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

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

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

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

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

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

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

The beauty of gas turbines Some say that beauty is in the eye of the beholder, but others believe that beauty can be universal. It sounds crazy to some, but often the intricate and complex machinery of a gas turbine, along with the resulting simulation, is considered incredibly beautiful. There is something mesmerizing about all the blades, valves, rotors, as well as the fuel lines and wiring. The shape and structure of the blades, vanes, channels and cavities exhibit an intuitive beauty, which design experts say is essential to the success of a part and assembly. Otherwise, there is a high risk of failure. The theory is that the physics of fluids and structures should align and appear natural, almost as if it came from nature, even if it is counterintuitive to the complexity of the parts, components, wiring, alloys and compounds — the pinnacle of human engineering. Like gas turbines, computational fluid dynamics (CFD) simulations are also known for their ability to mesmerize people with their colorful results. And with more computing power, increasingly unstable simulations with higher fidelity and more complex physics are being made. And even further and faster with GPUs. And with advances in machine learning, one can go even further and faster. Advances in gas turbine simulations and machine learning Siemens Energy has implemented an industry-leading multidisciplinary analysis and optimization (MDAO) workflow supported by Simcenter simulation technologies. This environment incorporates advanced capabilities such as expanded enterprise knowledge capture, artificial intelligence (AI)-powered design wizards, and reduced-order modeling that can operate in near real time. These advancements, including data science methods such as machine learning, have significantly improved the quality and efficiency of the design process. Benefits of gas turbine simulations A look at the current state of the art for gas turbine design workflow The “classical” approach of a CAD image from a jet engine assembly using NX . Designing a gas turbine in the past would take several years and would not always be a success. Thanks to digital tools, we can improve on today’s design quite easily with a multidisciplinary approach of design an optimization. Jet engine assembly (Generated with NX). Even though it is very advanced physics and complex geometries, one can today combine several of these steps in an automated way. Keeping the CAD alive, boundary conditions and various versions stay totally in your control. The design process of a component is shown in the schematic below. This is done by joining the CAD from NX to various CAE simulation tools like Simcenter STAR-CCM+ and Simcenter 3D . The automation and optimization are handled by HEEDS and all data is managed by Teamcenter. It really does not matter if it is higher efficiency through aerodynamics, improved mechanical integrity and durability, reducing cooling air usage or new combustion fuels; they all affect each other and there is no way to be competitive and innovative unless correctly using modern multidisciplinary design space exploration methods. Current state-of-the-art design process for a turbomachinery component. In order to effectively do product development, we want to evaluate as many designs as early on in the process as possible. Taking the next steps into the future means combining this with machine learning, since the design space can become large quickly and with many disciplines involved. What if we could have a machine learning algorithm train itself in real time on the design space that is currently being evaluated with computational fluid dynamics (CFD) or finite element method (FEM)? An improvement on multidisciplinary design optimization for future product engineering For that, we have two proofs of concept that are related to turbomachinery. One is to optimize a water pump efficiency at a flow rate of 110 kg/s and 1200 rpm. We worked on a parametrized model with 12 geometric variables and the number of blades. HEEDS , a comprehensive multi-disciplinary design analysis and optimization (MDAO) software, uses its default search method, SHERPA, to conduct multiple search strategies simultaneously, and it dynamically adapts to the problem as it learns about the design space. With SHERPA, HEEDS can discover 300 design variations in 40 hours. With the introduction of HEEDS AI Simulation Predictor, an add-on extension in HEEDS , SHERPA’s search technology is significantly enhanced. Some CFD simulations are replaced by AI evaluations conducted through an automatically trained AI model, leveraging insights gained from early simulations – revolutionizing this process. In this case, it counted 151 CFD runs while 149 were done with AI evaluation (for a total of 300). This took roughly 20 hours reaching the same results and saving 49% in time. The pump’s efficiency increased by 3% and head by 10%. Water pump – design space exploration