Welcome to the 3rd and final part of our special series of technical posts about computer simulations in engineering! If you want to have a complete overview of the project, check out the first part about CFD modeling  and the second about FEA analysis . In the first part, we detailed the multiphysics modeling and CFD simulation of a fuel cell using Simcenter STAR-CCM+ , while in the second part we did the modeling and structural analysis of a proton exchange membrane fuel cell (PEMFC) using Simcenter 3D . Case Study In the continuation of our series on fuel cell validation, we come to the third part, where we explore the simulation of fuel cells at the system level, that is, how they would operate integrated with other equipment and enable the analysis of their performance under different conditions. Unlike previous analyses focused on more detailed simulations, here we represent the behavior of the cell through a set of 1D equations simulated in Simcenter Amesim  software. This approach allows the integration of the cell model into a vehicle system. System simulation is a crucial step in understanding how a fuel cell behaves when incorporated into a larger system, such as an electric or hybrid vehicle. In this phase, the equations that govern the behavior of the fuel cell are solved together with the equations that describe the rest of the vehicle system. This approach provides a more holistic view of fuel cell performance in real-world operating scenarios. Furthermore, the systems approach simplifies fuel cell behavior without compromising the accuracy of the results. In this approach, key parameters such as energy production, fuel consumption and efficiency are represented by differential equations that capture the essentials of the cell's operation. Modeling Integrating a fuel cell stack into a vehicle system represents a significant challenge. Indeed, a fuel cell system encompasses a variety of components, such as the stack itself, as well as the auxiliary Balance of Plant (BOP) equipment, which includes the cooling circuit, the air and hydrogen supply systems, the humidifier, among other devices necessary for the proper operation of the cell. In addition, multi-physical phenomena are involved, including electricity, heat transfer, fluid flow, mechanical (inertial) resistances and electrochemistry. In this model, only the electrical aspect of the system was considered, which is the main focus of this study. This allows us to answer questions such as: Will the proposed fuel cell system offer a significant efficiency improvement compared to other conventional or hybrid vehicle configurations? What is the driving range of the fuel cell vehicle for a given duty cycle? Systemic modeling includes sets of differential equations that characterize the dynamic and steady-state behavior of fuel cell elements. These equations adopt different approaches to describe cell behavior and can be divided into quasi-static and dynamic models, depending on the phenomena involved. The results obtained in the Simcenter STAR-CCM+  software for the behavior of a single cell were extrapolated to a stack of cells. This stack was modeled as a stack of 200 cells connected in series, operating at a total voltage of 100 V. Each individual cell uses the polarization curve derived from the previous simulations. Polarization curve of a fuel cell obtained in the Star-CCM+ software  and imported into Amesim A relevant study in this context is the experimental scalability study carried out by Bonnet et al. [2008], which explores the extent to which a single cell or a reduced set of cells can faithfully represent a larger system. This study is especially useful for determining which experimental data from individual cells are still applicable at full scale, including operating data under conditions that are potentially adverse to the cell's durability. The main conclusions of the study indicate that: The polarization curves are nearly identical at different scales, suggesting that the scale effect is minimal under ideal conditions. Under varying air and hydrogen flow conditions, experiments with single cells and stacks show similar behaviors. The degradation effects with operating time follow similar trends at the different scales analyzed. The study on the impact of air humidification is not conclusive: at low relative humidity, the behavior of the cells is similar, but above 60% RH, significant differences appear. Integration with the Vehicle System Once the fuel cell has been modeled, the next step is to integrate it into the vehicle system model. Here, the interactions of the fuel cell with other vehicle components, such as the drivetrain, batteries, and control systems, are considered. The simulation allows predicting how the fuel cell will respond to different driving profiles, including variations in power demand, temperature, and other environmental conditions. Schematic representation of the vehicle system integrated with the fuel cell. The simulation was performed with a lightweight vehicle weighing 1928 kg operating at a fixed torque conversion ratio of 1:8.786. The fuel cell was sized to deliver 88 kW, supplemented by a 1.5 kWh battery. Detailed system information and the corresponding model can be seen in the figure below. Vehicle system model and system information in Simcenter Amesim The driving cycle used in this simulation was the Japanese Cycle 08 (JC08) normalized cycle . The test represents driving in congested urban traffic, including periods of idling and frequent alternations of acceleration and deceleration. It is used for emissions measurement and fuel economy determination. The parameters selected for the JC08 cycle include: Duration:  1204 s Total distance:  8,171 km Average speed:  24.4 km/h (34.8 km/h excluding idling) Top speed:  81.6 km/h Load ratio:  29.7% The velocity curve along the JC08 cycle Source: https://dieselnet.com/standards/cycles/jp_jc08.php Results: Performance Analysis under Operating Conditions Integrating the fuel cell model into the vehicle system enables performance analysis under a variety of operating conditions. For example, system efficiency can be assessed during sudden acceleration, regenerative braking, and steady-state operation. These scenarios provide valuable data for model validation and system design refinement. Plot of simulated speed versus driving cycle It can be observed that the simulated speed follows the driving cycle, indicating that the traction system is sized appropriately. Furthermore, in this same cycle, we can observe consumption and acceleration characteristics, as well as extrapolate the average consumption to define the vehicle's autonomy. This autonomy calculation only considers the use of the fuel cell, without taking into account the potential use of the battery for vehicle propulsion when the fuel tank is empty. Representation of the main characteristics of the system during the JC08 cycle This analysis also includes the transient behavior of the system in terms of consumption and battery charge status. Fuel consumption during the driving cycle Evolution of the battery charge state during the driving cycle The following graph shows the power control of the power bus. For lower power demands, power is supplied by the battery. When power demand is higher, the fuel cell supplies the power. During regenerative braking, power is directed to the battery for charging. Power distribution between fuel cell and battery Conclusion System simulation is a powerful tool that complements the detailed analyses performed in the previous steps. By integrating the fuel cell into a vehicle system, we can obtain a more complete and accurate view of its behavior under real-world conditions. This approach enables the development of efficient and reliable propulsion systems. This analysis reinforces the importance of validating fuel cell performance not only at the component level, but also in its final application. Want to learn more and in more detail?  Schedule a meeting or contact CAEXPERTS  through our communication channels  to discuss how we can collaborate in the optimization and validation of your project, integrating innovative solutions that increase performance in real conditions. Our team is ready to offer the necessary support to transform your simulations into concrete results. Also, follow our LinkedIn page @CAEXPERTS  for more insights and news! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br Reference Bonnet, C., Didierjean, S., Guillet, N., Besse, S., Colinart, T., & Carré, P. (2008). Design of an 80kW PEM Fuel Cell System: Scale Up Effect Investigation. Journal of Power Sources, 182(2), 441–448. DOI: https://doi.org/10.1016/j.jpowsour.2007.12.100 .

