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

Simcenter E-Machine Design and Simcenter Amesim working together for better electric motor design The typical electric motor design sequence involves many iterations, especially during the early design stages. Identifying the most important load points for a given design problem is necessary but complex. Our release of the Simcenter Motorsolve software in 2020 added a new set of experiments which leveraged user-defined duty-cycles. This capability has been enhanced in Simcenter Motorsolve’s replacement, the Simcenter E-Machine Design software. With an exchange of the machine performance requirements from Simcenter Amesim , Simcenter E-Machine Design can use realistic vehicle behavior to advance the design process. The losses and top five most important load points are calculated and transferred between the software. Load point workflow between Simcenter Amesim and Simcenter E-Machine Design This technology includes several standard Electric Vehicle drive-cycles for the automotive sector. To activate this feature, the user simply defines the desired vehicle torque and rotor speed details. Pulse Width Modulation analysis with arbitrary voltages Calculating machine performance based on measured or arbitrary voltages using traditional finite element analysis (FEA) can be time-consuming and impractical due to the signal’s switching frequency. In Simcenter E-Machine Design , the Pulse Width Modulation (PWM) analysis experiments utilize analytical analysis coupled with FEA to determine accurate performance in a timely fashion. An additional option of assigning user-defined arbitrary voltages to the Phase Windings is now part of the PWM analysis capability. Therefore, digital twins or model calibrations can be based on measurements imported directly from dynamometers or other sources. User-specific arbitrary voltage profile Halbach Array electric motor design in Simcenter E-Machine Design Halbach array template in Simcenter E-Machine Design Rotor templates support the creation of Halbach array patterns with even and odd numbered magnet segments per pole. It also includes the ability to apply unevenly distributed segments with user-defined magnetization directions. As a Halbach array generates the poles in a desired volume (the air-gap), there is a secondary benefit to the Rotor design. There is flux cancellation in the volume where the core would be, and so no back iron or steel is required; a nonmagnetic lightweight core can be used instead, significantly reducing the mass of the Rotor. “…electric motors based on the Halbach array offer measurable benefits over conventional designs, including high power density and high efficiency. One of the enablers of these benefits is that a Halbach array motor does not require Rotor laminations or back iron, so the motor is essentially ironless. This significantly reduces eddy current losses and hysteresis losses… “ Excerpt from “What is a Halbach array and how is it used in electric motors?” by Danielle Collins highlights the benefits of Halbach array electric motor designs. Maximum torque and flux weakening control Motor performance is highly dependent on the control strategy. This link between the motor and the electronics impacts performance parameters like efficiency, loss and the machine’s output power. Simcenter E-Machine Design continues to support these two key control strategies. Maximum torque per amps Flux weakening based on optimal load points You can have confidence that your experimental data more accurately replicates the physical conditions using these control strategies. Efficiency map based on MTPA and Flux weakening The figure above showcases the Efficiency map experiment for an electric machine where the newly added MTPA drive cycle and the Flux weakening control strategy are combined. Simcenter E-Machine Design impacts the electric machine design process. Significantly improve your efforts by including realistic vehicle behavior and control strategies in your experimental outcomes. To learn more about Simcenter E-Machine Design please consider the following: Want to learn how Simcenter E-Machine Design and Simcenter Amesim can transform your electric motor design? Schedule a meeting with CAEXPERTS and discover how our integrated solutions can streamline your analysis, save time, and improve your design efficiency. Don’t miss this opportunity to take your electric motor development to the next level! Cel.: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Team competed in the electric battery prototype category E3 UFSC   (UFSC Energy Efficiency Team) set a new Latin American record at the Shell Eco-marathon Brazil 2024 , reaching 381 km/kWh  with its electric battery prototype. This result was achieved in collaboration with CAEXPERTS  and Siemens Digital Industries Software ,  which plays a key role in the success of teams like E3 UFSC , offering a full range of software focused on design, simulation, and analysis. CAEXPERTS  provided support and training, and together with Siemens   enabled the E3 UFSC  team to access tools such as NX , Simcenter STAR-CCM+ , Simcenter 3D , and Solid Edge , which are essential to optimize the performance of the ultra-efficient prototype developed by the team.               E3 UFSC  was founded in 2009  with the mission of developing prototypes of ultra-efficient vehicles, focused on sustainable solutions. The team is composed of 28 members from various courses at the Federal University of Santa Catarina , covering areas such as mechanical engineering, electrical engineering, control and automation engineering, as well as disciplines such as pharmacy and geography, reflecting the interdisciplinary and collaborative nature of the group. In addition, the team has achieved notable results in previous competitions, such as 1st place  in the Shell Eco-marathon Brasil 2017  (petrol category), with a record of 525.7 km/L  and 1st place  in the Decarbonasing the Home challenge in 2021 .   Actively participating in the Shell Eco-marathon , the team is dedicated to building vehicles designed to maximize energy efficiency, setting new standards in the competition. The main objective of the E3 UFSC  prototypes is to minimize energy consumption, demonstrating that it is possible to combine performance and sustainability in innovative solutions. Source: https://www.instagram.com/reel/C_lq0fCpfVz/?igsh=MWhycTlsNXYyN3A3OQ==   With an eye on the future, the team plans to participate in the Shell Eco-marathon Americas  and develop new projects, with a focus on building an ultra-efficient urban vehicle. Innovation in energy efficiency and sustainability continues to be the main focus of E3 UFSC , which seeks to push its own limits with each new competition. The SIEMENS, CAEXPERTS and E3 UFSC Partnership CAEXPERTS not only provided the software, but also provided in-depth technical training and ongoing support. This included assistance with complex simulations and providing strategic knowledge to optimize results. With CAEXPERTS ' expertise , E3 UFSC was able to seamlessly integrate simulations into its design process, creating predictive models   to understand how parts would behave under competition conditions. The team is in a constant state of evolution and improvement, always seeking to innovate in different areas of the project. Simulations are a crucial part of this journey, allowing E3 UFSC  to optimize its designs based on accurate data and predict vehicle performance in different situations, which directly contributed to the record achievement.   