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In the modern power generation industry, where competitiveness demands operational excellence, the efficiency of industrial boilers stands out as a crucial pillar for success. Designed to meet performance and sustainability demands, these systems prioritize parameters that aim to: maximize the useful life of boilers and piping, optimize thermal efficiency, reduce emissions and ensure energy generation at maximum levels. In this scenario, furnaces emerge as a reference technology. Their differential lies in the arrangement of independent flames, which ensure stable combustion even under minimum operating loads - a critical advantage in scenarios of variable demand. In addition, their versatile architecture is not restricted to the traditional boiler format, allowing adaptations to different projects without compromising efficiency. Another highlight is the ability to maintain minimum deviations in the temperature of fuel gases along the horizontal flow, ensuring homogeneous thermal distribution and reducing the risk of premature degradation of components. Challenges and Solutions In the search for efficient combustion and reactive flow systems, the technical challenges transcend specific applications, focusing on four essential pillars: reducing pollutant emissions, maximizing energy efficiency, controlling costs (both design and maintenance) and adopting technological innovations, such as alternative fuels, hybrid systems and advanced materials. The strategic solution to these challenges lies in the creation of digital twins – virtual models that simulate real processes with high precision. In practice, companies use these simulations to optimize flame dynamics, heat transfer, thermal wear resistance and emission control (NOx, soot, CO). Furthermore, in complex reactive systems, predictive analyses allow for the prediction of critical parameters such as yield, chemical conversion, product selectivity and undesirable operating conditions, such as hotspots or stagnation zones. CFD simulation with STAR-CCM+ stands out as a strategic tool in this context, enabling the modeling of complex phenomena. Design variants can be quickly explored from the initial phases. This includes everything from adjustments to flame geometry and thermal distribution to the integration of new operating strategies. Simulation The simulation study, using STAR-CCM+ , in question focuses on the detailed analysis of the furnace of an industrial boiler , with the aim of investigating the combustion characteristics of natural gas and its impact on the efficiency and operational stability of the equipment. The simulation was developed based on a non-premixed combustion model , incorporating the detailed chemical kinetics of natural gas through a kinetic mechanism with more than 400 reactions. The computational mesh was strategically refined in critical regions, such as the flame zone and gas outlets, while the boundary conditions replicated real operational scenarios, including proportional fuel and oxidizer input. Figure 1: Geometry and computational mesh The results in Figure 2 show the temperature distribution in the furnace, where the regions close to the central line of the gas flow reach temperatures above 2000 K, indicating intense combustion activity. However, a low temperature zone is observed near the burner nozzles, caused by the high concentration of air in these areas, which dilutes the fuel mixture and reduces local thermal efficiency. Measuring this inefficiency is essential to verify whether the cost of preheating is justifiable or not, for example. Figure 2: Temperature profile From the image of the temperature field above, it is possible to understand the thermal distribution of the boiler and determine whether the areas of interest are at the appropriate temperature and receiving sufficient heat. Figure 3 shows the concentration profiles of CO₂, CO and OH along the height of the boiler. The CO₂ concentration increases progressively as the gases rise, reflecting the complete oxidation of the fuel in contact with the available oxygen. CO, on the other hand, shows peaks close to the burners, resulting from the centralized injection of methane (CH₄) combined with insufficient air in the primary zone. As the gases rise, CO is converted to CO₂ due to the greater availability of oxygen. The OH profile, in turn, confirms the stability of the flame, with high concentrations in regions of intense combustion, signaling combustion efficiency. Figure 3: Concentration profiles: (a) CO₂; (b) C; (c) OH In Figure 4, the velocity field and streamlines reveal how the geometry of the furnace and burners influence the flow of gases. Recirculation zones below the burners stand out, which retain unburned particles and improve combustion efficiency, and "dead" spots (regions of low turbulence), where there is a risk of accumulation of unreacted material. These patterns highlight the need for adjustments in the geometry to optimize the flow and avoid energy losses. Figure 4: Velocity profile Technical implications The results highlight opportunities to improve furnace design: Adjust the