Welcome to part 2 of our special technical blog series on computational simulations in engineering! If you haven’t already checked out part 1 on CFD modeling, we recommend checking it out here  for a complete overview of the project. In part 1, we detailed the multiphysics modeling and CFD simulation of a fuel cell using Simcenter STAR-CCM+ . FEA Case Study In this second part of the series, we will focus on the modeling and structural analysis of a proton exchange membrane fuel cell (PEMFC). Using Solid Edge  software for CAD modeling and Simcenter 3D  for finite element analysis (FEA), we seek to validate the structural robustness and mechanical resistance of the cell under various operating conditions. Simcenter 3D  is a simulation tool that allows the integration of several physics in a single model, as in the case of a PEMFC where there are pressure and temperature fields imported from STAR-CCM+  and also application of bolt tightening. As a reminder, to validate the CFD model, we used the JRC ZERO∇  CELL (BEDNAREK et al., 2021), chosen for its reliable technical documentation and the availability of experimental data at its source, which is a technical report from the Joint Research Centre   (JRC) (Figure 1). The JRC is the science and knowledge service of the European Commission, responsible for providing scientific and technical support to European Union policymaking by developing and providing methods, models, and data. Figure 1 – Excerpt from the Joint Research Centre technical report on the JRC ZERO∇CELL Source: Adapted from BEDNAREK et al. (2021) The availability of technical drawings of the cell geometry (Figure 2), the materials used and some conditions of use also favored its choice. Figure 2 – Technical drawing of the JRC ZERO∇CELL assembly, together with the description of the cell parts Source: Adapted from BEDNAREK (2021) 1 Modeling This topic will explain and discuss the most relevant points of FEA modeling. The simulation was developed based on the data and conditions provided in the article. In summary, the steps of the FEA study were as follows: Generation of the complete geometry of the problem; Adaptation of geometry for FEA analysis; Definition of boundary conditions; Generation of the computational mesh (division of bodies into small elements); Execution of the model and verification of results; If the results are not consistent, steps two, three and four are reviewed; If the results are consistent, they are then processed. Next, the modeling will be divided into topics and further detailed. 1.1 Geometry The PEMFC geometry (Figure 3) was developed based on the technical drawings of BEDNAREK (2021), referring to JRC ZERO∇CELL. For the FEA analysis, it was necessary to model all the parts and geometries provided by the document, since all of them will have an impact on the cell's stress and sealing results. However, small details were removed, such as aesthetic or assembly chamfers and very small gas flow channels, aiming at a simplification of the mesh. Figure 3 – Geometry (Isometric View) Figure 4 – Geometry (Side View) 1.2 Boundary Conditions The boundary conditions in a structural simulation are definitions that specify the forces acting on the system and the way in which that system is fixed in space. In addition, it is necessary to choose the materials for each component with their respective mechanical properties – and thermal properties, as in this case. 1.2.1 Restrictions As restrictions, we chose a fixation condition on all axes of the lower face of the cell, since the article does not specifically mention how the cell was fixed and we are focused on the sealing efficiency of the system, that is, we do not need to worry about the accumulation of tensions on the lower face or problems of excessive restriction of the model. The fixation face is made explicit below. Figure 5 – Model fixing condition 1.2.2 Materials The materials used were chosen based on the data provided by the article and using the materials from the standard Simcenter 3D  library, applying these to the meshes of their corresponding parts. Below is an image for each material used, showing their respective parts and then their properties. Figure 6 – Steel Parts Steel properties: Density: 7829 kg/m³ Modulus of Elasticity: 206940 MPa Poisson's ratio: 0.288 Coefficient of Thermal Expansion: 1.128e-05 1/Cº Figure 7 – AW2024T3 parts AW2024T3 properties: Density: 2794 kg/m³ Modulus of Elasticity: 73119 MPa Poisson's ratio: 0.33 Coefficient of Thermal Expansion: 2.16e-05 1/Cº Figure 8 – Bronze Pieces Bronze properties: Density: 8852 kg/m³ Modulus of Elasticity: 103400 MPa Poisson's ratio: 0.34 Coefficient of Thermal Expansion: 1.782e-05 1/Cº Figure 9 – Rubber Parts Rubber properties: Density: 1200 kg/m³ Modulus of Elasticity: 900 MPa Poisson's ratio: 0.4 Coefficient of Thermal Expansion: 0 1/Cº 1.2.3 Efforts In the structural simulation, we will have two times, the first applying the preload of the 4 bolts and the second applying the temperature and pressure conditions provided by the CFD analysis. To tighten the bolts, the data provided in the article was used and a strategy was adopted to apply this force properly. In Simcenter 3D , there is a loading called “ Bolt Pre-Load  ”, that is, bolt pre-load. In this loading, it is possible to apply a force on a given axis by choosing a face, for example, so that this face will be compressed on the chosen axis. Therefore, the bolt was cut in half transversally and another Simcenter 3D  feature was used , called “ Mesh Mating  ”. This feature unifies meshes of separate bodies, connecting the nodes so that they become coincident, practically unifying the meshes of the chosen bodies. Therefore, using “ Mesh Mating  ” for each screw cut in half and applying “ Bolt Pre-Load  ” to the faces generated by the cut, the screws are tightened from this face given the force applied. Below you can better understand the procedure adopted. Figure 10 – Cut screw Figure 11 – “Bolt Pre-Load” Moving on to the second part of the analysis, the results obtained in the CFD analysis were imported in .csv format, which in Simcenter 3D  was transformed into a cloud of tabulated points for both temperature and pressure. Figure 12 below shows the pressure and temperature fields applied to the system. In it, it is possible to see the meshes in which the conditions were applied, the small red arrows indicating the pressure field and the blue region that is under the temperature conditions extracted from the CFD analysis.   Figure 12 – Pressure and temperature fields 2 Results As a result, the contact pressure between the plates around the membrane and the fatigue resistance of the system can be highlighted. 2.1 Contact Pressure To assess the cell's sealing efficiency, it is necessary to analyze the forces acting to prevent the plates from losing contact. The preloading on the screws and the contacts between the plates were considered. We can evaluate the pressure involved in these contacts and compare them with the results obtained in the article to validate the model. Figure 13 – Interface contact pressure Figure 14 – Contact pressure 2.2 Fatigue Resistance To analyze fatigue resistance, it is necessary to simulate that the load imposed in the static analysis will be applied repeatedly to the system. To do this, we use the “ Durability  ” tool in Simcenter 3D . In this analysis, we use the yield strength of each material as a reference to calculate the safety factor. Figure 15 and Figure 16 below show the safety factor of the system, which remained above 2, and in Figure 17 the life of the components, which was shown to be infinite (>1e+9). Figure 15 – Safety factor (Isometric View) Figure 16 – Safety factor Figure 17 – Life (infinite) 3 Conclusion With this project, it was possible to accurately digitally reproduce the PEM  fuel cell model , demonstrating the capabilities of Simcenter 3D  to integrate with STAR-CCM+  for more complex analyses and obtain physical results consistent with those observed in the real world. In addition, it was possible to ensure that for the conditions tested, the cell has an excellent safety coefficient against fatigue failure.   to integrate with STAR-CCM+  for more complex analyses and obtain physical results consistent with those observed in the real world. In addition, it was possible to ensure that for the conditions tested the cell has an excellent safety coefficient against fatigue failure. STAR-CCM+  and Simcenter 3D  Integration   allows the fuel cell design to be complete, allowing topological optimizations based on the cell's operating data, in order to guarantee structural resistance and tightness without running the risk of failures.  allows the fuel cell design to be complete, allowing topological optimizations based on the cell's operating data, in order to guarantee structural resistance and tightness without running the risk of failures. Want to know more and in more detail? Schedule a meeting with us now or contact us through one of our means of communication! In the next post we will present the systemic simulation of the integration of the fuel cell in a hybrid vehicle in Amesim , based on the integration of the results obtained in STAR-CCM+  and Simcenter 3D ! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br 4 References BEDNAREK, Tomasz et al. Development of reference hardware for harmonised testing of PEM single cell fuel cells. 2021. BEDNAREK, Tomasz (2021), “The JRC ZERO∇CELL design documentation”, Mendeley Data, V1, doi: 10.17632/c7bffdv7yb.1

Welcome to our special series of technical posts on computational simulations in engineering! Over the course of three parts, we will explore the full complexity of a fuel cell design, validated with real tests, demonstrating the power of CFD (Computational Fluid Dynamics), FEA (Finite Element Analysis) and systemic simulation tools for the integration of a fuel cell into a hybrid vehicle, aiming to solve complex challenges. The first case study details the multiphysics modeling and CFD simulation of a fuel cell using Simcenter STAR-CCM+ software.   CFD Case Study This report aims to detail the 3D modeling of a proton exchange membrane fuel cell (PEMFC), seeking to understand the model and validate the approach. In this work, the Simcenter STAR-CCM+  software was used to perform the modeling. Simcenter STAR-CCM+  is a simulation tool that allows the integration of several physics in a single model, such as in the case of a PEMFC where there is fluid flow, heat transfer, chemical and electrochemical reactions. To validate the model, hardware from a single reference PEMFC was sought, with tests available in the literature. The fuel cell chosen for model validation was the JRC ZERO∇  CELL (BEDNAREK et al., 2021), a PEMFC with 24 parallel gas channels at the anode and cathode. Its choice was due to the reliability of its source, which is a technical report from the Joint Research Centre   (JRC) (Figure 1), which is the science and knowledge service of the European Commission. The JRC is responsible for providing scientific and technical support to European Union policymaking by developing and providing methods, models, and data. Figure 1 – Excerpt from the Joint Research Centre technical report on the JRC ZERO∇CELL Font: Adapted by BEDNAREK et al. (2021) The availability of technical drawings of the cell geometry (Figure 2), the materials used and some conditions of use also favored its choice. Figure 2 – Technical drawing of the JRC ZERO∇CELL assembly, together with the description of the cell parts Source: Adapted from BEDNAREK (2021) Furthermore, the provision of some structural data necessary for the structural analysis stage of the cell was also taken into account. 1 Modeling This topic will explain and discuss the most relevant points of CFD modeling. The model was developed in Simcenter STAR-CCM+  software, a powerful multiphysics computer simulation software from Siemens. Briefly, the steps of the CFD study were as follows: Generation of the complete geometry of the problem; Adaptation of geometry for CFD analysis; Selection of physical and chemical models, together with equations; Generation of the computational mesh (division of bodies into small elements); Execution of the model and verification of results; If the results are not consistent, steps two, three and four are reviewed; If the results are consistent, they are then processed. Next, the modeling will be divided into topics and further detailed. 1.1 Geometry The PEMFC geometry (Figure 3) was developed based on the technical drawings of BEDNAREK (2021), referring to JRC ZERO∇  CELL. For the CFD analysis, the focus was on the bipolar plate  (BP) , gas diffusion layer  (GDL) , catalyst layer  (CL) ,  membrane and gas passage channels. In addition, small details were removed to simplify the mesh. The changed details were: removal of chamfers on the edges, removal of screw holes and removal of rounded chamfers from the BP channels (Figure 4), through which the gases pass.   