Emerald Prototype Thanks to this collaboration between CAEXPERTS  and Siemens , E3 UFSC  is not only able to carry out faster and more accurate simulations and tests, but also continually improve its designs, ensuring that the prototype is always optimized for the extreme conditions of the competition.   ​ Source: https://www.youtube.com/live/PXA5gRzkdhQ?t=2007s The Shell Eco-marathon Competition   The Shell Eco-marathon  is a global competition that challenges university students to design, build and operate vehicles that are as energy efficient as possible. The goal is simple but challenging: to create a car that travels the longest distance possible using the least amount of energy. This includes using conventional fuels such as gasoline, but also alternative sources such as electric batteries and hydrogen.   The competition originated in 1939 in Shell's laboratories in the United States, starting as a bet between scientists on who could achieve the greatest fuel efficiency in their experiments. However, the modern form of the competition was officially created in 1985 in France, and has since expanded to various regions of the world, including Europe, the Americas and Asia.   There are two main categories:   Prototype : Ultralight vehicles, with a design focused exclusively on maximizing energy efficiency, reducing friction and weight to the extreme. Urban Concept : Cars designed to look more like street vehicles, but with sustainable technologies and a focus on efficiency.   The competition takes place annually and involves rigorous technical inspection phases, where the cars are evaluated in terms of safety, design and compliance with the rules. Only after they have been approved can the vehicles be tested on the track. The competition is not based on speed, but on energy efficiency, with teams aiming to cover the maximum distance with the least amount of fuel or energy. Currently, E3 UFSC  competes only in the Battery Electric Prototype category, where it has already achieved remarkable results. However, the team has plans to expand into the Urban Concept  category  in the future, developing an ultra-efficient vehicle for this class.   E3 UFSC wins Shell Eco-marathon 2024   E3 UFSC   achieved an impressive milestone at the Shell Eco-marathon Brasil 2024 , setting a new South American record  for energy efficiency. Competing in the Electric Battery Prototype category , the team achieved the 381 km/kWh  mark, which represents a remarkable feat in terms of efficiency. To give you an idea, this distance is equivalent to traveling more than 3,000 km with just one liter of gasoline , consolidating the team's excellent technical performance.   This achievement was the result of hard and innovative work by the team, which modified around 80% of the vehicle   compared to previous versions. The main changes include:   The implementation of a new transmission system , allowing greater energy conservation during vehicle operation. The development of carbon fiber wheels , designed and manufactured by the team members themselves, which drastically reduced the weight of the vehicle and improved its aerodynamics. A new drive and controller system , which was essential to optimize the use of electrical energy during the route. Changes to the monocoque  improving aerodynamics and reducing weight , optimized in CFD. These innovations were tested and optimized using Siemens  simulation tools , which allowed the team to perform detailed analyses of different scenarios and refine the prototype design to maximize its efficiency. NX  is widely used for CAD modeling, allowing the team to explore different geometries and adjust the vehicle design to maximize aerodynamic and structural efficiency. Simcenter STAR-CCM+  enables computational fluid dynamics (CFD)  simulations, which are essential for developing the vehicle’s aerodynamics and thermal control.   These tools not only accelerate the development cycle, but also enable high-fidelity analysis that would be impossible without the use of predictive simulations. The Future   The partnership between E3 UFSC  and CAEXPERTS  is increasingly promising, with the prospect of continued growth and development of new projects. With the constant support of CAEXPERTS , the E3  team is in a state of continuous improvement, always looking for ways to innovate in the design and efficiency of its prototypes. In addition, E3  plans to expand its operations, especially with the development of a vehicle for the Urban category   of the Shell Eco-marathon.   Sapphire Urban Prototype The partnership with E3 UFSC  reflects this dedication to continuous innovation   and the development of young engineers . By supporting the team with the most advanced simulation and design tools, Siemens  and CAEXPERTS  are empowering future professionals to solve engineering challenges with agility and precision. This commitment is not limited to technical support, but also extends to knowledge transfer and preparing students for the job market, as already seen in other successful partnerships with Formula Student  teams and other academic competitions.   WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Characterization and Optimization of Flows in Bioreactors The design of bioreactors presents challenges that go far beyond those of conventional reactors due to the use of living cells and microorganisms. The characterization and optimization of currents and flows in these equipment, as well as the adequate control of flow speed, concentrations and temperature, for example, are essential points that require a lot of attention from bioreactor engineers and operators. Microorganisms (animal cells, plant cells, bacteria, fungi and viruses), often used for the production of modern pharmaceutical and cosmetic compounds, are particularly sensitive to chemical and physical stresses. The homogeneity of the mixture, adjusted by the rotation of the impeller, is important to avoid chemical stress, while physical stress can be controlled by balancing the agitation and avoiding shear stresses that harm the organisms. Therefore, bioreactors and fluid flows within the tank must be well characterized. If key engineering parameters such as energy consumption, mixing time, and mass transfer coefficient (oxygen) are well known, it is possible to optimize the growth and productivity of organisms while maintaining high product quality. In addition, trial and error experiments, which are time-consuming and cost-intensive, can be reduced, which is especially important if the availability of biological material is limited, as is the case with primary tissues or stem cells. Challenges on an Industrial Scale Understanding and correctly modeling the complex interactions between biological and hydrodynamic phenomena is essential in bioprocesses. When scaling up a bioreactor from laboratory to industrial scale, it is common to observe a decrease in productivity. This is usually due to the decrease in mixing efficiency as the reactor size increases. With