air-fuel ratio in the nozzles to reduce low-temperature zones. Redesign the geometry to minimize dead spots and ensure homogeneous flow. Improve the fuel-air mixture to control CO emissions as well as NOx and SOx. Evaluate fuel substitution (heavy oils, for example, for natural gas). Conclusion The optimization of industrial boilers and furnaces through computer simulations, such as those performed with STAR-CCM+ , has proven to be a way to overcome the challenges of the modern energy industry. The use of simulations shows that digital technology improves performance, reduces emissions and increases the useful life of equipment. Investing in these solutions is a sure path to greater sustainability and productivity. References   GANDHI, Mikilkumar B.; VUTHALURU, Rupa; VUTHALURU, Hari; FRENCH, David; SHAH, Kalpit. CFD based prediction of erosion rate in large scale wall-fired boiler . Applied Thermal Engineering, v. 42, p. 90–100, 2012. ISSN 1359-4311. Available at: https://doi.org/10.1016/j.applthermaleng.2012.03.015 . Accessed on: May 20, 2025. WANG, Haopeng; JIN, Haoze; YANG, Zhi; DENG, Shanshan; WU, Xuehong; AN, Jingxue; SHENG, Ranran; TI, Shuguang. CFD modeling of flow, combustion and NOx emission in a wall-fired boiler at different low-load operating conditions . Applied Thermal Engineering, v. 236, Part D, 2024, 121824. ISSN 1359-4311. Available at: https://doi.org/10.1016/j.applthermaleng.2023.121824 . Accessed on: May 20, 2025. Schedule a meeting with CAEXPERTS and discover how computer simulation can transform your industrial processes, optimizing efficiency, reducing emissions and extending the life of your equipment. With STAR-CCM+ , you can implement customized solutions for real challenges. Talk to our experts and take your operation to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Not only for Gear Design Optimization specialists For the literal among you, gear grinding is the process of abrading a gear surface to give it its final shape and polish. However, it is also a common expansion to express annoyance at something, i.e., Do you know what grinds gears? X, Y, and Z but not necessarily in that order. Extremely repetitive processes that are easy to make mistakes during, grind our gears. This is especially true when we know a computer could do it in a flash if only I had the code to do it. As a very infrequent coder there is always the urge to code something, but cleaning up buggy code will likely take longer to fix than it would have taken to do the original task. So, it is only worthwhile if the code is likely to get used somewhat regularly. Consider macro and micro geometries Therefore, we sympathize with gear specialists who may face both the literal and metaphorical incarnation of this expression. When developing a transmission, you may need to optimize it for NVH, durability, efficiency, or a combination. To do this, you need to consider both the macro and micro geometry of the gear pairs. In a case with just two gear pairs, there will be approximately 40 design variables, and several objective functions for the microgeometry, plus several more objectives with multiple constraints for the macrogeometry. Code that works As described, setting up a test that looked at two gear sets in mesh could take an experienced engineer weeks. However, you could reduce this to days if you were fortunate enough to be in a company that already has and does a good job of maintaining a script that evaluates gear key performance indicators. Much time is spent by gear specialists on repetitive workflows or maintaining code at much expense to their companies. This is why Simcenter 3D Motion has developed the Simcenter 3D Motion Gear Design Optimization Software, released as part of the Simcenter Mechanical Solution 2412 release. Simcenter 3D Motion Gear Design Optimization This new tool removes the need for coding and maintaining internal scripts. Simcenter 3D Motion Gear Design Optimization enables you to fine-tune the micro geometry of gear faces. Furthermore, it works with Simcenter 3D Motion Transmission builder and HEEDS in an intuitive workflow to optimize your macro and micro gear geometries to ensure a fully optimized design. Do not pass Meshing, Do not collect a designer, Go straight to gear design optimization. Those of you who have had the pleasure of rapidly developing gear systems using Simcenter 3D Motion Transmission builder will know that you can use its wizard to select your required gear parameters and generate a model without having to produce any CAD yourself. You will also see your model generated in Simcenter 3D , where you were able to conduct simulations and test your designs. You may even have included HEEDS in your workflow to optimize your design. With the New Simcenter 3D Motion Gear Design Optimization tool, you follow the same steps to produce your initial macro and micro geometry as Simcenter 3D Motion Transmission builder. But now, with the addition of the new tool, you can also optimize the design using HEEDS and evaluate non-motion Key performance indicators such as efficiency and durability. The new workflow Start in Simcenter 3D Motion Transmission Builder. As