Figure 3 – Geometry of the JRC ZERO∇CELL PEMFC drawn in STAR-CCM+ – The leftmost figure shows the complete cell structure; the rightmost figure shows the cathode half of the cell, together with the GDL and the membrane Figure 4 – Representation of BP channels for CFD analysis Figure 5 – PEMFC with 24 channels – Cathode side Figure 6 highlights the channels through which the gases pass. For the CFD analysis, only the channels were considered as the volume through which the gases pass. Figure 6 – PEMFC – Highlighting the gas volume on the cathode side 1.2 Model Physics In this topic, the physics considered in the model will be discussed, together with the electrochemical reactions. The model was studied in the steady state (without variations in relation to time) and in three-dimensional space. 1.2.1 Bipolar Plate The bipolar plate's main functions were to determine the path of gases, conduct electric current and generate a difference in electric potential. The BP (Bipolar Plate) was considered as a solid with constant density, high electrical conductivity of 125,000 S/m and the other physical properties, for example thermal conductivity, as being composed mainly of graphite (as is the case of the BP of JRC ZERO∇  CELL). Furthermore, the heating generated due to the passage of electric current through the solid (ohmic heating) and electromagnetic effects were considered. On the extreme surface of each of the bipolar plates (Figure 7), an electrical potential condition was imposed. On the extreme surface of the anode, a potential of 0 volts was maintained. On the extreme surface of the cathode, different values ​​were placed, appropriate to the operational range  of the cell, varying from approximately 0.3 to 0.95 volts. Figure 7 – PEMFC – Anode surface where the electric potential is imposed 1.2.2 Gas Phase The gas phase flows in the channels generated by joining the BP with the GDL (Gas Diffusion Layer) (Figure 8). Figure 8 – Channels through which gases flow The gases in the system were considered to be oxygen (O₂), hydrogen (H₂), nitrogen (N₂) and water vapor (H₂O). The physical properties were considered to be those of the mixture of gases (changing at different concentrations of each gas) and the thermodynamic model of ideal gases was used. In addition, it was considered that the gases flow in the laminar regime. Regarding electrical conductivity, it was considered that the fluid is non-conductive, adopting a value of 0 S/m. 1.2.3 Gas Diffusion Layer The GDL (Gas Diffusion Layer) was modeled as a porous region, where anode and cathode gases diffuse into their respective GDLs. Laminar flow, ohmic heating resulting from the passage of electric current through the porous solid, electromagnetic effects, electrochemical reactions, heating generated by chemical reactions, together with the inertial and viscous resistances of the porous region were considered. It was also considered that the solid phase of the porous region has electrical and thermal conductivity equal to 50,000 S/m and 24 W/mK, respectively. Regarding the geometric definition of the pores, 2 parameters were taken into account: Porosity:    is the measure of the amount of empty space (pores) in the porous region, expressed as a fraction of the total volume of the material. In other words, it is the proportion of the total volume of a material that is occupied by empty spaces. Tortuosity:  is the measure of the complexity of the paths that fluids must travel through the pores of the material. In other words, it is the relationship between the actual distance that a fluid travels along the pores and the direct distance (shortest distance) between two points in the porous medium. According to the reference article (BEDNAREK et al., 2021), the GDL used was the “ SIGRACET GDL 25 BC ”. According to technical data for this GDL (SIGRACET, 2024), the porosity of the material is 0.8. In the model studied, porosity and tortuosity values ​​consistent with those found in the literature were used. The idea was to verify the influence of porosity and tortuosity on the model and to adapt the experimental polarization curve to the numerical one. 1.2.4 Catalyst Layer The porous CL (Catalyst Layer) is assumed to be infinitely thin and is not geometrically resolved. The anodic and cathodic reactions are represented by two-dimensional electrochemical reactions, which occur at the interface surface between the proton exchange membrane and the gas diffusion layers. In other words, an infinitely thin catalytic layer (platinum in the vast majority of cases – such as the JRC ZERO∇CELL) is assumed to be present at the interfaces between the GDLs  and the membrane. An electrochemical reaction model was used to simulate the potential variations at this reaction interface. The production and consumption of multicomponent gases due to electrochemical reactions are automatically calculated. 1.2.4.1 Reactions Hydrogen is supplied to the anode, where it diffuses into the GDL. When the hydrogen reaches the interface with the membrane, where the catalyst is located, it reacts, splitting into hydrogen ions (protons) and electrons (Equation 1). This is the main reaction that occurs at the anode. Electrons travel from the anode to the cathode through an external circuit, while hydrogen ions pass through the proton exchange membrane from the anode to the cathode. Oxygen is supplied to the cathode, where it also diffuses into the GDL. In the presence of electrons, oxygen forms oxygen ions. Hydrogen ions and oxygen ions react at the interface of the cathode GDL with the membrane, forming water and releasing heat. The overall cathodic reaction is described by Equation 2. The anodic and cathodic reactions complement each other by consuming and producing ions and electrons in a conservative manner. Anodic and cathodic reactions complement each other by consuming and producing ions and electrons in a conservative manner. The reaction current on the anode and cathode sides of the fuel cell is calculated using the Butler-Volmer equation, which considers several factors to determine the electrochemical reaction rate. The exchange current density at the electrode is a variable parameter, which depends on the materials used in the GDL and membrane, together with the type and concentration of catalyst in each of the CLs. Therefore, this parameter was adjusted in order to adjust the polarization curve of the model with the experimental polarization curve. 1.2.5 Membrane The anode and cathode are separated by a polymeric membrane, such as Nafion. Nafion is composed of long polymeric molecules with functional cations in their chains. The cations present in the chains are responsible for absorbing protons and water for transport through the membrane. Thus, the proton exchange membrane in this model is modeled to replicate the properties of Nafion, which allows the movement of positive ions through its pores. The membrane was modeled as a solid material using a solid ion model, which allows the transport of ions through the membrane geometry. Constant density, heating due to chemical reaction, ohmic heating, and electromagnetic effects were considered. 1.3 Boundary Conditions Boundary conditions in a computer simulation are definitions that specify the behavior of fluid and solids at the boundaries of their domains. Boundary conditions provide the information needed to define how the fluid enters, exits, and interacts with surfaces within the simulation domain, along with the behavior of solids. As inlet conditions for the anode and cathode gases, the operating conditions provided in BEDNAREK et al. (2021) were followed (Figure 9). Figure 9 – JRC ZERO∇CELL operating conditions Font: Adapted by BEDNAREK et al. (2021) Where the anode gases are composed of H₂ and H₂O and the cathode gases are composed of air (considered as approximately 22% O₂ and 78% N₂, in mass percentages) and H₂O. As already mentioned, on the extreme surface of each of the bipolar plates (Figure 7), the electrical potential conditions were imposed. Where, on the surface referring to the anode, a potential of 0 volts was maintained; and on the surface referring to the cathode, different values ​​were placed, appropriate to the operational range   of the cell, varying from approximately 0.3 to 0.95 volts. It was considered that the cell walls, with the excess of the surfaces where electrical potential conditions are placed, are thermally isolated from the external environment. Therefore, the only cooling that the cell has is that of the flow of reactant gases itself. On the surfaces that are not adiabatic, a fixed temperature value equal to 85°C was considered, the gas inlet temperature of the JRC ZERO∇CELL. 2 Results As results, the temperature profile, the pressure profile and the polarization curve can be highlighted. 2.1 Temperature To analyze the temperature, the cathode electrical potential was set at 0.52 V. As a result, we have the temperature at the ends of the BPs (Figure 10), where it can be observed that the temperature remained constant and with the value of 85°C, a temperature that was imposed at the end with the largest area. This behavior is expected due to the proximity of the side walls to the imposed temperature condition.  Figure 10 – Temperature at BP Figure 11 and Figure 12 represent the temperature in two different cutting planes, showing the heat generation with the chemical reaction. Figure 11 – Temperature in BP cutting plane Figure 12 – Temperature in BP cutting plane These cell temperature representations show the importance of adequate cooling. The 85°C condition, as expected, was not sufficient to maintain the cell core at its ideal operating temperature of 80°C. 2.2 Pressure Figure 13 represents the pressure at the anode gas inlets and outlets. The blue channels (lower pressure) represent the gas outlet surface; the red channels (higher pressure) represent the gas inlets. This shows a pressure drop of approximately 8 kPa. Figure 13 – Pressures at channel inlets and outlets Figure 14 represents the pressure at the cathode gas inlets and outlets. The blue channels (lower pressure) represent the gas outlet surface; the red channels (higher pressure) represent the gas inlets. This shows a pressure drop of approximately 4 kPa. Figure 14 – Pressures at channel inlets and outlets 2.3 Polarization Curve The polarization curve consists of a plot of current density versus cell voltage, where current density is the cell current divided by its active area (membrane area). The reference article (BEDNAREK et al., 2021) provides the polarization curve (Figure 15) for the cell studied (in Figure 15 it is a constant blue line curve). An extraction of the points from the curve (Figure 16) was performed to enable comparison with that generated by the model. Figure 15 – Polarization curve of the JRC ZERO∇CELL Figure 16 – Generation of the polarization curve by extracting the points from the JRC ZERO∇CELL polarization curve Therefore, the values ​​of the exchange current density at the anode were adjusted to 4.125*10⁸ A/m² and the exchange current density at the cathode to 1792.5 A/m² in order to adjust the polarization curve generated by the model with the reference curve. The porosity was set to 0.8, according to the real value of the porosity of the GDL   used. Thus, the polarization curve shown in Figure 17 was obtained. Figure 17 – Comparison of the reference polarization curve (in blue) and the one calculated by the model (in orange) 3 Conclusion With this project, it was possible to accurately digitally reproduce the PEM fuel cell model, demonstrating the ability of Simcenter STAR-CCM+  to work with complex multiphysics simulations and obtain physical results consistent and accurate with those observed in the real world. In this work, the STAR-CCM+  model proved to be validated and efficient for this purpose. Despite the possibility of performing co-simulations with Simcenter AMESIM , due to the high computational cost of modeling in STAR-CCM+ , the best alternative is to use the polarization curve, electrochemical data and geometric parameters of the cell obtained by CFD simulation in AMESIM . This way, the analysis will be faster. In this way, the optimization of computational resources is guaranteed without compromising the accuracy and reliability of the results obtained in the simulations. Thus, it will be possible to predict the influence of changes in geometric or process parameters quickly and with better cost-benefit, in an innovative way. Want to know more and in more detail? Schedule a meeting with us now or contact us through one of our means of communication! In the next post we will present the structural analysis of the fuel cell in Simcenter 3D , based on the integration of the results obtained in STAR-CCM+ ! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br 4 References BEDNAREK, Tomasz et al. Development of reference hardware for harmonised testing of PEM single cell fuel cells. 2021. BEDNAREK, Tomasz (2021), “The JRC ZERO∇CELL design documentation”, Mendeley Data, V1, doi: 10.17632/c7bffdv7yb.1 SIGRACET. GDL 24 & 25 Series Gas Diffusion Layer. Fuel Cell Store. Disponível em: < https://www.fuelcellstore.com/spec-sheets/SGL-GDL_24-25.pdf >. Acesso em: 23 maio 2024.   5 Additional References BEDNAREK, Tomasz; TSOTRIDIS, Georgios. Assessment of the electrochemical characteristics of a Polymer Electrolyte Membrane in a reference single fuel cell testing hardware. Journal of Power Sources, v. 473, p. 228319, 2020. BEDNAREK, Tomasz; TSOTRIDIS, Georgios. Comparison of experimental data obtained using the reference and the single-serpentine proton exchange membrane single fuel cell testing hardware. Data in Brief, v. 31, p. 105945, 2020.

