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

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

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

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

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

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

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

Working with large assemblies can be challenging. If you've worked on a large assembly with 500 or more parts, you're probably familiar with software slowdowns and even crashes that can occur with these larger assemblies. Fortunately, Solid Edge offers several techniques you can employ to improve performance when dealing with large assemblies. Solid Edge now supports a large assembly mode. A mode in which several applications and display settings have been tuned to provide improved performance with large assemblies. A new option has been added to Solid Edge Options > Assembly Open As a page to automatically apply/override various user and document settings that will improve performance. Settings made for this mode will be applicable only the context of the large assembly documents and their tree structure. Other non-large documents will use the default settings defined by the user & documents. Large Assembly mode is set based on assembly size crossing the threshold defined in Options->Assembly Open As settings. This mode can be seen on Home > Modes panel. You can use a toggle switch to enter and exit large assembly mode. Large Assembly mode gets applied on Assembly File Open, Place Part of a large assembly or component into the active ASM, Edit Open of a large sub-assembly and Edit open of assembly from Draft. View Settings Floor reflections High Quality Cast Shadows Floor Shadows Ambient Shadows Silhouettes Depth Fading Display Settings Settings > Options > View Tab Display drop shadows during view operations = OFF (Default is Off but prevent a user from modifying) Process hidden edges during view operations = OFF View Transitions = OFF Auto-sharpen = OFF (Default is Off but prevent a user from modifying) Glow Set to 0 (consider a checkbox) Use shading on highlight = OFF Use shading on selection = OFF Settings > Options > Assembly Dim surrounding components when a selection is made in relationship pf = OFF Inactivate hidden and unused components every XXX minutes = ON Highlighting PartsFast Locate Using Box Display Fast Locate Using Box Display You can improve large assembly performance by setting the Fast Locate Using Box Display option on the Assembly tab on the Options dialog box. When you pause your cursor over a part in the assembly, it will highlight using a rectangular range box, instead of all the graphic display elements of the part. Fast Locate using box display for assemblies When checked, subassemblies are displayed using a rectangular range box (A) instead of the graphic display elements of the geometry (B). Setting this option improves display performance when highlighting and selecting components in an assembly. This setting should be considered an option when working with large assemblies. Fast Locate when over pathfinder Setting the Fast Locate When Over Pathfinder option on the Assembly tab on the Options dialog box also allows you to improve performance. When you set this option, the name of the assembly component is displayed in the message field when you pass the cursor over the component name in Pathfinder, but it does not highlight in the graphics window. When you clear this option, the assembly component highlights in the graphics window when you pass the cursor over the component name in Pathfinder. In summary, Solid Edge contains user-configurable options that help to improve interactive performance with large assemblies. Watch the video below to see in full how simple it is: If you want to get the most out of Solid Edge and the technological innovations that CAEXPERTS can offer, don't wait any longer to improve the performance of your projects. We are ready to help you optimize your engineering and design processes. Don't miss the opportunity to take a leap in efficiency and productivity. Schedule a meeting with the CAEXPERTS team of experts right now and discover how we can take your work to a new level. Click the button below to schedule your meeting and embark on the journey towards technological success with CAEXPERTS !