increasing volume, steep gradients of substrate, dissolved oxygen and pH arise, which can alter biological responses, both in terms of physiology and metabolism, compared to small-scale cultures. Another important challenge is the shear forces in stirred tank bioreactors – larger volumes imply higher stirring speeds to ensure the mixture is homogeneous – which can impair the attachment of cells to microcarriers, causing collisions and cell damage. Therefore, it is necessary to predict the hydrodynamic behavior in bioreactors of different sizes and their interaction with biological reactions to ensure scale-up success and mixing efficiency. The use of Computational Fluid Dynamics (CFD) allows us to understand and adjust these phenomena, making scale-up more efficient and minimizing problems, optimizing the performance of industrial bioreactors. Use of Computational Fluid Dynamics (CFD) in Bioreactor Design Computational Fluid Dynamics (CFD) can provide detailed modeling of hydrodynamics and mixing to properly size both the process and the equipment. In this scenario, Simcenter STAR-CCM+ offers complete solutions, allowing simulation of not only fluid dynamics, but also chemical reactions and heat transfer, using multiphysics coupling. This makes it possible to more accurately model the physical and chemical dynamics of bioreactors, addressing mixing and performance challenges in an optimized way. In the following topics, we will explore case studies that apply CFD to bioreactors using STAR-CCM+ ,  demonstrating how this technology can predict and optimize key variables such as mixing efficiency and liquid accumulation. Mass Transfer in Gas-Liquid Flows Mass transfer in gas-liquid flows is a common phenomenon in the chemical and bioprocess industries. In this case, air is considered to be dispersed in water, through which oxygen dissolves in the water. This is a very important process for bioreactor  applications , playing a role in maintaining optimal conditions for biological processes. To model this process, the Eulerian multiphase approach was used, together with a population balance model to capture the bubble size distribution. The liquid and gas phases were treated as multicomponents to account for oxygen dissolution, with turbulence modeled by the KE model and the dispersed phase flow by the Issa model. Interphase interactions were described by drag and turbulent dispersion models, while Henry's Law was used to calculate mass transfer. The results showed that gas hold-up, i.e., the ratio between gas volume and tank volume, is a key metric for phase interaction. A higher hold-up increases the interfacial area and mass transport. The dissolved oxygen fraction converged to the saturation value (~8.24 mg/L at 25°C), and the simulation showed a higher gas concentration around the impeller axis due to centrifugal action, with less gas in the peripheral areas. These results highlight the efficiency of the modeling and its importance in gas-liquid interaction to optimize industrial mass transfer processes.   Design Optimization at the Innovation Process Center In the case study conducted by the Innovation Process Center , a design exploration approach was adopted to identify solutions that would deliver significant performance improvements before building physical prototypes. This method allowed for a more accurate and efficient analysis of design options, saving resources and time. Among the improvements achieved, a 40% increase  in mixing performance within the system stands out , which resulted in a significant reduction in costs and time required for development. In addition, the oxygen supply in the process was optimized, achieving an overall increase of up to 17%,   which contributed to improving the reactor's efficiency. The optimization process involved evaluating a complex range of parameters to maximize reactor performance. This was done by constructing a physics pipeline that analyzed velocity, mass transfer, and species present in the system for a variety of parameters. This significantly narrowed down the testing options, focusing on those alternatives with the highest chance of success. The criterion with the greatest impact identified was oxygen transfer rate, which is essential for optimal system performance. As engineer Alex Smith pointed out, “Instead of testing 25 options, we can focus on the ones that have the highest chance of success, saving time and cost.” This approach has resulted in a faster, more efficient design process. Improving Bioreactor Efficiency at the University of Los Andes The University of Los Andes conducted a case study with the aim of improving the efficiency of bioreactors in its wastewater treatment plant. To achieve this goal, the CFD simulation method was used in the design of bioreactor mixing vessels, seeking to optimize process performance. The analysis focused on improving mixing efficiency in the reactor and reducing energy consumption. The challenge was to perform CFD analysis based on injection points, designing different configurations and adding baffles to improve mixture homogeneity. The simulation results revealed unnecessary energy consumption in the current reactor, highlighting the need for system adjustments. Various jet agitation configurations and the inclusion of baffles were tested to explore the design space and identify the most efficient solution. The final design was able to balance sufficient mixing, adequate residence time, and reduction of process short circuits. Furthermore, the best solution was identified using the virtual model, allowing for a significant reduction in operating costs. The impact of the project was remarkable. According to Jorge Lopez of the University of Los Andes, “it is possible to obtain a homogeneous mixture in an anaerobic MBR system without the need to include a mechanical agitator.” The study demonstrated a 50% reduction in energy consumption  , reinforcing the effectiveness of using CFD simulation to optimize industrial processes and reduce energy costs. In conclusion, the use of advanced CFD techniques and software with STAR-CCM+  has proven to be essential for the characterization and optimization of bioreactor flows. Through detailed modeling, it is possible to improve mixing efficiency, minimize stresses on organisms, optimize mass transfer and reduce energy consumption. Case studies demonstrate that CFD simulation allows identifying effective solutions to design and scale-up challenges, resulting in more efficient and productive processes, saving time and resources. Interested in improving the performance of your bioreactors and optimizing industrial processes? Schedule a meeting with CAEXPERTS  and find out how our advanced Computational Fluid Dynamics (CFD) solutions using Simcenter STAR-CCM+  can help you characterize and optimize flows, saving time and resources in the development of your project.   WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br References DELAFOSSE, Angélique et al. CFD-based compartment model for description of mixing in bioreactors.  Chemical Engineering Science , v. 106, p. 76-85, 2014. WERNER, Sören et al. Computational fluid dynamics as a modern tool for engineering characterization of bioreactors.  Pharmaceutical Bioprocessing , v. 2, n. 1, p. 85-99, 2014.