New updates for NX in 2024. Tune in to this year’s annual What’s New in NX  premiere to learn about all the latest and greatest features and enhancements added through the continuous release cycle of NX  software. Featuring cloud SaaS products like NX X and Zel X, generative and AI-enabled design tools, and the future of immersive design. NX X - Cloud-based product engineering em Access NX ’s industry-leading product engineering resources  in the cloud with NX X. During the premiere, we’ll first show you how you can leverage the cloud for your CAD workflows. Centralized cloud license management reduces IT complexity to give you extra flexibility. You can install NX X on your desktop or even stream the software in your browser via AWS cloud services. Fully integrated and secure data management lets you share and collaborate with colleagues and partners directly within the NX X interface. Built on Siemens’ Teamcenter X software and as part of Siemens Xcelerator as a Service, NX X makes it easier than ever to level up your product lifecycle management (PLM) capabilities. Flexible and scalable licensing Get the most out of NX  and NX X with value-based licensing. Add-on modules already add additional capabilities to the core NX  functionality, tailored for specialized and advanced use cases. Value-based licensing provides flexible, scalable, and affordable access to these modules when and how you need them. The way it works is simple. You get a pool of tokens, and each additional module costs a certain number of tokens to use. These tokens are "taken out" as you use a module, and returned to your pool for use in another module when you're done. You start with enough tokens to cover your current module needs, and then easily add more as your organization grows or your engineering needs evolve. Almost all of the functionality demonstrated in this year’s video is available through value-based licensing. All you need to do to access the new features with your existing tokens is upgrade to the latest version of NX ! Cross-domain collaboration Collaborate in innovative ways with NX X, value-based licensing, and powerful Siemens Xcelerator integrations. Integrated data management in NX X means seamless change management and release workflows, while value-based licensing gives every expert the tools they need, when they need them. This year’s video also highlights how NX ’s interoperability with other Siemens Xcelerator solutions ensures efficient communication throughout the product lifecycle.   We show you how you can integrate Zel X, Siemens’ web-based engineering platform, into your design and manufacturing workflows, giving every team cost-effective access to the right level of functionality. Also noteworthy is the Managed Environment for Electronics Design, which brings together a set of Siemens Xcelerator products, such as NX , Teamcenter and Xpedition. Visualize mechatronic animations in high fidelity Mechatronics Concept Designer is an NX  add-on module that allows you to model, simulate, and validate machine designs with multi-body physics and automation-related behavior.   Now you can see accurate simulations of your designs in motion with NX Immersive Explorer, based on data from Mechatronics Concept Designer. AI-enabled design Improve and accelerate your processes by leveraging AI-enabled design tools in NX . A range of AI-powered design and simulation capabilities are introduced, such as Topology Optimization, Performance Predictor, and Gyroid modeling. Tools like these bring together AI capabilities like Command Prediction and Selection Prediction that can significantly increase efficiency in your routine workflows. Generative and AI-enabled design Tools like Topology Optimization, Lattice Designer, and Implicit Modeling don’t just automate workflows. They can optimize your designs and create complex geometry beyond the capabilities of traditional CAD methods. Watch the video below to see these add-on modules in action as part of a generative design workflow, or read on for more details. Topology optimization You can now watch the optimization process generate geometry live in the Graphics Window. This is a very intuitive way to assess progress and choose a suitable point to complete the optimization. Simply select View in the Solution Progress Monitor. Topology Optimization can now also automatically create blends between building bodies and the design space based on the specified voxel size. Choose the Auto-transition Blend option in the Building Body dialog box. You can also optimize for a wider range of manufacturing techniques. The Multi-Axis Tooling option in the Shape Constraint dialog now supports 5-axis milling. Implicit Modeling Two new commands have been added to Implicit Modeling in this new version: Set Resolution  – Use this to set the voxel size for any selected implicit bodies. Unit Cell  – Creates a triply periodic body with one body, for use by the Body Lattice command outside the Implicit Modeling task environment. The Unite command also has some new functionality. You can now choose between three types of blending between the target and tool bodies – Continuous, Circular, or Angular. Lattice Designer When creating Voronoi networks, you can now specify which pore size distribution to use. Uniform, variable and Gaussian pore distributions are available. The Filter Lattice command now offers even more flexibility. You can filter lattices by thickness or aspect ratio. There’s also the all-new Connect Dangling Rods command. It automatically connects the open ends of hanging rods to help prevent issues during additive manufacturing. You can choose a maximum rod length, a maximum number of rods per connection, and use a closest or random distribution. COMING SOON: NX Immersive Designer Get ready to experience 3D CAD in the industrial metaverse. Design while you’re in the room with your model. Take innovation to new levels. Learn more about the upcoming NX Immersive Designer software and the Sony XR head-mounted display, tailored for NX  controllers. Watch the full video of what's new in NX in 2024 Don’t miss out on the opportunity to drive innovation and efficiency in your organization! Schedule a meeting with CAEXPERTS  and discover how the new NX  updates in 2024, including NX X cloud products, can transform your design and engineering processes. Our expert team is ready to show you how cloud-based product engineering, flexible licensing, and AI-enabled design tools can take your projects to the next level. Contact us today!

NX is Siemens’ comprehensive CAD software that meets the design, engineering, and manufacturing needs of modern industries. This article details how NX can boost your business by providing an in-depth look at its features, benefits, and applications. Based on our experiences with Solid Edge and exploring the innovations of NX , we’ll explore how this tool can transform your operations. 1. NX Overview NX is an integrated CAD, CAM, and CAE solution that delivers flexibility, efficiency, and productivity across all  phases of product development. It is widely used in industries such as automotive, aerospace, industrial machinery, and electronics due to its ability to handle complex designs and large data sets.   2. Main Features a. Generative Design NX enables you to explore innovative and optimized design solutions by  utilizing advanced algorithms to automatically generate multiple design options that meet performance and manufacturing requirements. This not only speeds up the design process, but also opens up new creative possibilities that may not be obvious at first glance. b. Convergent Modeling With convergent modeling, you can combine traditional modeling with faceted meshes, creating complex designs with ease and flexibility. This capability is especially useful for integrating 3D scan data and quickly adapting existing models. c. Modeling Complex Surfaces NX provides advanced tools for creating complex, organic surfaces to meet the most demanding design demands. These tools enable designers to create precise, detailed shapes that are essential in industries such as automotive and aerospace.   Subdivision modeling (NURBS) in NX lets you create organic shapes and smooth surfaces with intuitive tools. This is ideal for designing consumer products and aesthetic parts, where appearance and ergonomics are crucial.     d. Tool and Mold Design With dedicated features for designing injection molds, progressive dies, and other complex tools, NX makes it easy to develop high-quality tools and molds. This ensures that products can be manufactured accurately and efficiently from the start.   e. Simulation and Analysis By evaluating product performance under a variety of conditions, NX  integrates simulation and analysis capabilities such as structural analysis, thermal analysis, motion analysis, and manufacturing simulation. This enables designs to be optimized to meet performance requirements and reduces the need for physical prototypes. With accurate simulation and validation tools, NX ensures that products meet quality and performance requirements from the beginning of the design process. This also reduces the need for rework and increases confidence in the integrity of the final product.    f. Fluid Simulation (CFD) with FloEFD Prepare and evaluate CFD fluid flow and heat transfer simulations with Simcenter FloEFD , a full-featured 3D computational fluid dynamics (CFD) analysis solution. With Simcenter FloEFD ’s integration with NX , you can perform “what-if” analyses and simulations with easy-to-use wizards that identify the optimal design early in the design process. g. CAM Integration To prepare your designs for production, NX Design provides tools for CNC programming, process planning, quality inspection, and additive manufacturing. This ensures a smooth transition from design to manufacturing, increasing efficiency and reducing time to market. h. Collaboration Facilitating team collaboration, NX  enables real-time data and information sharing. This improves communication and decision-making at every stage of product development, regardless of where team members are located.   3. Competitive Advantages a. Cycle Time Reduction NX streamlines the product development process, reducing cycle time from concept to production. Integrating CAD, CAM and CAE into a single platform eliminates the need for data transfers between different systems, minimizing errors and rework. b. Improvement in Product Quality With accurate simulations and validation tools, NX  ensures that products meet quality and performance requirements from the beginning of the design process. This also reduces the need for rework and increases confidence in the integrity of the final product.   Siemens NX CAD/CAM offers significant advantages for companies looking to optimize their design and manufacturing processes: 50% Faster Product Design Cycles:   Create high-quality new products with less rework and fewer prototypes using NX CAD. 20% Shorter Delivery Time: Meet tight deadlines with NX ’s integrated design and manufacturing capabilities.   90% First Time Production:  Improve key performance indicators (KPIs) while enhancing sustainability with NX CAM. 4. Use Cases and Applications a. Automotive Industry Automotive companies use NX  to design and develop complex vehicles, components, and systems. The ability to handle large data sets and perform advanced simulations is crucial to this industry, enabling more efficient and innovative design. b. Aerospace and Defense In the aerospace industry, NX  is used to design aircraft and aerospace components, ensuring high accuracy and compliance with stringent regulations. Simulation and analysis tools help predict performance under extreme conditions, critical to safety and efficiency. c. Industrial Machinery Industrial machinery manufacturers leverage NX  to create efficient machines and equipment by integrating mechanical and electrical design into a single platform. This enables faster development and better integration between different engineering disciplines. d. Electronics and High Technology Electronics companies use NX  to design electronic devices and components, optimizing design for manufacturing and performance. Advanced simulation tools help ensure that products meet quality requirements and perform as expected. Conclusion NX is an essential tool for companies seeking innovation, efficiency and competitiveness. With a wide range of functionalities and a strong focus on integration and collaboration, NX transforms ideas into reality, enabling companies to develop better products faster. Explore the potential of NX Design and take your business to new levels of success. To learn more about how NX  can transform your business, follow us on LinkedIn @CAEXPERTS  for more insights and updates. Schedule a meeting with CAEXPERTS  and discover how NX , Siemens’ comprehensive CAD software, can revolutionize your business. Our experts are ready to demonstrate the features, benefits and applications of this powerful tool, providing a detailed view of how it can optimize your design, engineering and manufacturing processes. Don’t miss this opportunity to boost your business with NX ’s innovative solutions. Contact us and schedule your meeting today!