How does digital engineering drive electrification? Hybrid vehicles have been developed as a form of alternative mobility to comply with increasing international regulations for a sustainable future. In this scenario of major changes in policies and accelerated technological evolution, a digital model is essential to maintain competitive development times, identify project bottlenecks in the early stages and reduce or eliminate the cost of building unnecessary prototypes. Model Building Consider the initial scenario of an electrification project: I have my vehicle's specifications, a variety of components to evaluate, and several possible configurations for the arrangement of these components. How to analyze the impact of these choices on final performance? Traditionally, responsible engineers use heuristics to reduce the universe of decisions to a handful of possibilities, which can then be evaluated by the team in the early weeks of the project. Alternatively, it is possible to set up a digital representation of the system in Simcenter Amesim , as in the simulation below. Configuration of a parallel hybrid vehicle Configuration of a series hybrid vehicle Configuring components from business data allows you to quickly evaluate key system metrics in different scenarios. To compare performance between the two architectures, we chose three driving cycles representative of real conditions: Urban Dynamometer Driving Schedule (UDDS): American standardized test that represents urban driving conditions Highway Fuel Economy Test cycle (HWFET): highway driving cycle with a high-speed profile used to determine the fuel economy rates of light vehicles My daily route to work: the cycle is automatically generated by Amesim using public GPS and traffic data. In general, the engine of a parallel configuration can be smaller than that used in a series architecture, as it transfers work directly to the wheels without losing energy for electromechanical conversion. For this study, the same engine was used in both configurations, and the other components were chosen to be as similar as possible. Analysis After a few seconds of simulation we obtain a concise summary of the performance of the configurations in each cycle. In a quick analysis we can see that the series architecture is a little more efficient in urban driving conditions. Furthermore, the driving cycle results obtained by GPS are consistent with those obtained by UDDS. Another critical parameter is battery power consumption and savings during the driving cycle. This is called SOC ( State of Charge ). The batteries are recharged during braking or when the SOC reaches certain limits, determined by the chosen control strategy. What does all this mean? As seen above, Simcenter Amesim represents the electric vehicle system by a comprehensible and highly customizable diagram, which allows rapid determination of vehicle subsystems from commercial data for rapid validation of new components and configurations in pre-design stages. In more advanced stages, it is possible to detail the control strategies and performance curves of critical components, such as batteries and motors, for a simulation of critical factors — for example, heating and energy demand. All of this makes it possible to evaluate the functioning of the project in conditions close to real ones from the initial stages. The only way to balance a large number of variables is to consider them comprehensively from the beginning of the process. In the digital world, the evaluation of different scenarios is optimized to save team work time, reduce time to market for the final product and enable evidence-based design decisions, resulting in greater added value to the final product. In addition to system modeling, when geometric aspects and spatial distribution of the quantities involved are relevant, digital engineering employs multiphysics virtual prototyping in 3 dimensions, considering fluid dynamic, thermal, chemical, structural, acoustic, electromagnetic effects, complex materials, constructive forms and manufacturing processes. Consult CAEXPERTS now to find out more about how we can help your company boost technological innovation and competitiveness. Let's talk about CAE?

You will find in this article: An intriguing and engaging point of view, with a critical and practical approach, to stimulate ideas and solutions to today's energy challenges. Let's review concepts, review the bases, going beyond corporate marketing, and point the way! Warming up the turbines... We have to admit, for a long time, we used energy in an archaic way. It's been a long time since man discovered fire, and this has been our main way of generating energy ever since. Burning, or destroying, is easy, but it has side effects. We were not able to make good use of the thermal energy released and the by-products, which, in general, are harmful to the environment. We should look more at conversion and decomposition, taking inspiration from natural processes, which are much more subtle. Take, for example, photosynthesis, which converts carbon dioxide molecules into oxygen and gives carbon a noble destination, with the help of a complex and inexhaustible source of energy that is the sun. Molecules such as chlorophyll and melatonin act as catalysts for the subtle reactions of making concentrated energy available. Reviewing distorted concepts... First, let's review two concepts that have distorted interpretations in the scientific-industrial-business context, which are “decarbonization” and “green hydrogen”. We should use integrated impact on the environment and society as a labeling criterion. It makes no sense to talk about a decarbonization agenda at any cost (whether economic or environmental side effects). It makes no sense to talk about green or blue hydrogen processes if the process in question is not efficient in technical-economic terms, or if it generates a negative environmental impact in some way. For example, from this point of view, the nuclear fusion of hydrogen is no longer so interesting, as it is expensive, dangerous and aimed essentially at generating heat. Decarbonizing and using green hydrogen just to please investors and embellish ESG reporting is not fair, it doesn't hold up. Sustainability has to be a choice, not a showcase. What do you mean, a choice? Engineers' mission is to make people's lives easier, using technology to improve society's quality of life, without harming the environment. There are infinite