you did previously, you start by defining your baseline layout using the Simcenter Motion Transmission builder wizard to parameterize the shaft, gears, and bearings from the selection menus. Once you have your gears defined, you can define/ choose your meshing pairs. Flanks can be either predefined by common standards or loaded from a CSV file to provide your own initial design. Note that if you are planning to complete an optimization using HEEDS , this initial input is only used for the baseline; HEEDS will alter it. To control the level of detail at the contact location, you define the number of gear slices. Where, the number of slices can be set to create imaginary gear layers in a sandwich-like fashion. Once your initial parameters are set, you can generate a model for the entire gear set with just one button. You can watch the model materialize as you watch in Simcenter 3D . The level of fidelity for Gear contact and bearing analysis can also be defined in the transmission builder tool and exported directly into your Simcenter 3D model by simply clicking ‘export all elements.’ Drivers and load cases can now be added, which is done in the Simcenter 3D Motion application. While the user does this, the total time to complete this task is measured in seconds. Now is a good time to create a solution and run the solver to check your setup. Once complete, you should review the outputs and results. Finding any errors in your setup at this stage will prevent you from wasting a lot of time later. Setup Simcenter 3D Motion Gear Design Optimizationn You can load the Simcenter 3D Motion Transmission builder and Simcenter 3D Motion files in the new wizard menu screen. Suppose you wish to optimize the durability of your gear set. In that case, you start by choosing the macro geometry features you want to include and setting your durability test by defining the speed, torque, and required life. Further parameters for this test are also needed, such as the material that can be selected from a menu and the microgeometry convention you wish to follow. Finally, you choose your desired post-processing, select the bearings you want, and set the optimization parameters. Start the design of experiments (DOE) study for your gear design optimization While you are still in the Simcenter 3D Motion Gear Design Optimization tool, initialize the calculation modules to compute the baseline model. This confirms that the model will run in HEEDS and provides HEEDS with the baseline result from which it needs to start. Now, when you open your project in HEEDS , you will find that over 70 design variables for the macro and microgeometry have already been created for cases with just two gear pairs. You will also find that all your output responses have been made, saving you considerable time. All that is left is to set the output responses you want to consider for the objective function and the constraints. You can also set the range to the input parameters if you want to. Getting a gear design optimization result Now, you can run the full DOE in HEEDS and, finally, post-process the results in HEEDS . Conclusion The New Simcenter 3D Motion Gear Design Optimization tool builds on the existing capabilities of the Simcenter Motion 3D Transmission builder with new capabilities that allow you to optimize your design using the power of HEEDS and evaluate non-multibody key performance indicators such as efficiency and durability. You can now develop designs you may never have conceived in a time frame you would have never thought possible in a general-purpose simulation tool. Schedule a meeting with CAEXPERTS and learn how to optimize your gear design with Simcenter 3D Motion Gear Design Optimization ! Reduce time, eliminate rework, and take your designs to a new level of efficiency. Talk to our experts today! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

After successfully using Simcenter 3D Smart Virtual Sensing to predict loads for strength and fatigue analysis by combining testing and simulation, the well-integrated workflow optimized the efficiency of the entire team. This approach also included the possibility of “measuring the unmeasurable” on a larger scale. Challenges in durability analysis When analyzing potential damage under all possible load scenarios to validate a design, it is essential to obtain accurate data on operational loads. For those who are not experts in durability analysis, designing and validating mechanical structures under static and dynamic loads can be challenging, especially when trying to include operational loads in the study. With smart virtual sensing, operational loads can be obtained for durability analysis. The resulting information on remaining service life is extremely valuable. Despite the specialized tools available for detailed analysis, there is also a need for simpler and more integrated methods for everyday use. The old problem Traditionally, strain gauges are installed on prototypes to perform durability and validation testing. The big challenge is not that critical damage often occurs in areas where it is difficult or impossible to obtain direct formation effects due to geometry