with HEEDS AI Simulation Predictor – CAD and CFD results Water pump efficiency for various designs – design space exploration with HEEDS AI Simulation Predictor. The second case is a gas turbine blade for cooling optimization. Here, the objective is to minimize blade temperature and minimize cooling air mass flow. A parametrized CAD from NX is used to simulate in Simcenter STAR-CCM+ . The CAD has 34 parametrized characteristics on the serpentine channel with changes of cooling ribs and shower head holes. The 500 design evaluations done for this case experienced an approximate 38% time save, skipping CFD simulations with AI and still reaching the same best solution. This might mean 20 days of time saved if 160 cores are used for each simulation. This way, you could easily save weeks and months on projects and get a better product faster to market. External and internal temperature for conjugate heat transfer turbine blade design space exploration with HEEDS AI Simulation Predictor, NX and Simcenter STAR-CCM+. Pareto front of design space exploration for minimizing blade temperature and reducing cooling inlet mass flow results using HEEDS AI Simulation Predictor. From these first examples of adding AI and machine learning to an already impressive CAD-CAE workflow, one can already see the potential and how easy it is to get started without being a machine learning or optimization expert. How big the revolution of AI and ML will be and the impact it will have on the fate of the mechanical industry is too early to say. But we already know that it will be the key to staying in front of the competition. Digital twin technology for turbomachinery Schedule a meeting with CAEXPERTS  and discover how the latest advances in gas turbine simulations and machine learning can transform your projects. Take advantage of this opportunity to explore innovative solutions that drive accuracy, efficiency and technical excellence in your industry. WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

We are surrounded by rotating machinery. Pumps, fans, compressors or turbines can literally be found everywhere: In cars – air conditioning fans, turbochargers, pumps for fuel or water, coolant pumps for engine and battery cooling. In our buildings and offices – from heat pumps to refrigerator compressors, hairdryers or electronic cooling fans in computers. And of course the industry and energy sector would never work without massive employment of turbomachinery. Large pump (impeller and volute) assembled by an engineer The dimensions of impellers and housings range from the size of a industry hall – like for gas turbines – to only a few millimeters – for example for incorporate blood pumps. A vast variety of different fluids and gases that can have rather challenging properties need to be transported, like chemicals or other aggressive media, hot gases or particle loaded fluids. Sometimes phenomena like phase change or multi-phase flow or the interaction of the fluid with the solid parts need to be taken into account. Medical applications like blood pumps (see more details in the example pointed out below) or respiratory equipment need to be compliant with strict medical technology regulations. Also food & beverage applications require high hygienic standards, and there are of course always safety requirements that must be fulfilled. Traditional Design versus Computational Methods There are several approaches to design a pump, fan, compressor or turbine. The first step is to decide about the basic concept, and to determine the impeller type: Will it rather be an axial, a radial or a mixed flow impeller? With or without stators, guide vanes, diffusors, volutes or housings? Will it be a single- or rather a multi-stage machine? The “traditional” way then is to use analytical equations to define the main dimensions as well as the flow angles and thus the shape of the blades. The calculations are based on the required duty data – volume flow, pressure rise or pump head for the design operating point, the rotational velocity and fluid properties. Available installation space, the type of electrical drive as well as the materials of the impellers and housings can also be relevant. The blade and housing design can be performed by using either commercially available or custom software tools, spreadsheets or even paper-and-pen based approaches. In reality, industrially relevant flow processes are turbulent and three dimensional – this is even more relevant for flow through rotating machinery. Such “real world effects”, as well as blade thickness, blade number, or the influence of tip clearings or secondary flow paths are neglected or only taken into account via empirical information by the usually applied design tools. The gap between the idealized and the real system can be closed with the help of Simcenter simulation approaches, modeling also the most complex physical behavior and geometry details with high accuracy. Visualization of 3D flow results Design from Scratch or Incremental Development? It is common practice for many manufacturers to incrementally modify already existing designs instead of creating a completely new machine from scratch. After the changes are implemented, prototypes need to be build and tested experimentally. The procedure is typically repeated several times to find a design fulfilling the respective requirements, which can be costly and very time consuming. Furthermore, with such an approach developers and engineers sometimes tend to rather stay in their comfort zone, thus missing potential for more efficiency and innovation. This can be avoided by applying 3D CFD computations or even systematic optimization methods, comprehensively involving the complete available design space. Design Space Optimization for Rotating Machinery Design improvements typically require numerous iterations through trial and error. By leveraging automatic optimization techniques , machines can be designed significantly faster.  