ways to produce technology, to generate energy, to make life easier for society. It cannot be expensive, and it definitely cannot harm the environment. Why hydrogen? The interesting thing is that hydrogen (green or not, whatever the label) allows for many energy generation routes. It's very versatile. We can say that it is the way to diversify energy availability in various configurations. Let's take as an example a route that uses the concentrated energy of ethanol, generated by renewable crops, to generate hydrogen, and that will generate electricity for cars (or whatever else is electrified, planes, ships, heavy machinery, agricultural implements, robots, etc.), with by-products like water, some heat and graphite (which goes back into the soil). Interesting, isn't it?! How to get there? How to be efficient? How to compact? How to make it portable? How to make it safe? How to make it cheaper? Keep reading this article ... How does nature convert matter? Now, let's remember how the natural processes of generation and accumulation of condensed energy in nature are. Petroleum, for example, is generated from organic matter under the action of high pressure, temperature and time. Our body's movement is propelled by energy stored in the form of fat (at our waist 😊), which came from food, which in turn came from the soil, and which received sun. There were several chemical reactions of conversion of matter and energy, which assumed different forms, some more stable, others not, ignited, accelerated or catalyzed by the conditions of the medium. The secret is the medium... Here is the key point: the secret is in the conditions of the medium where the chemical reaction takes place! Traditionally, industry (and nature) already uses catalysts and already controls the conditions of the medium (pressure, temperature, humidity, pH, etc.). Zeolites are the stars in this regard. They have a large surface area, mineral molecules naturally found in volcanic formations, and may include some synthetic additives. They are also called molecular sieves, as they sequester, or let pass, or exchange certain molecules in a reaction, greatly reducing the energy required for conversion. That is, it is not necessary to use brute force to perform the conversion. To better explain the role of the medium (catalysts, temperature, etc., or rather, field variables) in reactions, it's like when you want to enter any house: you can use brute force, break down the door, engage in a brawl with whoever is inside, or you can establish an affinity and be invited in gently. The power of catalysts... Still on zeolites: What do these volcanic and/or synthetic minerals have in common? These minerals are in crystalline form. The crystalline structure of crystal molecules is like a set of complex springs that can assume specific vibrations, which interact directly in resonance (vibrational affinity) with the molecules we want to convert, facilitating this conversion with less energy use. Not just with zeolites... Catalysts, in general, such as chlorophyll and melatonin, and many others, have the ability to have a selective vibrational affinity for a certain type of molecules and atoms. How to boost conversion... We can control the conditions of the medium, not only the traditional ones like temperature, pressure, pH, concentrations, but also the electric field, the magnetic orientation of molecules, the level of agglomeration (clusters) of molecules in solution, irradiation of resonant electromagnetic waves (microwaves, etc.) or sound waves (ultrasound, etc.), ionization, surface treatments (specific layers, electrochemical deposition, etc.), fluid dynamics processes (turbulence, vacuum, centrifugation, selective filtration, etc.), operational cycling (pressure, temperature, concentration, electrical voltage or magnetic field, etc.). These are the effects called accelerators or conversion boosters. Conclusion Hydrogen, being the simplest molecule, has a lot of molecular connection versatility, and results in more control (or assertiveness) of the reaction products. All materials dense in chemical or electrochemical energy (not just hydrocarbons) have hydrogen in their composition, or react with hydrogen. Thus, we can say that it plays a crucial role in remodeling our energy matrix, from portable and mobile devices to large industrial facilities. And the catalysts based on crystalline minerals consequently as well. As a final message, we suggest that we focus our scientific and technological attention more on vibration and less on matter, more on silicon and less on carbon * ! “If you want to understand the Universe, think about energy, frequency and vibration.” Nikola Tesla * Let's say that silicon, the base of crystalline mineral structures, presents a much more versatile crystalline structure than that of carbon, being able to generate more geometric patterns of molecular arrangements, with many more degrees of freedom, which results in richer vibrational patterns, or complex electromagnetic radiation, which consequently provide more versatile catalysts and easier energy conversions. Who we are? We are CAEXPERTS, Simulation Specialists! A technology-based company, specializing in projects, consulting, research, development and innovation in engineering, which has experienced technical consultants, pioneers in the implementation of computer simulation technologies in the national industry. We are technological partners of SIEMENS Digital Industries Software, and we have a wide range of engineering simulators, the most advanced in the world in each of their areas, in addition to scalable high-performance computing resources in the cloud. We have a unique way of working with our customers, being partners for technological development and innovation, adding our customers' knowledge to our experience, knowledge in advanced engineering, practicality, creativity and assertiveness, helping them to do more, faster and better. With the help of intensive engineering digitization, we help our customers to leverage their technological innovation potential, bringing years to months, and at a very competitive cost. We develop processes, products, equipment, systems, in the most diverse engineering disciplines, being experts in complex multiphysics interactions and resource optimization (costs, materials, weight, dimensions, energy, collateral impacts, durability, security, robustness, ... ). Book a conversation with us to learn more by clicking below!