Ignoring the profound impact of strong two-way coupling in the Fluid-Structure Interaction (FSI) between prosthetic heart valves and blood during the design process will result in suboptimal valve designs and could, ultimately, lead to heart failure. Fortunately, Simcenter STAR-CCM+ gives you all the tools you need to design for maximum longevity and safety of any type of valve. 35 million beats per year Your heart tirelessly beats approximately 100,000 times a day, totaling a staggering 35 million beats in a year. With each beat, your heart valves diligently open and close, facilitating the vital task of pumping blood through your arteries. Serving as one-way inlets or outlets from the heart muscle to the ventricle, these valves prevent the backward flow of blood. But what if one day there is a flaw in that clockwork? Unfortunately, sometimes even irrespective of how much one takes care, reality for some is that their heart may at one day simply not be functioning anymore as it should: the aortic valve has two basic failure modes called Stenosis and Insufficiency. Stenosis describes the narrowing of the valve orifice due to reduced leaflet excursion and opening of the valve, restricting the flow of blood in flow direction. Insufficiency describes the inability of the valve to close quickly enough, creating leakage of blood against the flow direction. Both failure modes will largely increase the workload on the heart and ultimately lead to heart failure. The trileaflet heart valve And so, unfortunately, some people face issues with their natural valves, leading to the necessity of replacing them with prosthetics. Considering the heart’s extraordinary workload of over 35 million cycles annually, the careful and precise design of these prosthetic trileaflet heart valves is not just important but critical. That is why today, I will demonstrate how Fluid-Structure-Interaction (FSI) simulations can aid engineers in the design of safer, longer-lasting prosthetic heart valves (PHV) and guide you through the effects that a Multiphysics approach will have on enhancing the precision and reliability of heart valve simulations and compare it to a single physics approach. To model strong two-way coupled Fluid-Structure Interaction (FSI) applications, where a dense fluid interacts with a flexible structure and vice versa, is one of the most intricate challenges in the realm of multiphysics. To predict the dynamic interaction between fluid and structure, engineers need dedicated sophisticated simulation capabilities. And while modeling the FSI in a heart valve is a highly complex engineering challenge it has the potential to transform the everyday of many, for the better. Why FSI? Despite the highly nonlinear nature of opening and closing of a PHV, where the thin leaflet membrane experiences a snap-through instability transitioning between open and closed positions with virtually zero stress, it successfully captured these motions. In his set-up, the opening and closing of the valve was driven by a pressure boundary condition on the leaflet surface, mimicking the blood pressure. The time-dependent pressure curve was derived from experimental data on the differential pressure between the inlet and outlet of the valve. While this is a remarkable dynamic simulation, it is not really new and could have been done with other simulation tools as well. And, more importantly, a significant effect was ignored here – the valve leaflets don’t operate in isolation; every minuscule movement of a leaflet affects the surrounding blood, creating fluctuations in pressure, modifying flow patterns, or even inducing turbulence. Simultaneously, the fluid’s motion and the pressure exerted on the leaflet surface induce or dampen deformations and accelerations of the leaflet. Because of the blood’s high density, the ratio between the mass of the leaflet and fluid mass displaced by the leaflet is nearly one. Together with the very low stiffness of the leaflet, this indicates a strong two-way coupling between fluid and solid. Disregarding these strong Fluid-Structure Interactions will lead to inaccurate predictions of the valve dynamics. The video below provides a visual comparison of the leaflet dynamics with and without considering FSI effects, e.g. the dampening effect of the blood on the leaflet and the acceleration of blood through the leaflet displacement. As you can see, disregarding the two-way FSI leads to an overprediction of the opening and closing speed of the valve, which in return may lead to a leaflet design that does not open and close quickly enough when interacting with the surrounding blood. As mentioned earlier, this will cause Stenosis and Insufficiency and increase the workload on the heart with heart failure as a potential outcome. The next animation shows the difference in the shape and size of the orifice during valve opening between a simulation without FSI and with FSI effects included. It is evident that the coupling between blood and leaflet structure has not only an effect on the speed at which the valve opens but also on the shape of the orifice once the valve is opened. This shows that only a correct simulation of all the two-way coupled FSI effects will allow to design an optimal leaflet shape and thickness with maximum efficiency and longevity and therefore safety for the patient. The challenge of modeling strong two-way coupling in FSI Modeling strong two-way coupled Fluid-Structure Interaction (FSI) presents inherent challenges owing to the distinct physics and dynamics of fluids and solids, each governed by different equations and continuum discretization methods. In the context of a prosthetic heart valve (PHV), where the fluid adheres to Incompressible Navier-Stokes equations and the solid follows a hyper-elastic material law, coupling these equations becomes a complex endeavor, particularly at the fluid-structure interface. Two fundamental physical conditions must be satisfied at this interface. The kinematic condition dictates identical velocities for fluid and solid, essentially preventing the fluid from detaching from the solid or penetrating it and ensuring their cohesive motion. The dynamic condition balances pressures and forces at the fluid and solid sides of the interface and dictates a force equilibrium. In the case of a strong two-way coupled FSI application like the prosthetic trileaflet valve, the thin leaflets displace a substantial mass of blood and encounter large variations in pressure, leading to extensive deformations and accelerations, making it inherently challenging to satisfy the two physical conditions. Tackle the toughest FSI problems with Simcenter STAR-CCM+ Simcenter STAR-CCM+ addresses these challenges by introducing the new FSI Dynamic Stabilization Method and the 2nd Order Backward Differentiation integration scheme for