Simcenter allows to systematically optimize a design. Different blade parameters can be made accessible as variables during the design exploration, thus representing the blade-shape, the main dimensions and number of blades.  Optimized pump impeller geometry In that way, also contradicting simulation objectives – for example increasing pressure rise or pump head while at the same time reduce power – can be tackled. The chart shown here illustrates the tradeoff between the two objectives. Every dot in the diagram stands for one impeller design point. The blue line represents a so-called pareto front, where one objective cannot be improved without changing the other one for the worse. In doing so, hundreds of pump impeller designs can be compared. Design space analysis with pareto front (blue line) Solving Operational Challenges with Advanced Flow Behavior Modeling A very typical and frequent challenge is cavitation in a pump, occurring when the static pressure in a flow drops beneath the vapor pressure of the fluid, forming gas-filled bubbles. These are carried away downstream with the flow, and decay again when reaching areas of higher pressure. This can lead to unwanted or even dangerous effects like noise, damage of structures or vibration. An example for dealing with cavitation with the help of Simcenter STAR-CCM+ simulations is demonstrated by MORFO (Morfo Design Srl), a startup and spin-off from the University of Florence, specialized in the aerodynamic development of turbomachinery, who has pioneered the integration of parametric and optimization tools with Simcenter STAR-CCM+ . The pump inducer shown here increases the pressure of the cryogenic fluid while minimizing cavitation issues, which arise due to the thermodynamic conditions at the inlet (low pressure relative to temperature). It is thus significantly improving the overall quality of the pump. MORFO has parametrized the inducer with “Papillon”, their own graphical interface. Simcenter STAR-CCM+ was then used to perform a CFD study and optimization, using various modelling capabilities to represent complex phenomena like phase change and gas bubble formulation and transport as well as the interaction of the bubbles with the fluid flow. To be able to evaluate the inducer’s efficiency and it’s robustness against caviation, it is crucial both to represent cavitation mechanisms accurately and to examine also off-design by varying the flow rate. The development goal here was to maintain a constant flow rate and determine the lowest possible total inlet pressure without the inducer significantly losing performance in terms of compression ratio. Pump inducer: discretized geometry Visualization of cavitation areas Modeling Flow Behavior of Specialized Fluids: Medical Blood Pumps Another realm where complex flow behavior must be taken into account are medical devices. Pumps are used for several medical applications, for example for live-support machines, where they are used to maintain blood circulation in emergencies or during surgeries, for ECMO (extra-corporate membrane oxygenation), or as dialysis or infusion pumps.  For blood pumps, it is crucial to operate in a blood-friendly manner and reduce hemolysis – destruction of blood cells – as well as thrombogenicity – clotting of the blood. This can be achieved with the help of 3D CFD by limiting the shear stress in the fluid, keeping the blood temperature below body temperature, and also by trying to avoid stagnating or recirculating flow areas. Blood pump with shear stress at the walls Computational Fluid Dynamics is able to provide detailed insights into the non-newtonian blood fluid flow and allows to optimize the pump accordingly. Good pump efficiency means low power consumption and is an important goal as well. Terumo Corporation is developing next-generation technology, including diagnostic devices, therapeutic devices, myocardial regenerative therapy, and devices for emerging markets and is usingCFD to develop blood pumps for cardiovascular surgical devices. Introducing a CFD-based design exploration tool allowed to increase the efficiency of blood pump development, deal with variations of the blood properties and eventually bring a better design to market faster. The exploration team is not a specialized CAE department, but is using CFD together with optimization techniques in order to both increase the efficiency of the product development process and bring a better design to market faster. Visualized cfd results: pump impeller and volute Benefits for SME: Generate Efficient Rotating Machines, Faster! Many SMEs are often bringing specialized solutions to the market. Design tools for