What you will learn in this post: In this material, you will explore Synchronous Technology, the opinion of current users about it and the areas where this approach can save time and resources: Fast and flexible design creation Fast response to late-stage design changes Seamless editing of imported 3D CAD data Improved reuse of designs from other 3D CAD models Simultaneous editing of multiple parts in an assembly Easier simulation preparation Going beyond traditional modeling approaches to solve design challenges Remember that time you were almost done with a project and you got a last-minute change request? And when did you start implementing it and the model got completely out of whack? This is frustrating, isn't it? And that doesn't just happen with a single project, right? Reusing designs, handling imported data and making changes - why do such commonplace activities still pose so many challenges? Is engineering design not complex enough already? You spend most of your time at work, sacrificing vacations and facing staff shortages to keep up with all the projects. You engage in customer meetings, collaborate with suppliers, participate in conference calls, and hold conversations on the shop floor. And you are not alone! Isn't it time things got simpler? Isn't product development software supposed to be a tool to help you? Synchronous technology makes it possible to quickly create and edit conceptual designs, respond promptly to change requests, and perform simultaneous updates to multiple parts of an assembly. The reuse of projects, the manipulation of imported data and the implementation of changes are easily facilitated by the Synchronous technology, which assists in the activities that you routinely perform, making them more agile and convenient. Advantages of Synchronous Technology: We are all familiar with traditional modeling methods - direct and history-based - with their respective advantages and disadvantages. However, what if there was a way to combine the strengths of both modeling approaches, allowing you to design with the agility of direct modeling and the control and intelligence of history-based modeling? This possibility already exists: it is called Synchronous Technology in Solid Edge . Synchronous Technology in Solid Edge enables you to quickly create new conceptual designs, respond quickly to change requests, and perform simultaneous updates to multiple parts in an assembly. With this design flexibility, you can avoid the need for complex pre-planning, avoiding resource failures, rebuilding issues, and time-consuming rework. The power of Synchronous Technology makes it possible to treat cross-platform CAD data as if it were native formats, facilitating seamless collaboration with partners and suppliers. However, it is important to be careful. While many vendors claim to offer "flexible" modeling or a "combination of direct and feature-based modeling" approach, these approaches are not always equally effective. This text will show you how to ensure that you understand how the vendors you are evaluating are actually implementing this functionality and what the implications of this approach are. Synchronous Technology lets you focus on the design instead of worrying about the complexities of the CAD application. This means you can spend more time on product development, which is at the heart of your career. By eliminating low-value-added tasks, you recover more of your personal time. Choosing Approaches: Direct and History-Based Modeling Direct and History-Based Modeling Product development software vendors generally take one of two main approaches to creating and modifying geometry: direct modeling and history-based modeling (also known as ordered or feature-based modeling). Each approach has its advantages, but also presents specific challenges. Direct modeling, for example, offers ample flexibility. You can create and modify geometries by selecting them and then applying operations such as pushing, pulling, dragging, or rotating. The modifications are not registered by the software, that is, there is no saved history of the operations carried out, and the interrelationships are not maintained. History-based modeling is a structured process where a resource history tree, with parent-child relationships, is created to define the model. This requires prior planning of design intent, including dimensions, parameters, and relationships. History-Based Modeling: Powerful but Inflexible In history-based modeling, the structure and order of features determine how the model reacts to changes or edits. This results in predictable edits to underlying sketches using precise dimensional changes. This ability to control resources also allows you to easily automate changes and link resources. However, designers must plan carefully for model construction, as simple edits can be time-consuming and, in more complex cases, may require a complete rebuild. Also, if a model has a lot of features, recalculating them can affect performance, taking minutes to hours. Few options for editing imported geometries When dealing with imported geometries, which do not have associated features or parameters, making