solids. These enhancements significantly improve convergence and stability in simulations of such complex systems and ensure full kinematic consistency across the FSI interface for first and second-order time integration. In addition, satisfying geometric conservation is crucial, requiring the fluid and solid meshes to move synchronously at the interfaces. Simcenter STAR-CCM+ excels in this aspect through effective use of mesh morphing, overset meshes, and dynamic re-meshing, ensuring harmonized movement of meshes even in this case, where the large leaflet deformations lead to dramatic changes of the fluid domain. More accurate, stable, and reliable modeling of strong two-way coupled FSI In essence, these advancements in Simcenter STAR-CCM+ pave the way for more accurate, stable, and reliable modeling of strong two-way coupled FSI, particularly in intricate applications like prosthetic heart valves. There aren’t many instances where CFD can literally be called life-changing. This time I feel it is fair to say so. Disclaimer No hearts were broken during the making of this post. 😊 Want to learn more about Simcenter STAR-CCM+ and how it can transform your computer simulation? Schedule a meeting with CAEXPERTS to find out how our solutions can optimize your engineering projects! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Why Simcenter Nastran is used to model airframes Airframe structures must undergo a rigorous evaluation process to become flight certified. Finite element software is a critical tool used in the process as it enables simulation of the airframe to predict stress and deflections for many flight conditions. Linear finite element methods are most typically employed because of their efficiency of computation for a large number of load cases. Under linear behavior, structures have the same stiffness independent of how they are loaded. This is a generally valid assumption however, in airframes with thin skin, the linear behavior assumption may not be valid. When the load on the airframe changes The skin structure not only provides the lifting surface but also supplements the load-carrying capacity of the spars, ribs, and stringers. The skin carries mostly membrane and shear loads. When the skin is in a tension-loading condition, it can carry both membrane and shear loads. But if in compression, depending on the framing, it can locally buckle. In such cases, it can no longer carry membrane loads but can still carry shear loads. Existing modeling methods In the past, airframe analysts have modeled this behavior using manual techniques. They start with a base model that uses standard shell elements to model the skin. In Simcenter Nastran, shell elements are modeled with CQUAD4 elements with the PSHELL physical property. This element type has both membrane and shear stiffness. Then analysts perform a loads analysis to locate areas where the shell elements are in compression. In those areas where the compression level is significant enough, the analyst would create a new model by replacing the compressed shell elements with elements that only carry a shear load. In Simcenter Nastran, this is the CSHEAR element with the PSHEAR property. As can be imagined, this is a tedious effort and requires creating multiple models corresponding to various load conditions. In addition, it becomes difficult to manage the models which makes certification more challenging. The New modeling method A recent enhancement has been made to Simcenter Nastran Multistep Nonlinear (SOL 401) to simplify the workflow for this use case. A new element formulation has been added that changes the stiffness characteristics of the shell element based on whether it is in tension or compression. Thus, only one model is needed for all the various load conditions. In the new workflow, users again use a base model with shell elements. But the shell elements reference a new type of physical property called a shell/shear panel using the PSHLPNL physical property. The new property type has definitions for both membrane and shear stiffness properties. This new formulation is sometimes referred to as a tension-only quad element because it carries no compressive loads. How the new solution works The Simcenter Nastran Multistep Nonlinear (SOL 401), as the name implies, is a nonlinear solver and iterates on the stiffness until the residual forces are eliminated. Initially, the solution starts with the shell/shear panel elements with shell stiffness behavior. During the iteration process, the solver will check the internal loads in the shell/shear panel elements and the ones that have compression are converted to the pure shear formulation. Converged solutions are typically achieved within a few iterations, so solution times do not take much longer than a linear solution. Additionally, users can solve many load conditions in just one solution. The set-up Users have several settings to consider when defining the properties of the shell/shear panel element. The properties for the shell and shear behavior are the same as for standard shell and shear elements. While the new settings control conversion from shell to shear. There are two main settings in this regard: Stress direction for conversion Stress level for conversion For the shell/shear panel the user needs to define the stress direction that will be used to determine whether the element is in tension or compression. Options include the element X or Y direction, material X or Y direction, or the minimum principal stress direction. The software will compute the normal stress in this defined direction and is then compared to the user-defined conversion stress level. The default conversion level is 0.0, but it is often advised to use a small negative stress level instead. Figure 1: Example Wing Model Figure 1 shows an example wing structure that is using the new shell/shear panel elements shown as the darker blue elements. The skin thickness in these areas varies from 1.2 mm to 2.0 mm. The stress direction aligned with the stringer orientation is used for the stress component to determine the conversion behavior. A material coordinate was applied to these elements to define the desired direction. Figure 2: Material Co-ordinate system for stress evaluation, used in stiffness conversion Figure 2 shows the material coordinate system assigned to the shell/shear panel elements. The arrows are showing the X direction of the material coordinate system which is the direction chosen for the stress evaluation. The stress level for conversion was set to -6 MPa for the thinner skin section to -10 MPa for the thicker section. For the analysis, the wing is fixed where it would attach to the fuselage. A set of five force loadings were applied along the wingspan corresponding to various flight conditions. Results Figure 3 shows the deflection (in units of mm) from