve the overall performance of products. In the aforementioned context of a prosthetic blade for athletes, Simcenter 3D and HEEDS can be used to enhance performance simulation before the product is subjected to real competition conditions. Product performance is of paramount importance. Choosing to use these software allows companies to use several integrated design workflows to test product performance before it reaches customers. Producing a design that achieves optimal performance with greater efficiency improves the overall quality of a company. The future with the use of NX , Simcenter 3D and HEEDS enables growth in market shares with lower development costs and higher quality products. In general, the use of these integrated software enables a more comfortable, reliable and accessible design for the patient, while resulting in cost savings for the company. Want to know more? Schedule your meeting with CAEXPERTS right now and understand how we can help you.

Hello everybody! In an ever-evolving world where operational efficiency, cost reduction and lower emissions have become crucial priorities for Energy and Utilities (E&U) companies, technology is playing a key role. And in this scenario, simulation is leading the revolution. In our latest video, we'll dive deep into the world of simulation and how it powers data-driven decisions that drive innovation and cut costs. It's the smartest way to face E&U's challenges today. E&U companies face intense pressures to improve operational efficiency while reducing costs and emissions. Our video reveals how advanced engineering simulation and testing solutions can: Provide end-to-end engineering analysis and insights across an integrated portfolio. Cover all phases of development of energy assets and systems. Improve collaboration between simulation teams and other engineering disciplines. Enable superior designs while reducing prototyping times and costs. Help engineers identify innovations in plants and assets that accelerate decarbonization. Regardless of rapidly changing market conditions, simulation can help your business achieve continuous improvements across the entire energy supply chain. The energy industry faces constant volatility in prices and supply, as well as the pressing need to reduce emissions. To thrive in this changing environment, energy companies can maximize their innovation through the power of multiphysics simulation. We will examine how simulation helps companies master their complexity, achieving reliable results and sustainable operations. Whether you want to achieve breakthroughs in chemical process engineering or decarbonize your supply chain, simulation empowers your engineers with insights that drive innovation. Physics-based simulation data models define optimal designs for new energy assets, and when combined with a closed-loop digital twin, your engineers can better understand and predict system behavior, leading to improved designs and optimized production. Promoting teamwork and collaboration is key, and our cloud-based simulation solution connects engineering teams to promote teamwork and collaboration. Integrates and retains simulation output analysis in a shared digital twin. With critical information instantly accessible to key stakeholders, decision-making and execution improve dramatically. Discover how your business can achieve its sustainability goals by watching our video. We invite you to explore the endless possibilities that simulation offers for the energy and utilities sector. Watch the video now and start your journey towards more efficient operations, more reliable results and a more sustainable future. Join us in this exciting exploration of simulation in power system development and optimization. It's time to shape the future of the E&U industry with the power of simulation. Schedule a conversation now with CAEXPERTS , technological partner of SIEMENS Digital Industries Software, specialist in complex multiphysics simulations that drive technological development.