impellers and housings have been used for decades and are based on simplified assumptions and empirical information, but to allow for real-world physical behavior, often costly trial-and error test procedures need to be performed yet. 3D CFD can be a good alternative to extensive physical testing, allowing insights which are not possible experimentally. By systematically exploring the design space, higher quality can be reached in less time. Sophisticated, automated workflows enable engineers and developers to focus more on engineering challenges rather than spending excessive time on modeling tasks and allow to leverage simulation technology also for non-experts.Of course also other disciplines beyond CFD, like structure mechanics, can be tackled with Siemens software products. As a result, engineers and developers can allocate more time and effort towards dealing with complex engineering challenges, analyzing simulation results, and implementing innovative design improvements. The use of Simcenter simulation, available via scalable licensing and pricing, thus empowers them to make informed decisions, enhance performance, and optimize the overall design process, resulting in faster time to market and higher quality. Schedule a meeting with CAEXPERTS and discover how we can improve the performance of your rotating machines. Our experts use advanced technologies, such as Simcenter STAR-CCM+ , to develop more efficient and innovative solutions, reducing costs and production time. Let's together improve the quality of your project and increase the efficiency of your processes. Get in touch and take your design to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

The Simcenter STAR-CCM+ 2410 release brings major enhancements to accelerate and improve your simulation workflows. It introduces powerful tools for modeling complex physics, such as the new Sub-grid Particle Aging model for accurate battery degradation prediction and advanced SPH surface tension modeling for rapid, accurate lubrication simulations. The release boosts productivity with multi-body instancing, providing faster geometry setup, and speeds up Volume of Fluid (VOF) simulations with a new Dynamic Implicit Multi-Step feature. For automotive aerodynamics and fluid further applications, GPU-accelerated sliding mesh and SPH solvers deliver significant performance gains, now with Windows support for GPUs, making high-speed simulations accessible across platforms. Integration enhancements like Teamcenter Active Workspace support and automatic material assignment streamline workflows, ensuring consistency across simulation projects. With these updates, Simcenter STAR-CCM+ 2410 empowers you to model complexity, explore engineering possibilities, and innovate faster than ever before. Improved fidelity for Battery Cell Degradation Prediction Battery degradation due to mechanical stresses is a significant challenge, as it can lead to reduced capacity and increased internal resistance over time. In Simcenter STAR-CCM+ 2410 , the introduction of two Sub-grid Particle Aging models addresses this issue by simulating local degradation effects, including cracking during lithium cycling. You can now use the “Loss of Active Material” and “Surface Crack Growth” options to understand the specific impacts of mechanical stress on cell performance. This enables you to identify the regions most affected by aging, improving predictions of battery life and reliability. Ultimately, this allows for a more accurate prediction of cell capacity and impedance evolution. Higher fidelity for Powertrain Lubrication simulations Modeling the interaction of liquids with solids presents a key challenge in powertrain lubrication applications. The latest release of Simcenter STAR-CCM+ 2410 addresses this by introducing a surface tension model for Smoothed-Particle Hydrodynamics (SPH), allowing for accurate yet rapid simulation of hydrophilic and hydrophobic fluid-solid interactions. You can now apply the same surface tension workflows used in finite volume methods, enhancing the accuracy of simulations in lubrication scenarios and other applications. This upgrade ensures more precise modeling of liquid-wall interactions, leading to better product performance prediction through higher fidelity in powertrain lubrication simulations with SPH. Leaner modeling of complex CHT problems with Motion Setting up complex Conjugate Heat Transfer (CHT) problems with moving meshes often requires manual mapping of data, which can be time-consuming and error-prone. With Simcenter STAR-CCM+ 2410 , explicit mapped contact interfaces are now compatible with motion, eliminating the need for manual data mappers and Java macros. You can set up advanced CHT simulations, such as turbine blade cooling or E-Machine thermal management, more efficiently with automatic mapping of heat transfer coefficients and reference temperatures. This enhancement allows for leaner and more straightforward modeling of complex CHT problems with motion, saving time and reducing setup complexity. Maximum modeling flexibility for Turbulence Transition Turbulence transition modeling is a research topic in continuous evolution, with numerous variations presented in the literature. Each model offers specific advantages for different industrial applications. Hence, to achieve