two of the load cases. Case 3, on the left, shows a downward deflection, and Case 4, on the right, shows an upward deflection. Figure 3: Deflection under load case 3 (left) and 4 (right) One of the new results with the shell/shear panel elements is a status value. A value of 0.0 indicates that the element has not converted to shear behavior and a value of 1.0 indicates it has been converted. It is noted that each load case is solved independently and starts from an unconverted condition. Figure 4: Tension Only Quad Status for load case 3 (left) and load case 4 (right), upwards loading in the top views and downward loading in the bottom views. In load Case 3, where the wing deflects downward, the skin elements on the bottom of the wing are in compression and hence can be seen to have been converted. In load Case 4, where the wing deflects upward, the opposite occurs and the skin elements on the top of the wing are in compression and are converted. Stress results on the shell/shear panel elements can also be displayed. Figure 5 shows the normal stress in the material X coordinate system. For elements that have converted, there is no longer any normal stress and subsequently, there are no results on these elements. For the unconverted elements, the normal stresses are seen in tension. Figure 5: Normal Stress XX for load cases 3 (left) and 4 (right) Figure 6 and 7 shows the shear stresses for the same load cases. The contours in Figure 6 show the shear stresses in the converted shell/shear panel elements. Again, for load case 3, the contours are only on the bottom side of the wing where conversion occurred, and for load case 4, the contours are on the top side of the wing. Figure 6: Shear stress XY for load case 3 (at left) and 4 (at right) on top of the converted shell/shear panels The contours in Figure 7 show the shear stresses on all unconverted elements and the standard shell elements. Figure 7: Shear stress XY for load case 3 (left) and case 4 (right) on the unconverted shell/shear panels and the standard shell elements Do you want to optimize your airframe modeling and ensure accurate analysis with Simcenter Nastran ? CAEXPERTS can help your team implement best practices and take full advantage of advanced finite element solutions. Schedule a meeting with us and find out how we can improve your simulation and certification processes in an efficient and innovative way! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

CAD-embedded CFD simulation The new Simcenter FLOEFD 2406 software release enhances integration across Simcenter portfolio with import from Simcenter Flotherm XT software, introduces integration with Siemens NX PCB Exchange tool for greater workflow opportunities, adds Python scripting support for automation, speeds up handling of large CAD assemblies and much more. Read on to learn how new electronics cooling simulation oriented features and overall software enhancements that help you stay integrated, model the complexity, explore the possibilities and go faster in your simulation processes. Import Simcenter Flotherm XT models into Simcenter FLOEFD To enable easier model interchange and enhance communication between users and between organizations, you can now import a Simcenter Flotherm XT model into Simcenter FLOEFD and utilize the model set up from the original model. Export from Simcenter Flotherm XT and import into Simcenter FLOEFD This also helps users who are selecting to transition to using Simcenter FLOEFD to leverage a CAD-embedded analysis environment and take advantage of multi-physics oriented workflows including thermo-mechanical stress analysis capabilities within Simcenter FLOEFD . Below is a video showing the steps for importing exporting a thermal model from Simcenter Flotherm XT and then importing into the Simcenter FLOEFD . Leverage PCB Exchange with Simcenter FLOEFD PCB Exchange is an ECAD-MCAD bi-directional collaboration tool from Siemens Digital Industries Software allowing users to create and modify NX models leveraging EDA data. Capabilities have been added to PCB Exchange recently to create a simcenter FLOEFD project. The main capabilities are as follows: Create a Simcenter FLOEFD project directly from PCB Exchange EDA data is transferred as a Smart PCB, that users are familiar with PCB Exchange supports creation of wirebonds PCB Exchange is compatible with Simcenter FLOEFD for NX and Simcenter FLOEFD SC ( Simcenter FLOEFD for Simcenter 3D environment). Below is an extended demonstration video of a power electronics module thermal model analysis with the steps for importing PCB information shown using PCB exchange and the IDX file format and in particular components with wirebonds (via CCE file). Wire bonds are important to model in these types of applications. Model thermal vias quickly and easily in Simcenter FLOEFD 2406 New PCB thermal via modeling capabilities have been added to the Simcenter FLOEFD EDA Bridge so you can more easily explore thermal management options: Quickly add thermal vias by defining under a component Thermal vias are created as a cuboid representation of an array with orthotropic material properties when transferred to Simcenter FLOEFD How to add a thermal via representation under a component in EDA Bridge A thermal via region is created quickly by first selecting the relevant component and then adding the Thermal Via Region. How to edit PCB thermal via properties How do thermal vias appear in Simcenter FLOEFD 2406 Within Simcenter FLOEFD , a Thermal Via assembly is created within the parent component assembly. Geometry is created for each dielectric layer of the PCB. No geometry is created for the conducting layers since the additional conducting material from the via region is negligible. A material with an effective biaxial conductivity is automatically calculated from the thermal via properties and attached to each object in the thermal via assembly. Use a local system for point parameters You can now convert local coordinate systems to define point parameter locations. This means you can convert local system coordinates to a global one. For example if you select a local coordinate system, paste coordinates in from a table or import from a file, then you will be prompted if you want to convert them to global coordinates. Utilize simulation automation: PYTHON scripting support in EFDAPI Python is a widely used, popular scripting language for automation across engineering tools and functions. Simcenter FLOEFD 2406 introduces Python support for automation within the Simcenter FLOEFD API (the new EFDAPI was introduced in Simcenter FLOEFD 2312 ) . This opens up opportunities for pre-processing, simulation solve and post processing automation tasks. You can also pursue automating Simcenter FLOEFD operations within in multi-tool workflows for you analysis process. There are