limitations. A new approach For load prediction, Simcenter 3D Smart Virtual Sensing can use just a few strain gauges in easily accessible locations to predict operational loads. The question was: could this approach be used continuously to measure unmeasurable loads and predict structural failures? Simcenter 3D Smart Virtual Sensing 2412 is dynamic and has a new feature – the Virtual Damage Sensor . This feature improves structural failure detection, providing a practical solution to a recurring problem. Solution Implementation With the Smart Virtual Damage Sensor solution, strain gauges can be placed in accessible locations to provide the smart virtual sensing tool with the necessary data. This enables data fusion between the physical measurement and the FE model, providing measurements in locations that are difficult to physically measure. Virtual sensors provide detailed results, including strain, stress, velocity, and displacement. In addition, the newly added virtual sensors integrate virtual sensing with durability analysis to provide important damage information, such as remaining service life, accumulated damage history, and damage increments. When combined, these results provide insights into the durability of the structure, indicating when damage is likely to occur and how much useful life is left. The workflow The process for implementing this solution follows these steps: Step 1: Use Simcenter 3D Smart Virtual Sensing Optimal Sensor Placement to determine where to place the physical strain gauges Parallel instrumentation with Simcenter SCADAS RS Step 2: Perform physical testing using Simcenter SCADAS and obtain strain measurements with operational loads. Step 3: Predict operational loads by feeding the measurements and the reduced-order finite element model into Simcenter 3D Smart Virtual Sensing . Step 4: Complete the durability study in Specialist Durability by connecting it to the Smart Virtual Sensing events. Step 5: Perform a durability study and figure out where the potential damage area is. (Step 4 and 5 are a recommended but optional way to find the critical areas). Step 6: Put virtual damage sensors on the potential damage area and run the smart virtual sensing solution to get damage information in the selected locations   The results With this workflow, data on remaining useful life, accumulated damage history, and damage increments can be obtained, allowing for more accurate analysis of the root cause of damage. By following this process, structural damage analysis becomes more efficient and reliable. The Smart Virtual Damage Sensor is a valuable tool for design and validation, offering a practical way to measure damage and assess remaining service life at critical locations. In addition, by making the Virtual Damage Sensor a real-time solution, it is also possible to implement predictive maintenance. This allows potential problems to be identified before they occur, avoiding costly downtime. Optimizing maintenance task scheduling can reduce large safety factors typically included in worst-case scenarios, making structural engineering more accurate and cost-effective. Schedule a meeting with CAEXPERTS and find out how the Simcenter 3D Virtual Damage Sensor can transform your durability analysis, making it more accurate and efficient. Talk to our experts and take your structural engineering to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Don’t try this at home – unless you’re simulating it! Thermal Runaway: a recap Thermal runaway of batteries is a dangerous phenomenon where the battery cell overheats uncontrollably. A self-sustaining feedback loop occurs where the battery receives a certain amount of heating, and if left unaddressed, this in turn triggers a series of chemical reactions inside the battery cell. These reactions release even more heat and gases, which can result in huge pressure build up inside the battery. Extremely hot (over 1000oC!) and combustible venting gases are then emitted from the battery cell. When these gases ignite, fire spreads to other battery cells and catastrophic damage can occur to an electric vehicle, property and of course presents a high risk to life. Look at this short video which shows the violent nature of a thermal runaway event: It should be clear that these scenarios should be avoided, and battery systems designed to ensure safe operation if a thermal runaway event occurs. Indeed there are now various national and international regulations that cell makers and electric vehicle manufacturers must adhere to. Time to test? – Accelerated rate calorimetry (ARC) The most intuitive way to understand the behavior of batteries as they undergo a thermal runaway event is to replicate the causes of thermal runaway and see how the battery behaves. In the case of overheating, a common methodology to understand the heat release of the battery is an accelerated rate calorimetry (“ARC”) test. These tests provide clear insights but come with some issues: primarily that the tests themselves are expensive, require access to test facilities and require a physical prototype of the battery cell! Of course, this is where simulation can fit in and allow for