optimal results for a given use case, customization is often required to enhance prediction accuracy. Simcenter STAR-CCM+ 2410 introduces user-defined source terms for Gamma and Gamma-ReTheta models, providing you with the flexibility to adjust transition behavior for specific industrial applications. This approach enables you to fine-tune simulations for scenarios like turbine blade flows, ensuring more accurate thermal and fluid dynamics predictions. The added flexibility allows you to achieve maximum modeling customization to match your specific needs. Accurate and realistic Colliding Spray shapes for any mesh Accurate spray modeling can be limited by the influence of mesh size and grid topology on collision outcomes, sometimes leading to unrealistic spray shapes. Simcenter STAR-CCM+ 2410 addresses this with a new superior collision detection method that uses cell clusters to identify collision pairs and eliminate mesh-related artifacts. By dynamically re-clustering cells, this solution reduces unrealistic spray patterns caused by mesh dependencies, such as “clover-leaf” artifacts. As a result, you can achieve more reliable predictions of droplet sizes and spray shapes, ensuring accurate and realistic spray shapes even for trimmed meshes. Increased realism for Pharmaceutical and Chemical processing applications Simulating wetting phenomena in pharmaceutical and chemical applications can be challenging due to the need for an accurate representation of liquid-solid interactions. In Simcenter STAR-CCM+ 2410 , a new absorption model for Lagrangian-DEM interactions enables you to model mass transfer from liquid droplets to solid particles. This feature allows for the realistic simulation of processes like tablet coating, where droplet deposition on the surface of solid particles must be resolved. By modeling wetting behavior accurately, you can achieve increased realism in simulations related to pharmaceutical and chemical processing. Quickly and easily model multi-stage solid stress and fluid-structure interaction In complex solid mechanics simulations, dealing with multiple stages of stress and deformation can be challenging, especially when different load conditions and boundary parameters need to be considered. In the latest release of Simcenter STAR-CCM+ 2410 , you can now utilize staged physics and simulation operations to automate multi-stage solid stress and fluid-structure interaction (FSI) cases. This allows you to bundle specific sets of loads and boundary conditions into distinct stages, streamlining the setup process. As a result, you can efficiently model sequential applications such as the deformation of an O-Ring under various conditions, from being stretched onto a piston to achieving full sealing capacity when squeezed between components. By automating these simulation sequences, you save significant time and effort while maintaining the accuracy of complex physical behaviors. Gain deeper insights into your study results Design exploration often generates large datasets that can be difficult to analyze effectively. With Simcenter STAR-CCM+ 2410 , you can perform operations on columns within the Output Table, using expressions to calculate metrics such as averages, sums, or standard deviations. This spreadsheet-like functionality lets you derive reports from any combination of study metrics, giving you a deeper understanding of your results. By enabling the creation of user-defined reports, you can gain insights more quickly and make informed decisions on design optimizations, allowing you to gain deeper insights into study results. Quick and dynamic Qualitative Field Analysis Volumetric CFD data analysis can be complicated by obscuring surfaces that hide important result details. Simcenter STAR-CCM+ 2410 introduces dynamic slicing and clipping for resampled volumes, allowing you to visualize the complete dataset while hiding unnecessary elements. This feature makes it easier to identify areas of interest and understand flow behavior. The ability to slice through the data dynamically enhances your exploration capabilities and supports a deeper understanding of complex phenomena, facilitating quick and dynamic qualitative field analysis. Faster interactive CAD handling with user-defined hotkeys Navigating through the 3D-CAD interface for geometry preprocessing tasks can be time-consuming, impacting productivity. With the new release of Simcenter STAR-CCM+ 2410 , you now have the ability to define custom hotkeys for any 3D-CAD action, providing faster access to frequently used operations. This update not only enhances your workflow but also supports collaboration by allowing the export and import of defined hotkey sets among users. The hotkey table also includes filtering and clash detection features, ensuring seamless navigation and optimal use of your shortcuts. This improved usability in 3D-CAD translates to faster simulation setup, enabling you to explore more design iterations in less time. Increased productivity and reduced memory footprint Managing complex CAD assemblies is time-consuming, particularly when explicitly dealing with multiple instances of the same part. Simcenter STAR-CCM+ 2410 addresses this by introducing multi-body instancing, enabling you to create instances of bodies using pattern or rotation features. Allowing