documentation and scripting examples available on Support Center to assist users. Below is a short simple demonstration video illustrating a Simcenter FLOEFD thermal analysis with all conditions, features and heat sources being created via Python script and how the simulation results are being post-processed and exported as an excel spreadsheet and graphic files. This is illustrated for an electronics cooling simulation model of a boost converter. Faster handling of large CAD assemblies It is now much faster to open, create and clone projects that contain thousands of component to 100K+ components. Of course any speed up is model dependent, 1.5 – 5 x faster for a model with 45K component to 100 to 150 x for a advanced package with 125K components (i.e lots of solder balls). Smart PCB thermal analysis memory consumption improvement The Smart PCB is one of several options for PCB thermal modeling and it is constantly being enhanced for speed, and memory use optimization. Smart PCB is a sophisticated approach to efficiently capture the detailed material distribution of a PCB without the added computational resource and time penalties typically required to model the PCB explicitly. It does this by using a network assembly approach , whereby a voxel-style grid based on the images of each PCB layer in imported EDA data is generated. In Simcenter FLOEFD 2406 , the solver has been optimized to further reduce memory required for thermal analysis. In comparison to the last 2306 release, memory use reduction is illustrated to be in the 18-20 % range. You can see this this illustrated for fine vs average approach for 3 types of board model in the figure below. Take advantage of the new features of Simcenter FLOEFD 2406 to optimize your simulation and electronic repair processes! Schedule a meeting with CAEXPERTS experts and discover how these innovations can improve your workflows, speed up training of large CAD assemblies, quickly and easily model thermal pathways, and more. Contact us and schedule right now! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

The recent CAEXPERTS  webinar highlighted how simulation using Simcenter STAR-CCM+  is transforming the design and operation of agitated tanks. The integrated approach to engineering digitalization was a key focus, highlighting how to predict and optimize the behavior of complex processes, reduce costs and increase operational efficiency. 1. Why is Agitated Tank Simulation Necessary Today? With the growing demand for efficiency and innovation, agitated tank simulation is becoming a tool for industrial process design. Simcenter STAR-CCM+  allows you to explore multiple design variants and operating conditions, reducing the need for expensive experimental testing and increasing visualization of phenomena that only complex sensors can measure. In this way, companies can improve mixing quality, reduce energy consumption and increase productivity, creating more sustainable and competitive solutions. 2. Complex Geometry Manipulation and Multiphysics Modeling Simcenter STAR-CCM+  stands out for its ability to manipulate complex geometries, enabling the creation, modification and repair of CAD models directly in the software. With a flexible and robust mesh, the tool accurately captures geometric features, ensuring detailed and realistic results. Multiphysics modeling allows the simulation of complex interactions between different phases, such as gas-liquid or solid-liquid, and the prediction of the conversion and yield of chemical reactions. 3. Design Exploration and Workflow Automation with Admixtus Workflow automation with the Admixtus tool accelerates the configuration and simulation of mixing tanks. This approach facilitates the configuration of geometries, generation of meshes and definition of the physics involved in an automated manner based on best practices. The tool also facilitates the post-processing of results, generating reports and graphs in an integrated and customizable manner, ideal for exploring different design scenarios and operational conditions. 4. What Can Be Calculated Using Simulation? Simcenter STAR-CCM+  allows you to calculate a wide range of critical parameters for the optimization of agitated tanks, such as pumping and circulation rate, mixing time, flow field, shear rate, impeller torque, energy consumption, among others. These simulations are capable of predicting the performance of complex systems and adjusting design variables to achieve the best results. 5. Case Studies and Practical Impact Several case studies show the practical application of simulation. One of the highlights was the exploration of impeller positioning and rotation to minimize mixing time and reduce energy consumption in mixing tanks, resulting in significant process savings. Another study focused on the optimization of impellers and baffles, showing improvements in energy efficiency and mixing quality. 6. Challenges and Solutions for Agitated Tanks Key challenges addressed include energy efficiency, bubble and particle size distribution, and prediction of mixing quality in multiphase systems. Simulation helps minimize these challenges by enabling adjustments that improve process efficiency, reduce energy consumption, and increase design flexibility. The tool also facilitates the evaluation of new raw materials and process intensification, contributing to sustainability and cost management. 7. Solutions for Non-Newtonian Fluids During the webinar, we also addressed the challenges of mixing non-Newtonian fluids, such as polyacrylamide. Simulation with STAR-CCM+  allows for careful adjustment of the agitation speed and agitator design to avoid problems such as lump formation and inefficiency in the flocculation process. This type of analysis is essential to ensure the quality and homogeneity of the mixture, even under complex conditions. 8. Multiphase Models and Their Applications Simcenter STAR-CCM+  offers a comprehensive set of multiphase models, such as Discrete Element Method (DEM) and Volume of Fluid (VOF), which are used to capture the complexity of phase interactions. The Eulerian Multiphase (EMP) model is particularly useful for simulating the mixing of miscible fluids and predicting phenomena such as coalescence and break-up, essential for processes such as fermentation and polymerization. The ability to capture these complex effects is critical for simulating industrial processes involving multiple phases, such as gas-liquid or solid-liquid systems. 9. Heat and Mass Transfer, and Chemical Reactions The ability to simulate heat and mass transfer between different phases is essential for predicting the efficiency of chemical reactions in stirred tanks. STAR-CCM+  allows you to analyze everything from the dissolution of substances to heat transfer in complex systems, such as those involving heating or cooling coils. With dedicated models, it is possible to simulate reactions both within a phase and at the interface between phases. 