rapid design iterations early in the design cycle, with safety in mind. Simcenter STAR-CCM+ already has a wide range of models and functionalities for battery modelling and battery safety, from the 3D cell design right through to the full battery pack and thermal propagation during a runaway event: In version 2502, another layer of functionality will be added – most notably the ability to model detailed thermal runaway at the cellular level with the Homogeneous Multiphase Complex Chemistry (HMMC) model. Thermal runaway chemistry When we consider the inside of a battery cell then there are multiple solids (anode, cathode and separators), as well as liquid electrolyte. When thermal runaway reactions occur, these components can react and decompose to release flammable gases such as hydrogen and methane. This results in multiple phases all reacting with each other e.g multiphase. To capture this complexity, a framework has been developed in which the early stages of a thermal runaway event can be modeled with detailed chemical modeling. This allows designers to explicitly model the fundamental reactions that are triggered during a thermal runaway event. The reactions are considered as a homogeneous mixture, allowing for simple and easy setup in conjunction with our multiphase mixing model. Furthermore, this is all built on top of a proven complex chemistry solver, allowing reactions to be easily defined using standard chemkin file import and accounting for intra- and interphase reactions. This provides unique modeling functionality to deeply understand battery behavior. For example, one can directly configure reactions for SEI decomposition/production, conductive salt decomposition, hydrogen fluoride production, and cathode decomposition; see the example of imported reactions for thermal runaway testing above. Let’s get cookin’ – Heat-Wait-Seek Armed with a new modelling tool, let’s apply it to the simulation of an ARC test and see how it performs. The ARC test involves an initial period of heating the battery called Heat-Wait-Seek (HWS) where the battery is gradually heated before waiting to see if exothermic reactions begin. When those reactions begin, no more heat is applied to the battery, and it is left to continue reacting / self-heating until the onset of thermal runaway. Unveiling the interaction of reactions and phase transition during thermal abuse of Li-ion batteries https://doi.org/10.1016/j.jpowsour.2021.230881 One great thing about having this physics functionality inside Simcenter STAR-CCM+ is that it can leverage the automation capabilities. The ARC testing process can be easily implemented using Simulation Operations and Stages without any scripting. And the results are excellent: As can be seen, the new HMMC model can accurately capture the onset of both the exothermic reaction and thermal runaway. Being able to accurately capture this behavior allows for effective countermeasures or mitigation strategies such as heat shields to be implemented by engineers much earlier in the design process in a safe and cost-effective manner. For example, the predicted heat generation from this simulation can be used as an accurately calculated heat source directly in a larger pack level model, or the predicted vent gas compositions used for a further combustion analysis. An excellent example of how simulation is vital in the battery design cycle. Schedule a meeting with CAEXPERTS and find out how we can help your team design safer, more efficient battery systems using advanced simulation to predict and mitigate thermal runaway events. Contact us now and take your innovation to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The latest update to Simcenter STAR-CCM+ 2502 brings pivotal enhancements across various domains, focusing on boosting simulation speed, enhancing model accuracy, and improving integration across different engineering disciplines. Key advancements include an efficient mesh motion technology for moving objects, faster vehicle thermal management and aerodynamics simulations, streamlined data exchange for E-Machines, and sophisticated methods for accurately modeling battery thermal runaway onset, corrosion and complex non-Newtonian fluid behavior. These developments are designed to help you accelerate development cycles, optimize product performance, and facilitate cross-team collaboration, ultimately driving innovation and efficiency in your projects. Improved battery safety simulations Battery manufacturers face the critical challenge of ensuring safety, particularly the risk of thermal runaway during short circuits or other failures. Traditional modeling tools have struggled to accurately predict these complex chemical and electrochemical reactions. To address this, the latest release of Simcenter STAR-CCM+ 2502 features the “Homogeneous Multiphase Complex Chemistry Model,” designed to provide a detailed simulation of battery cell behaviors under adverse conditions. Although a general model, the homogenous multiphase complex chemistry model can allow designers to explicitly model the fundamental reactions that are triggered during a thermal runaway event, providing detailed insight