modifications on one instance to be propagated across all instances with ease, this approach reduces geometry preparation time and memory usage. As a result, you can handle large assemblies more efficiently, increasing productivity and reducing memory footprint. Accessible Order of Magnitude speed-up for VOF Simulations Volume of Fluid (VOF) simulations often require long run times, limiting their usefulness for time-sensitive applications. The previously introduced Implicit Time Stepping approach, while offering speed-ups led to variable time steps, which many users try to avoid. With the new Dynamic Implicit Multi-Step feature in Simcenter STAR-CCM+ 2410 , you can now achieve speed-ups of almost two orders of magnitude by using large and constant timesteps without sacrificing accuracy. This improvement is made possible by dynamic sub-stepping, which maintains stability during the simulation. The result is significantly faster simulations, allowing you to achieve accurate results more quickly and making accessible order of magnitude speed-up for VOF simulations. GPU-accelerated sliding mesh for automotive applications To meet CO2 emissions standards while coping with a multitude of vehicle variants, faster simulation tools are needed for external vehicle aerodynamics. Simcenter STAR-CCM+ 2410 introduces GPU-accelerated sliding mesh simulations to cope with rotating wheels on GPUs, providing over 30% faster performance compared to CPU-based methods. This acceleration is crucial for validating CO2 emissions and aerodynamic performance in compliance testing. The improved speed enables you to complete more design iterations in less time. Rapid SPH simulations on single GPUs Smoothed-Particle Hydrodynamics (SPH) simulations in Simcenter STAR-CCM+ were limited to CPU-based calculations. In Simcenter STAR-CCM+ 2410 , we introduce a GPU-native SPH solver which significantly reduces simulation compared to CPU solutions. This allows you to run complex SPH simulations, such as multiphase flow analysis, much faster while maintaining accuracy. The seamless transition and a shared code base between CPU and GPU ensure consistent results, enabling rapid SPH simulations on single GPUs and enhancing your productivity in fluid dynamics simulations. Achieve faster simulations with GPU acceleration on Windows To date the use of GPUs for accelerated simulation runs with Simcenter STAR-CCM+ have been limited to Linux systems. With Simcenter STAR-CCM+ 2410 , GPU-native physics solvers are now available on Windows, bringing significant performance improvements to your workflow. You can now leverage GPU acceleration on your Windows workstation, achieving reductions in simulation time by up to 24 times compared to CPU-only runs. This capability extends support to various physics scenarios while delivering consistent results across both Windows and Linux systems. The ability to unlock such speed enhancements on Windows greatly broadens accessibility, allowing you to solve large-scale problems on more commonly used platforms. Benefit from faster-coupled flow and energy simulations with improved GPU linear solver Even with the adoption of GPUs, the need for faster solver technologies remains critical for complex simulations. The new Simcenter STAR-CCM+ 2410 addresses this by incorporating algorithmic improvements in the GPU linear solver, significantly boosting the performance of coupled flow and energy simulations. You will experience the largest speedups in cases where most of the computational effort is focused on solving linear systems, such as turbomachinery aerodynamics and thermal management of automotive components. This update allows you to complete demanding simulations more efficiently, freeing up valuable resources for additional analyses or higher-fidelity studies. Increased confidence in simulations being Correct and Relevant Adding new parts to complex simulations as a corrective retrofit can disrupt workflows if done in a non-orchestrated manual ad-hoc way. Simcenter STAR-CCM+ 2410 integrates Teamcenter Active Workspace directly, enabling you to add parts and automatically update traceability information within the simulation environment. This integration ensures that you are always using the correct data, fostering collaboration across teams and improving data accuracy in large assemblies. As a result, you can achieve increased confidence in simulations being correct and relevant. Seamless Material Properties assignment from CAD to CFD Manually assigning material properties in large-scale simulations of complex assemblies is tedious and error-prone. The automatic material assignment feature in Simcenter STAR-CCM+ 2410 reads metadata from CAD models, matches it to user-defined material databases, and propagates the material assignments to the physics regions and boundaries set up, streamlining the setup process. This automation reduces errors and ensures consistent material properties across all components, enhancing the reliability of your simulations by allowing seamless material properties assignment from CAD to CFD. Schedule a meeting with CAEXPERTS and discover how the improvements in the new Simcenter STAR-CCM+ 2410 version can transform your simulation processes, increasing your productivity and accuracy. Don't miss the opportunity to explore innovations that will accelerate your analysis and help you tackle complex engineering challenges with faster, more realistic results. WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Discover the power of Solid Edge X — the same Solid Edge you know and love in a secure SaaS environment. You can easily and instantly access your projects either online or offline. With automatic software updates and simplified IT management, Solid Edge X offers a seamless user experience. Simplified IT management is done through centralized cloud license management, aiding in a lower cost of ownership. Solid Edge X is also connected to Solid Edge and other Siemens Xcelerator products by utilizing the same Solid Edge architecture.   