10. Intelligent Design Optimization and Exploration The tool also stands out for its intelligent design exploration, combining multiple optimization strategies to find the best design configurations in fewer iterations. This includes performing Design of Experiments (DoE) and optimizing multiple objectives, such as minimizing mixing time and power requirements while maximizing yield and productivity. 11. Economic Impact and Return on Investment Finally, the economic impact of simulation is discussed, highlighting how reducing the number of experimental tests and optimizing the design can lead to significant savings. Simulation allows for accurate prediction of tank performance, reducing yield losses and scale -up  costs , as well as accelerating the development time of new products with greater reliability and much lower investments. 12. The Future of Simulation and the Redefining of Engineering The use of advanced tools such as STAR-CCM+  is redefining the way engineering is conducted. Digitizing processes allows for digital exploration and physical confirmation, minimizing the time and costs associated with physical testing. Using simulation, companies of all sizes can explore new designs and improve products more quickly and efficiently, while remaining competitive in an increasingly demanding market. The CAEXPERTS  webinar showed that agitated tank simulation with Simcenter STAR-CCM+  goes beyond simple analysis; it is an essential tool for innovation, efficiency, and competitiveness in today’s market. By adopting integrated digital simulation, companies can explore new design possibilities, reduce costs, and increase productivity in a sustainable way. Want to know how this technology can transform your processes? Schedule a meeting with us and find out how we can help your company optimize operations, reduce costs and increase competitiveness. WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

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

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

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

New updates for NX in 2024. Tune in to this year’s annual What’s New in NX  premiere to learn about all the latest and greatest features and enhancements added through the continuous release cycle of NX  software. Featuring cloud SaaS products like NX X and Zel X, generative and AI-enabled design tools, and the future of immersive design. NX X - Cloud-based product engineering em Access NX ’s industry-leading product engineering resources  in the cloud with NX X. During the premiere, we’ll first show you how you can leverage the cloud for your CAD workflows. Centralized cloud license management reduces IT complexity to give you extra flexibility. You can install NX X on your desktop or even stream the software in your browser via AWS cloud services. Fully integrated and secure data management lets you share and collaborate with colleagues and partners directly within the NX X interface. 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Also noteworthy is the Managed Environment for Electronics Design, which brings together a set of Siemens Xcelerator products, such as NX , Teamcenter and Xpedition. Visualize mechatronic animations in high fidelity Mechatronics Concept Designer is an NX  add-on module that allows you to model, simulate, and validate machine designs with multi-body physics and automation-related behavior.   Now you can see accurate simulations of your designs in motion with NX Immersive Explorer, based on data from Mechatronics Concept Designer. AI-enabled design Improve and accelerate your processes by leveraging AI-enabled design tools in NX . A range of AI-powered design and simulation capabilities are introduced, such as Topology Optimization, Performance Predictor, and Gyroid modeling. Tools like these bring together AI capabilities like Command Prediction and Selection Prediction that can significantly increase efficiency in your routine workflows. Generative and AI-enabled design Tools like Topology Optimization, Lattice Designer, and Implicit Modeling don’t just automate workflows. They can optimize your designs and create complex geometry beyond the capabilities of traditional CAD methods. Watch the video below to see these add-on modules in action as part of a generative design workflow, or read on for more details. Topology optimization You can now watch the optimization process generate geometry live in the Graphics Window. This is a very intuitive way to assess progress and choose a suitable point to complete the optimization. Simply select View in the Solution Progress Monitor. Topology Optimization can now also automatically create blends between building bodies and the design space based on the specified voxel size. Choose the Auto-transition Blend option in the Building Body dialog box. You can also optimize for a wider range of manufacturing techniques. The Multi-Axis Tooling option in the Shape Constraint dialog now supports 5-axis milling. Implicit Modeling Two new commands have been added to Implicit Modeling in this new version: Set Resolution  – Use this to set the voxel size for any selected implicit bodies. Unit Cell  – Creates a triply periodic body with one body, for use by the Body Lattice command outside the Implicit Modeling task environment. The Unite command also has some new functionality. You can now choose between three types of blending between the target and tool bodies – Continuous, Circular, or Angular. Lattice Designer When creating Voronoi networks, you can now specify which pore size distribution to use. Uniform, variable and Gaussian pore distributions are available. The Filter Lattice command now offers even more flexibility. You can filter lattices by thickness or aspect ratio. There’s also the all-new Connect Dangling Rods command. It automatically connects the open ends of hanging rods to help prevent issues during additive manufacturing. You can choose a maximum rod length, a maximum number of rods per connection, and use a closest or random distribution. COMING SOON: NX Immersive Designer Get ready to experience 3D CAD in the industrial metaverse. Design while you’re in the room with your model. Take innovation to new levels. Learn more about the upcoming NX Immersive Designer software and the Sony XR head-mounted display, tailored for NX  controllers. Watch the full video of what's new in NX in 2024 Don’t miss out on the opportunity to drive innovation and efficiency in your organization! Schedule a meeting with CAEXPERTS  and discover how the new NX  updates in 2024, including NX X cloud products, can transform your design and engineering processes. 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