Accurate and affordable multiphase simulation, including mixtures Hybrid multiphase is a smart approach for the affordable simulation of multiphase liquids such as jets, films, droplets and mist. However, the current state-of-the-art VOF-Lagrangian Multiphase – Fluid Film approach could not adequately cover applications with mixtures (mist) as it required everything to be resolved or accounted for discretely – which would mean significant computational cost. To address this, the new Simcenter STAR-CCM+ 2406  release introduces several features to put Multiphase Mixing with Large-Scale Interfaces MMP-LSI at the heart of hybrid multiphase modeling. First, the new release supports the transition of small Lagrangian droplets to MMP. This enables more efficient treatment of very small droplets—typically 10s of microns in size, transported in continuous flow—where LMP is not an efficient model. Additionally, S-Gamma for MMP-LSI enables accurate transport and prediction of droplet or bubble size distributions in MMP phases from LMP (and other sources). Finally, MMP-LSI’s Impact of LMP on Free Surfaces enables simulation of scenarios where LMP droplets transition into high volume fraction regions of the corresponding continuous phase. Together, these new capabilities allow you to cover applications including mixtures, always leveraging the most efficient multiphase model to be used locally, while transitions between the best-fit models are handled automatically. This innovation allows you to efficiently simulate resolved free surfaces, ballistic droplets, films and mixtures in a single simulation. The result is accurate predictions of droplet sizes and phase transport, making it efficient for a wide range of applications such as electric motor cooling. Improved fidelity for battery cell aging risk assessment Degradation of a battery cell's internal active materials leads to decreased cell performance in the long term, posing a challenge for battery designers in identifying mitigation methods. With the new release of Simcenter STAR-CCM+ 2406 , the Sub-grid Particle Surface Film model for cell degradation captures two key aging mechanisms: Solid-Electrolyte Interphase (SEI) film growth and lithium plating film growth. Designed to be used in conjunction with the 3D Cell Designer in Simcenter STAR-CCM+ , this model allows you to pinpoint the cellular areas most impacted by aging, with all models validated against experimental results from the European Commission-funded MODALIS project. This innovative approach helps you model the complexity of aging processes in batteries. It complements long-term time-domain focused system simulations by providing valuable spatial insights into cell degradation mechanisms and thus contributes to more effective mitigation strategies. Improved particle agglomeration modeling of granular (wet) flows Particle granulation is an important part of the process industry and pharmaceutical manufacturing and plays a crucial role in the final quality of the pharmaceutical product. Simulation of such industrial processes with agglomeration or deposition of solid particles is challenging and requires accurate modeling of the cohesive forces. With the new release of Simcenter STAR-CCM+ 2406  , the particle agglomeration model, which replaces the parallel bond model, facilitates the formation of bonds based on user-defined local and temporal conditions. This model allows bonds between particles and boundaries and includes bond stiffness independent of mechanical properties. These improvements enable more realistic simulation of particle agglomeration processes in various industrial applications, significantly improving realism and reducing computational cost. Compromise-free contact modeling for complex fluid-structure interaction (FSI) contacts The standard penalty method for mechanical contacts requires user input for the penalty parameter, which describes the stiffness of the contact. This can be challenging, especially in complex contact situations. The new version of Simcenter STAR-CCM+ 2406  includes the Augmented Lagrangian Multiplier (ALM) method, based on the Uzawa algorithm, which mitigates this by enforcing precise contact regardless of the penalty parameter. It is robust even to sudden contact changes, with an optional automatic update of the penalty parameter for faster convergence. Now you can achieve high accuracy and robustness in complex contacts without compromise. Faster design exploration studies through intelligent simulation initialization Extensive design exploration studies benefit most from the acceleration possibilities of the underlying individual design simulations. The new version of Simcenter STAR-CCM+ 2406  introduces a solution that automatically initializes simulations of a new design that is closest to the expected results by leveraging existing results from the nearest neighbor previously simulated in the design space. In other words, the solution takes the calculation result of the solution that is assumed to be the closest. This approach speeds up individual design simulations and, consequently, reduces the overall turnaround time of the design exploration study. It should be noted that in cases where the design space and the solution space are non-linearly correlated, the time saved may be negligible. For monitoring and understanding purposes, you can easily identify reused designs and designs reusing results with specific Design Sets. The workflow is simplified without the need for manual operation, and this feature is available for Scan, Design of Experiment, and Optimization studies, even if you are not saving all Designs. This simple method allows you to conduct more efficient and faster design exploration processes, significantly increasing productivity. Easily evaluate the impact of CAD parameters on a cost function Designing a product often requires analyzing how changes in geometric parameters affect performance, a task that can be daunting without extensive parametric design exploration studies. In the latest release of Simcenter STAR-CCM+ 2406 , you can now compute adjoint sensitivities of a cost function with respect to global parameters used in 3D-CAD, extending the Compute Parameter Sensitivity functionality introduced in release 2306. This enhancement enables you to efficiently evaluate the impact of CAD parameters on global cost functions, such as pressure drop, without the need for complex setups. This means you can now quickly understand the effects of design changes on key performance metrics, streamlining the design optimization process. This capability enables you to make informed decisions faster, reducing the time and effort required for design iterations. Improved ease of use of gradient-based optimization (Adjoint) Gradient-based adjoint is a powerful optimization method. But it is not always beneficial to compute and evaluate the adjoint over the entire geometry. Restricting the computation of sensitivities to specific areas of interest required laborious assignment to specific thresholds. The new version of Simcenter STAR-CCM+ 2406  introduces per-surface subgroups for calculating adjoint sensitivities, allowing you to optimize design components more effectively by calculating surface sensitivity only when needed. This configuration avoids unnecessary adjoint evaluations outside the region of interest, making the gradient-based optimization workflow more efficient and easier to use. Immersive exploration of results from scratch Install Virtual Reality on the web Using Virtual Reality for CFD simulations previously required an on-premises installation of Simcenter STAR-CCM+ Virtual Reality. This can be challenging in highly restrictive IT environments, which may be a reason not to incorporate the technology into new workflows. Now, with the new release of Simcenter STAR-CCM+ 2406 , Virtual Reality exploration can be triggered from the Simcenter STAR-CCM+ Web Viewer with a single click. This allows you to better understand your results anytime, anywhere, without installation. You can easily enter the Scene directly from the browser and seamlessly transition to Virtual Reality, enhancing understanding and sharing insights more effectively. Greater efficiency in manipulating and nullifying instanced bodies Explicitly manipulating instanced bodies in the Simcenter STAR-CCM+  embedded 3D-CAD modeler can force you to perform repetitive and inefficient geometry preparation steps, and it also risks becoming a memory bottleneck. The challenge of efficiently handling instanced bodies is addressed with the new Simcenter STAR-CCM+ 2406  release by using pre-existing CAD instance information to create instances of the original body. This approach ensures that modifications applied to any instance can be propagated to all instances, including repair features, sketch commands, and body operations. This results in reduced memory consumption proportional to the number of instances within the geometry, making the process more efficient for you. More efficient boundary layer capture with Adaptive Mesh Refinement (AMR) Adaptive mesh refinement (AMR) offers several benefits, including increased accuracy, improved efficiency and scalability, and reduced memory usage. However, with isotropic refinement of the prismatic layer, AMR can result in unnecessarily large numbers of cells within the prismatic layer and the inner domain. This represents an unnecessary penalty in runtime without adding any benefits in terms of increased accuracy or stability. To address this, the new version of Simcenter STAR-CCM+ 2406  now supports anisotropic prismatic layer refinement during AMR. This refinement strategy reduces the overall number of cells, resulting in faster simulation times. You benefit from high flexibility with support for isotropic, tangential, normal, and criteria-based refinement strategies, ensuring more efficient boundary layer capture without compromising accuracy. Tackle complex helicopter simulations more easily The design of rotary aircraft presents significant challenges due to the complexity of analyzing and predicting