modifications is more complicated. This often involves recreating the design intent, often removing existing geometry and manually adding new features. In this process, you would use the parameters of these new features to drive the changes. As the project progresses, flexibility decreases as modifications are restricted to the definition of each feature. Scope is also limited by existing resources and parameters. Fragility of Complex Models When a change is made to a feature created early in the design, the edit affects the entire model from that point onwards. Features created after editing need to be recalculated based on the new entries, which can trigger a series of cascading failures. In many cases, modifying one feature can cause a chain reaction of bugs throughout the model, making it easier to start from scratch. 62% of CAD users agree that history-based modeling is powerful but inflexible, slowing down conceptual design due to time-consuming advance planning and making changes at later stages difficult. Direct Modeling: Intuitive but Limited Direct modeling does not keep a history of features or record the model creation process. There are no underlying feature sketches that define the part. Edits are performed by selecting the part to be modified and changing it - fast and simple. Since changes are not registered as features, subsequent edits do not affect system performance. However, due to lack of resources or history, direct modeling lacks accuracy in edits or automation through parametric inputs. Lack of Organization in Design and Complex Edits While it is possible to add dimensions and even create relationships in direct modeling, control over design intent and purpose is a weakness. This makes it difficult to automate smart changes. Furthermore, the lack of recognition of the relationships between different parts of the geometry can result in difficulties in creating accurate matches. The lack of organization and engineering intent in the models also makes it difficult to identify specific features and related groups that need to be changed. Dimension-driven editing is also less accurate compared to feature-based modeling. The Best of Both Worlds: Synchronous Technology to Solve Design Challenges What if there was a way to bring together the best aspects of each modeling approach, allowing you to design with the speed and simplicity of direct modeling, while maintaining the control and intelligence of history-based design? This possibility is already a reality: it is Synchronous Technology. Synchronous Technology in Solid Edge enables the agile creation of new conceptual designs, quick responses to change requests, and the simultaneous updating of multiple parts in an assembly. With this design flexibility, you can eliminate complex pre-planning, avoiding resource failures, rebuild issues, and time-consuming rework. Additionally, Synchronous Technology's ability to treat multi-CAD data as native files enables effective collaboration with partners and suppliers. Synchronous Technology: Fast and Flexible Synchronous Technology combines the strengths of direct and history-based modeling approaches, offering a unique set of capabilities. Users now have access to a powerful yet easy-to-use solution. Those who have tried Synchronous Technology have also reported that it has helped them overcome their main challenges: The Value of Synchronous Technology: More Agility Fast and Flexible Design Creation With Synchronous Technology, you can start conceptual designs immediately using integrated 2D and 3D sketches, without the need for time-consuming pre-planning. You work directly with the design geometry and can make changes instantly, while maintaining control through feature trees organized as needed. Precision in Direct Modeling Synchronous Technology offers the best of both worlds: the agility of direct modeling combined with precise parametric control, including face matching, scaling with design intent control, and intuitive 3D edits without the need for sketches. It's fast, easy, and most importantly, accurate. Agile Responses to Change in Advanced Stages With Synchronous Technology, making changes is simple, even for history-based models. Simply update reference dimensions or manipulate geometry, without worrying about feature failures, troublesome rebuilds, or lengthy rework. Simultaneous Editing of Multiple Parts in an Assembly Easily edit multiple parts in an assembly without the complexity of history-based edits or the need to establish relationships between parts. Select and drag to make changes. “Our process engineer advised me to taper the sides. This would have taken two hours in the ordered environment. With synchronous technology, it took one minute.” Daryl Collins, Designer, Planet Dryers “Through synchronous technology, the system has improved significantly. I'm really excited about how easy it is to operate. Synchronous technology means a quantum leap in the user-friendliness of 3D CAD systems.” Rainer Schmid, Gerente Geral Assistente e Coproprietário, Waldis The Value of Synchronous Technology: Easier Easy Editing of Imported Data With Synchronous Technology, importing files from