Feel secure with Solid Edge X data management and collaboration  The security of your work is never compromised with Solid Edge X . Every seat of Solid Edge X includes built-in, cloud-data management. Having a secure data management system enables you to assign and complete tasks, create and manage revisions and much more during collaborative processes. Improved collaboration among team members improves workflows, working to make collaboration a hassle-free and seamless process by utilizing Teamcenter Share.   Flexible user experience With more and more people working remotely all over the world, the workplace requires more accessible software solutions. Companies now need to provide their employees with a flexible software solution that meets their needs. Solid Edge X enables workers to download and install the software which they will have access to anywhere at any time. This provides a flexible user experience by making the software easily accessible. Solid Edge X works online and offline, ensuring productivity, regardless of internet access. Value-based licensing Value-based licensing allows users to utilize and explore a multitude of Solid Edge portfolio products at a low cost. Solid Edge X works hand in hand with value-based licensing to provide users with the most flexible and seamless software experience. Instead of buying each individual product, you can mix and match as you need. Value-based licensing includes Generative Design Pro, Point Cloud Visualization, Solid Edge Simulation Advanced and many others. Seamlessly work across disciplines Solid Edge X is integrated with a plethora of products in the Siemens Xcelerator Portfolio. Since Solid Edge X is built on the same infrastructure as Solid Edge , the software effortlessly works with NX CAM and Simcenter 3D . AI productivity assistance Solid Edge X has new Artificial Intelligence powered features that offer real-time assistance. The real time assistance provided by AI works to minimize disruptions to your workflows. You can quickly and easily get your questions answers without leaving the Solid Edge X environment thanks to the AI chat copilot. Schedule a meeting with CAEXPERTS  to explore all the new features of Solid Edge X ! Discover how this secure and flexible SaaS environment can transform your productivity and simplify IT management, with advanced AI and cloud collaboration capabilities. Don't miss the opportunity to see up close the possibilities that Solid Edge X offers your company! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

“When I was young, there were no signs of an electric drive or electrified vehicles. Car adverts were about speed and horsepower. Now, they are all about range and zero emissions,” comments Steven Dom, Director, Automotive Industry Solutions at Siemens Digital Industries Software. No, this is not an invitation for speeding. These advertisements from 1985 illustrate how customer requirements have shifted over the years. As electric vehicles (EVs) have changed advertising, they have also changed engineering. “A team of engineers tasked with developing a combustion engine might choose to buy or design a gearbox,” continues Steven. “As long as they meet the vehicle specification, the decision is theirs. That type of solo decision-making is not possible in EVs, where the trend is clearly to go to integrated electric drive units or e-drives in which the power electronics, motor, and transmission system that make up the drive are packaged as one entity. From a manufacturing perspective, it is easier to build one integrated box but to get that package right, there must be an ongoing conversation between each distinct engineering discipline. For some individuals and organizations, this will be an enormous challenge.” Although electric drives are simpler, lighter, and more efficient than traditional engines, their development is technologically challenging. Our integrated approach to electric drive engineering allows for rapid redesign and workflow reuse as requirements change while staying connected to a PLM platform. Managing the challenges of electric drive systems engineering Siemens experts Steven Dom and Benoit Magneville, Electrification Product Manager, addressed all aspects of electric-drive systems development and how organizations can support engineering teams and embrace closer collaboration. As Benoit explains: “The overall aim is to design an electric drive that is highly efficient in a wide range of operating conditions, yet there are many potentially conflicting requirements. Reducing the distance between the inverter and the motor, for