flow fields under unsteady trim conditions. The new blade element method cutting option in Simcenter STAR-CCM+ 2406  provides a fast, mid-fidelity solution for analyzing these unsteady flow fields during cutting operations. By incorporating this method, you can streamline your workflow by eliminating the need for manual adjustments after each run, which shortens the overall simulation process. The result is faster response times compared to the traditional rigid body motion (RBM) approach, allowing you to quickly obtain reliable results. The new version makes it easier to tackle complex rotorcraft simulations. Benefit from scalable and faster rigid body motion simulation Applications involving rigid body motion (RBM), such as unsteady vehicle aerodynamics and electric motor cooling, often rely on sliding mesh interfaces, which can be computationally demanding and limit performance at large core counts. The new metric-based intersection in Simcenter STAR-CCM+ 2406  offers a solution to this challenge by providing faster and more scalable interface intersection calculation. By employing this innovative approach to interface intersection computation, you can achieve improved performance and faster response times for complex simulations involving large interfaces. Run GPU-accelerated, workflow-supercharged vehicle thermal management simulations The 8x reduction in execution time is evaluated by comparing a CPU solution on 128 AMD EPYC 7532s with a GPU solution on 4 and 8 NVIDIA A100 cards. Conjugated Heat Transfer (CHT) applications, such as full Vehicle Thermal Management (VTM), are computationally intensive, particularly when radiation models are employed. In such studies, all solid parts of the vehicle (10k+ in modern configurations) must be modeled in detail to ensure that no component overheats during a wide range of operating conditions. Surface properties, such as emissivity, of each solid part play a key role in the accuracy of the simulations. Simcenter STAR-CCM+ 2406  introduces a GPU-native Surface to Surface (S2S) radiation model, as well as a completely revised workflow for storing and inputting surface properties. The GPU-native S2S model accelerates VTM and other CHT simulations, providing CPU-equivalent solutions while maintaining a unified codebase. The new surface property workflow dramatically reduces preprocessing time for simulation files with thousands of solids through better integration with material and model databases. By leveraging the power of GPUs and native automation capabilities, you can achieve significant reductions in the end-to-end simulation process, making it possible to perform detailed thermal analysis more efficiently. These advancements not only speed up your simulation processes, but also ensure that results are consistent and reliable regardless of the hardware used. Take advantage of more solvers and features ported to GPUs Additionally, several solvers and features have been ported to GPUs to expand the range of applications that benefit from the GPU. Simcenter STAR-CCM+ 2406  now supports GPU-native grid sequencing, which accelerates steady-state vehicle aerodynamics. Porting the partial slip and isothermal segregated fluid model to GPUs allows you to run rarefied flows more efficiently. Finally, any type of simulation will benefit from GPU-native derived part tracking. With these improvements, you can run simulations on the hardware that best suits your business needs and projects, seamlessly transition between GPUs and CPUs, and ensure consistent results through a unified codebase. Choose from more hardware options for native GPU acceleration Likewise, hardware options are being expanded. In the Simcenter STAR-CCM+ 2402 version, the first AMD GPU functionality was introduced, to accompany NVIDIA GPU functionality, with the ability to run on AMD Instinct™ MI200 series GPUs, With the new release of Simcenter STAR-CCM+ 2406 , support has been expanded to include AMD Instinct MI300X and Radeon™ Pro W7x00. This extension provides you with even more flexibility and access to native GPU acceleration, offering a cost-effective performance boost by supporting both high-end GPUs and workstation-style graphics cards. Run an expanded range of applications with SPH Smoothed-Particle Hydrodynamics (SPH) technology is a powerful alternative method for modeling complex transient flows with highly dynamic free-surface flows. While the introduction of SPH in Simcenter STAR-CCM+ 2402 provides integrated access to this method alongside the traditional mesh-based approach, the initial release was limited in its range of applications. To cover more applications, SPH capabilities are being continuously expanded. With the new release of Simcenter STAR-CCM+ 2406 , liquid injection applications for SPH are now enabled through the support of inlet boundary conditions for SPH particles. This expands the set of applications covered by SPH to include vehicle water runoff and powertrain lubrication with oil jet injection. This increases the versatility of SPH within Simcenter STAR-CCM+  and expands your options for modeling highly dynamic flows with the most appropriate method from within a single simulation environment. Leverage extended simulation automation intelligence Implementing Java scripts to automate complex CFD workflows, while powerful and flexible, can be challenging to maintain and update. In Simcenter STAR-CCM+ 2406 , native simulation automation is being expanded to support multiple physics configurations and even more complex workflows in a single simulation. Two new features support turbulence model selection in Stages and nested Simulation Operations sequences. This means you can easily automate RANS-to-DES workflows and robustly launch supersonic and hypersonic aerospace simulations using a fully automated Inviscid-to-RANS workflow, all with a single physics continuum and without the need for Java scripting. Nested Simulation Operations make it easier to manage, maintain, and troubleshoot complex simulation sequences, increasing the reliability and efficiency of your workflows. It also enables you to create a single simulation model for multiple scenarios, reducing the need for manual intervention and scripting. These new capabilities help you quickly create and execute sophisticated automated workflows, improving productivity and ensuring consistency across different simulation projects. Discover how Simcenter STAR-CCM+  can revolutionize your projects! With innovative solutions for complex mixes and advanced industrial processes, you’ll achieve more efficient and accurate forecasts. Schedule a meeting with CAEXPERTS  today to explore how we can help transform your operations, reduce costs, and increase productivity. Don’t miss the chance to take your projects to the next level!

Imagine a perfect operation where chemicals flow seamlessly, driving production and progress. But unfortunately, the reality is that chemical spills are a constant threat, wreaking economic and environmental havoc. Hydrogen, for example, has a high production cost and is difficult to store and transport. It faces the complex challenge of liquefying hydrogen, where each stage of the process must not only be efficient but, above all, safe. In the face of global energy shortages, hydrogen is seen as a promising alternative to fossil fuels. Liquefying hydrogen helps reduce its transportation and storage costs, increases safety and extends the life of fuel cells. A hydrogen liquefaction plant involves transmitting signals over long distances, for example 150 meters or more. Some devices are also installed in explosion-proof areas. In the complex corridors of processing plants, a simple pipe rupture can trigger a disaster. In this context, engineers have the mission of: anticipating and correcting processes. To this end, Simcenter Flomaster  is an advanced and essential tool for reproducing, understanding and optimizing processes in chemical plants. In a detailed analysis, the impact of product spills after the heat exchanger line is assessed, observing critical variables such as pressure. It is found that spills can cause problems throughout the entire process, from the beginning of the production line, significantly affecting system pressure. To mitigate these risks, it is vital to implement safety valves at strategic points. Through simulations, with Simcenter Flomaster, the most effective strategic locations within the plant for such implementation are determined, considering where the variables will have the most significant influence and where the problems will most intensely affect the process, as demonstrated in the simulations developed by CAEXPERTS. Implementation of Safety Valve with PID control in the process line Furthermore, implementing controllers that influence valve opening in the automation system is essential to minimize losses caused by spills. Simcenter Flomaster offers a wide range of controllers to simulate and predict the behavior of the control system, as it allows the evaluation of the process line configuration, with or without control. In this way, it is possible to pre-analyze cost losses in both scenarios. For example, for the hydrogen liquefaction process, the impact of the line with and without process control is analyzed. The results show that without control, there is a loss of 4m³, while with control, the loss is reduced to just 1.1m³. In other words, Simcenter Flomaster enabled a 72.5% reduction  in the volume of lost hydrogen! With Simcenter Flomaster, we not only simulate, but also control the implementation of safety solutions in real time. By enabling connection to real plant sensors and optimizations via Excel integration, it keeps the engineering team one step ahead in detecting and mitigating problems before they even occur. Join us on this journey of innovation, where every modification, every simulation takes us closer to a safer, more efficient and more sustainable process. Schedule a meeting with CAEXPERTS to find out how Simcenter Flomaster can transform the safety and efficiency of your hydrogen liquefaction processes.