other 3D CAD systems is as simple as opening them. Editing of imported data is performed by clicking and dragging the features. Dimensions can be added and edited in real time, and smart updates happen automatically, as if a history tree were present. Want to learn more about library migration to Solid Edge and support for other 3D CAD systems, click here ! Improved Design Reuse of Other Templates Easily reuse design details from other templates with a simple copy and paste. Synchronous Technology treats files in other CAD formats as if they were native to Solid Edge . Design Intent Recognition Synchronous Technology recognizes and preserves design intent in real time, enabling predictable and effective changes, speeding revisions. Preparation for Simulations Preparing a model for finite element analysis (FEA) is simple with Solid Edge Synchronous Technology , even if you are not a 3D CAD expert. Solid Edge provides easy-to-use tools for preparing FEA simulations, regardless of whether the geometry was created in Solid Edge or another 3D CAD tool. Harnessing the Power of Synchronous Technology in Solid Edge Solid Edge is an affordable, easy-to-use suite of software tools that cover all aspects of the product development process - from 3D design to simulations, manufacturing, data management and more. Synchronous Technology in Solid Edge combines the best elements of direct and history-based modeling in a single design environment. This allows you to design with intuitive discoveries, precise control, and the ability to capture design intent. The ability to make adjustments at any point and understand existing geometric relationships facilitates changes to feature-based models and imported geometry. The True Power of Synchronous Technology At the end of the day, what Synchronous Technology in Solid Edge really offers is the ability to focus on the design rather than the CAD tool. This means you can dedicate more time to the core activity of designing products, freeing up more personal time as low value-added activities are reduced. “Using Solid Edge with synchronous technology, I can actually do many more iterations now that I wasn't able to do before. And because of that, the cost of the product comes down. The weight of the product comes down. The performance The profit margin loves it.” John Winter, Gerente de Engenharia Mecânica, Bird Technologies Differences Matter While many vendors claim to offer "flexible" modeling or a combination of direct and feature-based modeling, not all approaches are created equal. When evaluating vendors, it's important to understand how they deliver this functionality and the implications of the chosen approach. "Translation" approach One approach maintains separate environments for direct and feature-based modeling and translates any creations or modifications between them. This approach may seem logical, but it can lead to problems. Feature-based modeling geometry follows predefined definitions, while direct modeling allows for more dramatic changes that may violate feature definitions. How to translate these changes? This approach still lacks clear solutions. "Featurization" approach Similar to the translation approach, this one maintains separate environments for direct and resource-based modeling, but registers actions as resources. This can result in many additional features and increased interdependent complexity. This can make models more prone to failure, and users can end up creating more complicated models than if they had only used feature-based modeling. Synchronous Approach Unlike previous approaches, Solid Edge takes a synchronous approach, leveraging the best of both approaches in a single environment. There is no back and forth translation and no hidden features to complicate the model. Synchronous Technology allows designers to make intuitive changes to design intent using the 3D model's own faces. Geometric relationships are automatically recognized and maintained, simplifying editing without user intervention. In short, Synchronous Technology in Solid Edge gives you the ability to design quickly, accurately and flexibly, eliminating many of the challenges found in traditional modeling approaches. This allows designers to focus on design, making the most of their work time and freeing up more personal time. If you're ready to experience the innovation of Synchronous Technology in Solid Edge and discover how it can revolutionize your designs, we're here to help. Schedule your meeting with us at CAEXPERTS and explore the future of design and engineering. Click below and book your time slot now for an exclusive demo. Se você está pronto para experimentar a inovação da Tecnologia Síncrona no Solid Edge e descobrir como ela pode revolucionar seus projetos, estamos aqui para ajudar. Agende sua reunião conosco na CAEXPERTS e explore o futuro do design e da engenharia. Clique abaixo e reserve agora o seu horário para uma demonstração exclusiva. Did you like it? They are and check out our post with some other features of Solid Edge by clicking on: Solid Edge: Designed to expand your business. Want to get an overview and learn even more about Solid Edge ? Click here !