example, presents benefits in terms of overall package size, cable weight, and harnessing; however, it creates new thermal and mechanical challenges as the inverter is evolving more restrained way.” Other challenges related to thermal cooling include a critical requirement within a package of heat-producing items. Considering separate cooling systems for each component in an e-drive is not the most efficient approach. Integrating the cooling system for all components will simplify construction, doing away with an array of pipes, pumps, and heat exchangers. Still, it also makes for a more complex engineering task. On top of this, the battery and the passengers compete for effective thermal management, and appropriate cooling will need to be provided. In addition, there is a complex dynamic between meeting operational targets for the e-drive and predicting how noise and vibration are perceived by people sitting in the cabin. From a commercial perspective, passenger comfort is essential to manufacturers, particularly for high-value brands. Addressing Power Electronics design, system integration, and reliability Topology design is one of the early stages of developing an electric drive’s electronics. Key metrics, such as efficiency, cost, tolerance, and EMI suppression, must be understood to define the best topology. Much engineering time can be spent assessing how topology impacts the vehicle and then optimizing based on those results. However, the effort can be wasted if thermal implications are only discovered at the end of that process. Ideally, thermal design and simulation are entirely in sync with topology design and evaluation. The choice of semi-conductor technology is also important. Still, the best decisions cannot be made if you do not know how to identify a semiconductor’s characteristics and compare available options. “The ability to understand junction temperature is key because that defines reliability,” says Benoit. “You cannot just rely on performance ratings from a supplier or on one set of test results.” Evaluating different wide-bandgap (WBG) semiconductors and inverter thermal management systems enables accelerated decisions of inverter technology and thermal design innovation. A thorough and accurate electronics design exploration encompassing PCB (Printed Circuit Board) and Busbar design requires integration with mechanical CAD and electromagnetic, thermal, and structural analysis. The solution is for development to take place within a single environment in which all engineers have easy access to other disciplinary areas, and specialists can interact with each other. From early electric motor sizing up to performance validation One fundamental requirement is that a motor’s lifetime is reliably higher than the vehicle warranty and the vehicle’s lifetime. Thermal design is one of the main ways to improve lifetime and performance. “As usual, success begins with the design phase,” notes Benoit. “Electric motor requirements are cascaded down from EV performance targets. The best way to obtain fast and accurate motor sizing and configuration is to quickly evaluate multiple design types and topologies against electro-magnetic efficiency, thermal and thermal and vibro-acoustics performance while still in the architectural phase.” The video below demonstrates an axial fluw machine workflow, performed in Simcenter E-Machine Design . Axial flux machine workflow The Simcenter portfolio connects all these areas, enabling an assessment of how motor sizing and design impact the entire vehicle. In the initial stages, when the design only exists as a set of operational requirements, Simcenter offers an extensive library of motor templates and more than 200 materials. This opens the possibility of identifying a completely new motor architecture that will fulfill targets and generate the best thermal cooling system. Any virtual model can be tested and validated simply by exporting it into Simcenter Amesim . Maximizing the efficiency of the electric drive transmission From an operational point of view, the challenge is to maximize transmission system efficiency while minimizing weight and combining it with the rest of the drive within packaging limits. It is essential to assess gear contact stresses, bearing forces, and shaft flexibility so that noise and vibration from the rotating gear in the gearbox can be accurately predicted. Again, this means designing against multiple attributes, including durability and oil supply for lubrication. Manufacturers want to create lighter vehicles and may consider using new materials, yet these bring specific challenges because they are not always fully proven. Another factor is budget. The cost of prototyping a single gear can be up to $200,000 US. Hence, performance needs to be thoroughly evaluated, and any failure or weakness promptly addressed before a capital investment is made. Want to streamline the development of electric drive systems and maximize the efficiency of your projects? Schedule a meeting with CAEXPERTS to discuss how our integrated approach can transform your engineering. Our experts are ready to help your team tackle the challenges of automotive electrification, from systems integration to thermal and vibro-acoustic design. Contact us today! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br