The production of castings is always a challenge, and not everything always goes as planned. There are several factors that, when not managed, compromise the quality of one, or even a batch of parts that come out defective. A delivery defect is the deviation in quality or condition of an actual casting compared to an ideal product. The Simcenter Star-CCM+ has models that accurately predict the most common defects that can compromise the quality of castings. Common defects are: Porosity Oxides Misruns Gas Inclusion Porosity Micro porosity: Criteria functions as empirical models to assess micro shrinkage Niyama Criterion Dimensionless Niyama Criterion Require manual validation for different alloys against real casting Macro porosity For pure thermal simulations based on density based volume deficits in isolated liquid metal pockets High fidelity macro shrinkage prediction for fully coupled simulations based on pressure drop in isolated liquid metal pockets allows to include buoyancy effects Oxides Passive scalar based oxide prediction model: Accumulates the time the metal phase is exposed to air User can modify the underlying oxide accumulation law to include effects such as the amount of oixidizable amount of metal The model does not influence the flow field but the effect of an oxide film on the flow may be included by oxide thickness based viscosities Evaluating the filling front: The filling front will usually transport the most of the oxides and dirt By tracking the progress of the filling front and its final resting place, the oxide contamination can be estimated Misruns Concurrent solving of flow and solidification: The solidification of metal and its effects on the filling of the mold is calculated concurrently to the actual flow Mushy zone for technical alloys: Modeling effects of growth and accumulation of metallic dendrites on the flow Porous media approach Influence of solidification on flow behavior Flow stop model: All fluxes across flow stopped cells are zeroed expect energy For large pressure gradients or body forces (i.e. HPDC) Volume of Fluid multiphase model: Inherently provides the spatial distribution between gas and metal phase Post processing filling and phase distribution allows to assess gas inclusions Transport equations are solved for the entire fluid domain, thus the gas phase is transported through the domain and interacts with the metal phase Surface tension effects Gas entrainment Counter pressure due to venting and gas compression effects The production of castings presents complex challenges that can compromise the quality of your products. At CAEXPERTS, we use Simcenter Star-CCM+ to predict and resolve critical defects such as porosity, oxides and runtime errors. Schedule a meeting with us and discover how our advanced solutions can transform your casting processes, ensuring efficiency and superior quality. Contact CAEXPERTS today and raise the standard of your production!

Offshore wind energy represents a significant advance in sustainable electricity generation. Using wind turbines installed offshore, these installations take advantage of the strongest and most constant winds in the ocean, increasing efficiency and power generation capacity. With more than 70% of the Earth's surface covered by seas and oceans, offshore wind energy transforms vast ocean areas, which have productive potential and were not being used, into valuable energy assets, even though many installations are still relatively close to the coast. Operation of an Offshore Wind Farm Offshore wind farms can be divided into two main categories, based on the turbine installation technique: Turbines Fixed on the Seabed: Used in areas close to the coast or in shallow waters. Turbines on Floating Platforms: Suitable for the open sea, where the depth is greater and the sea floor is more complex. The operation of these turbines is similar to onshore ones: the wind drives the rotating blades, which in turn drive an electrical generator, converting mechanical energy into electrical energy. The crucial difference is the location, as the wind speed in the ocean is higher and more uniform, enhancing energy generation. Types of Floating Platforms Floating offshore wind energy is made possible by several floating platforms, chosen according to the oceanographic conditions of the location and the specific project: Barge: Large surface area in contact with the water, providing stability, similar to a boat. Semi-submersible: Minimize exposed surface area, maximizing volume moved for stability. Spar: Cylinders with weight at the base to guarantee vertical stability, ideal for larger turbines. TLP (Tensioned Legs Platform): Stellar structure with minimal arms, anchored by tensioned cables. Challenges and Advantages Challenges: Designing platforms to withstand the variability of wave movement and intensity so as not to compromise operation and structure, construction at greater depths, transport and installation of large structures and the complexity of sea floors. Advantages: Use of vast maritime areas, without the presence of relief or stronger winds, contributing significantly to renewable energy and sustainability. Case Study: CFD Design of Floats for Offshore Wind Turbines To support and accelerate the development of floating platforms for offshore wind turbines, a detailed study was carried out using computer simulation on the operation of the Extended Response of Oscillation (RAO). Tests conducted in the laboratory measured the response of the floats, and these experiments were faithfully reproduced with CFD simulation by Simcenter Star-CCM+. The precision present in this tool is crucial to reducing development time and costs, allowing fast and efficient optimizations of floating platform designs and other wind turbine components. Simcenter Star-CCM+ allows engineers to perform the following float design activities: RAO Motion Validation: Star-CCM+ enabled accurate validation of RAO (Amplified Oscillation Response) motion using model-tested wave signals. This process is essential to accurately represent the physical behavior of the different fluid and solid phases, ensuring that simulations accurately reflect the real conditions faced by floating structures. RAO validation is critical to ensuring that float designs can operate stably and efficiently in marine environments, minimizing the risks of structural failure and maximizing power generation efficiency. In the graph below, the experimental (Test Model) and numerical (CFD) curves are compared, demonstrating the great accuracy of Star-CCM+ in reproducing the tests. This precision is crucial to reducing development time and costs, enabling fast and efficient design optimizations of floating platforms and other wind turbine components. Lift response comparison (Blue: Test Model, Red: CFD ) Pool Wave Testing (OTRC 2013): Pool wave testing uses scale models to validate the performance of floats in simulated marine conditions. The pool generates controlled waves to replicate the sea, and sensors measure forces and movements of the models. These tests identify and correct design issues before actual construction, saving resources and ensuring the efficiency and safety of offshore operations. Design Tree Optimization: Development of efficient floats for applications in wet and dry conditions, ensuring optimized performance and adaptability to different operating environments. This process involves the analysis and modification of several design parameters to maximize the efficiency, durability and cost-benefit of the floats, taking into account factors such as corrosion resistance, stability in different sea conditions and ease of maintenance. Offshore wind energy, especially with the use of floating platforms, represents one of the most promising solutions for generating renewable energy. With the support of computer simulation and advanced Siemens software, technical challenges can be overcome and the development of solutions is accelerated, allowing the implementation of efficient and sustainable projects quickly. Advances like these not only contribute to a greener planet, but also drive innovation in the renewable energy sector, promoting a more sustainable future for everyone. Schedule a meeting with CAEXPERTS to find out how we can help make your offshore wind energy projects a reality. Let's drive innovation and sustainability in the energy sector together!

Accurately predicting strength and durability is crucial, especially in electric vehicles, where heavy batteries alter mass distribution and create new charges. As engineers, we seek a balance between durable components, cost and material weight. During this process, we deal with large amounts of data from various sources. However, do we know how to integrate them efficiently? Despite a lot of information, we may not know which fatigue analysis method is most suitable. Test results, often represented by stress values ​​in color images after inputting loads, geometry and material data, are not always clear about the strength and fatigue of the structure. CAE Fatigue Methodology for Design Optimization and Virtual Validation Using CAE Fatigue Methodology for Design Optimization and Virtual Validation There is a significant difference between how loads and events affect the structure as a whole and individual components. Even after long analysis processes, it may be necessary to identify which events and loads contribute to the damage. Common Challenges: Loads on the Structure: Multiple loads act on a car, such as the independent excitation of each wheel by the road, creating multiaxial loading situations. External influences such as temperature and oxidation must also be considered. How do you get and measure these loads? Stresses and Strains: To understand fatigue, it is crucial to know the stresses and strains in the structure. How do you transform measured or simulated loads and local geometry into stresses and strains that affect the structure? Effect of Manufacturing Process: The manufacturing process can change the basic fatigue properties of the material. Surface finishes or welding during assembly create residual stresses that influence the behavior of the material. Do your experiments consider all of these influences? A process of resistance and durability from end to end. CAE Fatigue Methodology for Optimization and Validation Simple but comprehensive. Fatigue analysis using CAE methodology can be facilitated with the integration of real load data and prediction. Obtaining load data can be done through measurements or simulations, and it is crucial to integrate this data with the geometry for an accurate analysis of resistance and durability. Load prediction can be simplified by integrating the data stream into Simcenter 3D Motion, enabling a complete durability analysis when selecting the component and running load prediction. Benefits: Direct import of Simcenter 3D Motion results Typical industry load data formats supported Superposition of hundreds of measured or simulated load channels and finite element results Complex Work Cycles The analysis of specific events, such as torsion or screwing modes, can be done directly from the finite element results, considering the nonlinearity of the material and/or geometry. FE Result For cargo acquisition (or third party forecast): Opening for file formats, load time history and FE Smart interface to automatically connect channels and load cases Static and pre-stress cases Automatically adapt to re-calculations For components subjected to frequency loads, such as batteries, simulation of random and harmonic events is essential to cover all load conditions. Efficiently combining these events into a work cycle is facilitated by Simcenter 3D, allowing the creation and activation of load events directly in the graphical interface or from a spreadsheet. Simcenter 3D 3D Job Cycle Events : Combine any of the load events Create in the GUI or automatically from a spreadsheet Simple activation/deactivation (also from a spreadsheet) Event Order: Choose to count event order or ignore it Integration of material data into FE models is simplified in Simcenter 3D, allowing you to inherit material properties or use estimation tools. Advanced methods consider local influences and manufacturing processes. 1.      Integration into FE: Inherit material from FE 2. If FE materials do not include fatigue data: Use estimation tools – reusing E, Rm if available. 3.     Alternatively, use material from the material database, especially for welding. Add materials based on FE configuration 4.     Double-check by plotting the data 5.     Repeat for each material (if inherited, this is done automatically) To configure fatigue analysis effectively, it is essential to logically group parameters into simulation objects, using templates that guarantee the consistency and robustness of the process. The use of intelligent solvers that select the best methods based on available data is essential. Post-processing of Results Understanding the results of fatigue analysis is crucial to identifying the causes of failures and improving design. Advanced tools allow you to analyze work cycles in detail, identify critical areas and correlate simulations with real tests. Intelligent local analysis helps reduce loads and improve the flow of loads in the system. Using this integrated and simplified approach to fatigue analysis, you can perform faster and more accurate design optimizations, ensuring better understanding and continuous product improvement. To ensure the durability and resistance of components in general, CAEXPERTS offers an advanced CAE fatigue analysis methodology for design optimization and virtual validation. Integrating real load data and simulations, simplifying durability analysis and facilitating accurate load prediction. Schedule a meeting with CAEXPERTS to find out how we can help transform your analysis processes and ensure more robust and efficient results for your projects.