Nowadays, sustainability is a hot topic, and that's no wonder. There are several reasons why this happens. Several countries around the world are striving to achieve net zero emissions. At the same time, organizations and ordinary people alike are doing their part to reduce the negative impact on the environment. In addition, they are establishing more effective ways to care for the planet. This goes beyond just doing the right thing – it's also a business movement, where organizations are taking environmental and social responsibility to drive positive change and sustainable economic growth. After all, sustainability makes good business. The US Environmental Protection Agency defines the pursuit of sustainability as “creating and maintaining conditions under which humans and nature can coexist in productive harmony to sustain present and future generations”. At Siemens, our technology partner, sustainability is an essential part of the overall strategy, which is organized through the DEGREE framework. This approach covers many areas, from reducing emissions to issues of ethics, governance, efficient use of resources, equity and employability. The energy industry and its significant role in advancing sustainability A look at different perspectives within the energy sector reveals fundamental key points: The energy industry is undergoing a global transformation that redefines our relationship with natural resources. In this context, sustainability emerges as the main guiding criterion. It is essential to consider sustainability at each stage of the energy value chain to minimize negative impacts. Investments in digital solutions play a crucial role in addressing complex transformation challenges and ensuring profitability and growth in line with environmental priorities. Sustainable business practices not only meet growing energy needs, but also safeguard the planet for future generations. The quest for sustainability is, at its core, simple. With knowledge and technology in hand, the challenge is to make it the top priority. While this is a challenging task, it is vital that everyone assumes their share of responsibility for ensuring a livable planet for generations to come. Energy production and consumption play central roles in developed economies. Although the quest for energy is inherent, different sources have different implications for sustainable development. It is crucial to adopt policies that drive economic growth and social progress without compromising the global environmental balance. The intrinsic connection between all life forms and nature is highlighted through a more holistic perspective. By recognizing the interdependence of all elements, the need to align our activities with nature's limits and opportunities emerges as a universal imperative. Minimizing resource extraction and restoring what was taken from the Earth is the key to sustainable coexistence. Energy is the engine of creation, encompassing all forms of life and matter. Taking advantage of it in a sustainable and conscious way is essential to guarantee a better quality of life for us and the next generations. A lifestyle geared towards reducing consumption, with an emphasis on reuse and recycling, is fundamental to preserving the natural resources that sustain life in all its manifestations. Incorporating these principles across industries, from design to product maintenance, is a route to a more harmonious future. The adoption of strategies and actions that place environmental and social responsibility as a priority has a lasting impact on the economic scenario. Energy efficiency, renewable energy, responsible management of chemicals and circular economy practices are clear examples of how companies can reduce their environmental impact. This results in sustainable development and generation of value for all stakeholders in the long term. Sustainability and Innovation: Producing Sustainable Batteries In the current scenario, the demand for sustainable energy drives challenges in the production of high quality batteries. The partnership between CAEXPERTS and Siemens, leaders in technology and innovation, offers a pioneering approach. Digitization is the essential tool for aligning sustainability and quality. CAEXPERTS brings expertise in digital engineering, while Siemens offers advanced solutions. Together they are shaping efficient and sustainable batteries, responding to the urgency of reducing our environmental impact. The transformation encompasses the entire value chain, from design to production, promoting efficiency and innovation towards a sustainable future. Driving sustainability forward with digitalization Looking to the future, digitalization plays an important role in the pursuit of sustainability. The World Economic Forum predicts that digital solutions can contribute to a global reduction of up to 20% in emissions. Technologies such as artificial intelligence, machine learning and the Internet of Things are being used to predict energy demand and improve efficiency. CAEXPERTS is dedicated to being a partner in the pursuit of sustainable growth in industries. Our high-performance and value-added technological solutions are a reflection of this commitment. We have a team experienced in advanced engineering and digital engineering solutions (CAE, computer-aided engineering), as well as expert consulting services and virtual prototyping. With scalable hardware and software resources in the cloud, we can develop custom solutions to meet every need. Our focus on computer simulation allows us to analyze and optimize systems and processes in energy, environmental and economic terms, optimizing costs and design times. In addition, we are at the forefront of research and development projects, where intensive digitalization is applied to reduce costs and accelerate the development of clean energies, shaping the future. We are available to collaborate as an innovation partner in the market. Our ability to conduct large-scale research and development projects, with an emphasis on digitization, is an effective tool for driving technological advances. CAEXPERTS is ready to be your partner in the journey towards pioneering clean energy solutions. Contact us to schedule a meeting and explore how we can contribute to a sustainable and innovative future.