The power of advanced solutions In the chemical industry, challenges arise as quickly as the reactions taking place inside reactors. Given the complexity and risks inherent to chemical processes, the adoption of innovative and effective solutions is essential to ensure safety and operational efficiency. To deal with these issues, the chemical industry has been turning to advanced technological solutions. Market-leading software now has simulation and predictive analysis capabilities that allow you to not only anticipate possible failures before they occur, but also optimize the process as a whole. The Chemical Industry The chemical industry constantly faces the challenge of operating large-scale and complex systems. To manage this complexity, companies are increasingly turning to advanced technological solutions, such as software that uses digital twins to create robust and simplified models. These models are essential for understanding the behavior of systems in different situations, minimizing risks , reducing costs  and accelerating  the development of new products. Simcenter Flomaster and Digital Twins Digital twins, virtual representations of physical systems, allow engineers to simulate and analyze the behavior of chemical systems under varying operating conditions. This ability to predict system performance even before its actual implementation is an invaluable advantage. It delivers critical insights that lead to process optimization and continuous improvement without compromising safety or sustainability. An example of software that uses digital twins is Simcenter Flomaster. It allows you to analyze fluid flow in piping systems of any size and complexity. This means that engineers can understand the behavior of piping systems in terms of fluid and thermal flows at any stage of their life cycle, from initial engineering design through to operation. Therefore, risks and development costs can be reduced and companies can innovate quickly, while ensuring the performance and safety of piping systems in their factories. In the process line of chemical industries, some of the main objectives include energy efficiency, product quality control, environmental safety, sustainability and overall process optimization. To achieve these objectives, it is crucial to adopt an appropriate approach to uncertainty calculation and equipment optimization. The challenge is to resolve these uncertainties effectively, ensuring that the system not only meets current requirements, but also adapts to future demands. Furthermore, minimizing losses and redundant variables is essential to simplify the process without sacrificing its necessary complexity. At the same time, it is essential to focus on maximizing system robustness and reliability. This approach not only improves earnings and profit but also boosts overall industry productivity. Adopting these technologies and strategies means not only keeping up with innovation trends, but also leading the march towards a more efficient and sustainable chemical industry. The benefits are clear: more efficient processes, reduced waste, better environmental compliance and, most importantly, the ability to respond quickly to market changes with effective and innovative solutions. Schedule a meeting with CAEXPERTS to discover how our advanced technological solutions can transform your operation in the chemical industry. With our expertise in simulation and predictive analysis software, you can anticipate failures, optimize processes and ensure safety and efficiency. Don't miss the opportunity to lead innovation in your industry – contact us today!

Introducing: Linde Engineering Linde Engineering is a leading global industrial gas and engineering company that serves a variety of end markets, including chemicals, energy, food and beverage, electronics, healthcare, manufacturing, metals and mining. Linde’s industrial gases are used in countless applications, such as oxygen for hospitals and high-purity and specialty gases for electronics manufacturing. Supporting sustainability Industrial plants make practically everything that we use daily from the food we eat to the liquified natural gas (LNG) we use to heat our homes. They are some of the most complex and mission-critical products on the planet. Not only do they tend to handle potentially hazardous raw materials for downstream manufacturing and energy generation, but they also feature some of the world’s most advanced engineering and highly secure processing systems. Critical as they are to daily life, today’s society requires these plants operate even more efficiently than ever before and do more with less. Industrial plant engineers want to reduce project complexity, increase efficiency, guarantee safety and reduce both required capital and operational costs. This is where Linde Engineering steps into the picture. Headquartered in Germany, Linde Engineering is known as a market-leading engineering, procurement and construction (EPC) provider for turnkey industrial plant projects. With a client list that reads like a who’s who of global industry leaders, including Shell, BASF and CERN, Linde Engineering has successfully completed highly demanding, high tech plant design, construction and upgrades for all types of companies around the world. Today, Linde is focusing on new markets to support the green energy transition, like hydrogen and ammonia cracking, but the common factor in every Linde project is the company’s expertise in the design and construction of turnkey plants. They have delivered projects that have included the largest and most advanced processing plants in the world. With the annual world-wide demand for materials and energy expected to grow significantly, the industry needs innovative and top-performing plants that are engineered for high throughput production and reliable performance with the added flexibility to cover a full range of product options. From the drawing board to digitalization Linde Engineering meets this challenge with experienced engineers like Hans-Joachim Dieckmann, senior expert, surge and pipe stress analysis for plant layout and engineering services. As he finishes a career spanning more than four decades, Dieckmann has witnessed first-hand the digitalization of industrial plant engineering. “I started as a piping designer and spent a year onsite in Norway as a Linde contractor for Statoil,” says Dieckmann. “After that, I returned to Germany and started to work on pipe stress analysis. In 1992, Linde decided to change its engineering approach. In one day they took out all the drawing boards, and we got computers: the first days of digitalization. It was marvelous. Over the years, we have all accepted computers, programs and all the digital interfaces we use today. And with AI quickly approaching, we will change again. There will be a lot more AI-influenced digitalization coming in the next years as well.” Getting it all to fit Even as the software tools and processes become smarter and more sophisticated, Dieckmann and his team have the core task of getting everything to work safely and securely together. “When you are engineering a plant, you have about 500 square meters of space,” explains Dieckmann. “Not more. And the length of the piping is about 4.4 kilometers. This is a long, long distance to wrap around as a processing system. The trick is to get everything to work and to fit. “When you are designing a plant, one of the most important things is the water-cooling system. It’s normally a system that includes about 100 heat exchangers, pumps, discharge lines and standpipes for hydraulics. The whole system is controlled by analyzers for safety. It is very complex system to get right.” Leveraging Simcenter Flomaster In 2011, the team turned to simulation software as a possible way to iron out the complex engineering issues in the water-cooling system. This is when they discovered Simcenter Flomaster software, which is part of the Siemens Xcelerator business platform for software, hardware and services. Backed by accurate and powerful solvers, Simcenter Flomaster is a leading tool for thermofluidic system simulation, and the piping system team at Linde Engineering quickly discovered that engineers could easily and effectively size gas, liquid and two-phase systems and components for maximum efficiency. As the process developed over the years at Linde, the team created a digital twin that could easily be used to analyze a variety of dynamic events, such as operating conditions, layout schemes and startup, and failure and emergency scenarios to ensure security and safety. “Every single plant that we implement is unique according to the site location and the customer requirements,” Dieckmann states. “It will be a different plant in Turkey than in Singapore. And you need to look at all the options and find the right solution for each site before you build. You can’t just add an extra well for the water-cooling system onsite or decide to fix an issue by adding an extra 50 meters of piping. Working like that is just not possible.” Evaluating each foreseeable risk factor Today, the team uses Simcenter Flomaster to evaluate each foreseeable risk scenario to the system prior to construction. The simulation work starts from getting everything to fit to the “as operated” system state before the disturbance occurs. Disturbances that arise during a risk scenario, such as a sudden pump trip, valve closure or startup and redistribution, are run on the complete simulation model and the resulting pressure surges are calculated across the entire system. This detailed simulation work helps identify the critical points where the resulting hydraulic forces can cause damage to the system. Using Simcenter Flomaster enables the user to automatically deliver a force map file that serves as input for third-party stress analysis tools. This process ensures due diligence in design and enables Linde engineers to evaluate mitigating measures to secure the system against undesired events and address critical safety aspects in various operational scenarios. “Simcenter Flomaster is simply necessary,” confirms Dieckmann. “At the end of the day, you want to receive the right steady-state and transient scenarios for all the hydraulic forces acting on the piping system. Every calculation is critical; not only for the quality of our engineering, but because we want to assure our clients there will be no accidents in their plant because of pressure-surge effects.” Safety is paramount As an engineering company known for quality and safety, it isn’t surprising that Linde maintains its own exacting standards for health and safety issues including surge effects in plants. "I like to say that safety is a very big topic for a space engineer as well as a surge engineer,” says Dieckmann. “It’s necessary to design your piping systems with zero overstress in the pipes. That is why we use Simcenter Flomaster to calculate how the forces will act during each pipeline point according to scenarios developed by the safety team or the plant layout team. “The calculations are complex and there is no room for errors. It depends on the mass, the velocity, the pipe size and the pump power. And then you must consider the type of liquid being pumped and/or the transportation medium. Currently, LNG plants are becoming critical, and this is a different system with loading arms in the delivery lines that will also need to be calculated for a pressure surge window. Of course, all this is regulated by a special Linde standard for surges.” Harnessing an immense amount of data If you are simulating an entire plant with hundreds of surge calculations, you are creating an immense amount of simulation data that requires an immense amount of processing power. This is one of the key advantages of Simcenter Flomaster. “Only Simcenter Flomaster can handle this type of simulation volume in an acceptable amount of time,” reports Dieckmann. “Some of our most complex plant models can take four days to run for each scenario. This is good, but we are working together with the Simcenter Flomaster development team to do this even faster. We could reach significant reductions in simulation per scenario. The Simcenter Flomaster developers will work closely with our team at Linde and reduce the time even further over the next few years.” Are you ready to take your company to the next level of efficiency and innovation? As strategic consulting and technology partners for greats industrial companies, CAEXPERTS is here to help. Schedule a meeting with us today and discover how we can collaborate to contribute to the success of your project. Together, we can transform challenges into opportunities and achieve incredible results.