into the electrochemical and thermal response to a short circuit or nail penetration incident in a battery. This allows engineers to gain deeper insight and design batteries with high degrees of safety without the cost of physical testing. This improved capability ultimately helps in preventing accidents, enhancing consumer trust, and complying with stringent safety regulations. Advanced corrosion analysis Corrosion is a pervasive issue across many industries, leading to significant maintenance costs and equipment downtime. Traditional analysis tools often fail to predict the onset and progression of corrosion effectively, leading to unexpected failures. Simcenter STAR-CCM+ 2502 integrates Corrdesa’s Corrosion Djinn Database for advanced corrosion analysis. High-quality polarization data describes the relationship between the potential drop at the material interface and the specific electric current and are important inputs to the Electrodynamic Potential solver of Simcenter STAR-CCM+ . The Corrdesa Corrosion Djinn material database hosts collection of data, including surface polarization, derived through rigorous experimental quantification. This feature offers engineers robust tools to simulate and predict corrosion under various environmental conditions, using high-fidelity data. As a result, industries can proactively design out potential corrosion issues, extend equipment lifespan, and significantly reduce maintenance costs. Accurate modeling of complex fluids In the food processing industry, accurately predicting the behavior of complex fluids, such as mayonnaise, which exhibit non-Newtonian characteristics, presents significant challenges. These fluids can behave like a solid at lower shear stress levels before moving like a fluid at elevated stress levels. This behavior is complicating production processes and quality control. In response, Simcenter STAR-CCM+ 2502 introduces Generalized Non-Newtonian Fluid Models, namely the Yield Stress Threshold and Yielding Viscosity for Non-Newtonian Cross and Carreau-Yasuda laws. These advanced viscosity models capture the intricate behaviors of such complex Bingham fluids under stress more accurately than ever before. By simulating these fluids’ behaviors, engineers can precisely predict how they will act in the real world, thereby optimizing manufacturing and filling processes. Uniform spray coverage for various applications Achieving uniform spray coverage is imperative in industries ranging from agriculture to automotive manufacturing, where it impacts everything from crop yields to paint finishes. Variability in this process can lead to inefficiency and wastage, posing a substantial logistical challenge. In many such cases the flat fan nozzle Injector provides a uniform, flat spray pattern of a thin, fan-shaped sheet of liquid. In agriculture, flat-fan nozzles are e.g. essential for providing uniform spray coverage in aerial pesticide applications. Similarly, these types of injectors are used for cleaning and degreasing, coating and painting, cooling and humidifying, lubrication, surface Treatment and dust control. The Flat Fan Nozzle Injector model in Simcenter STAR-CCM+ 2502 makes it easy to set-up such injectors quickly and easily. Accurate results are achieved by the Linear Instability Sheet Atomization (LISA) method. This technology ensures uniform application across varied operations, improving resource utilization and process efficiency. Ultimately, this leads to reductions in waste and costs, contributing to more sustainable and eco-friendly production practices. Faster adjoint-based optimization Adjoint optimization methods rely on a series of multiple simulations, in which the adjoint solver is executed at each step. Considering that the adjoint solver is very expensive in terms of computational resources, these optimization studies easily reach the limit of feasibility. The algorithmic improvements made in Simcenter STAR-CCM+ 2502 to the adjoint solver with second order discretization drastically improve the convergence rate, thus lowering significantly the total turnaround time. Furthermore, this improvement reduces the need for falling back to first order adjoint discretization for a robust convergence, hence improving the accuracy of the computed adjoint sensitivities. The reduced simulation time and higher-quality results, enables engineers to explore and realize optimal designs much more efficiently and effectively. Fast and scalable simulations of moving objects Many applications, such as paint dipping and bottle filling, involve the movement of a solid body that affects the motion of a fluid. To capture the motion the overset meshing approach offers both flexibility and accuracy by utilizing a background mesh combined with a body-fitted moving mesh. This configuration ensures high mesh quality near the boundaries of moving objects, leading to precise results. However, this accuracy comes at the cost of increased complexity, which results in higher computational expense and suboptimal scaling. The new Virtual Body approach that is available in the new version of Simcenter STAR-CCM+ 2502 eliminates the need for two separate meshes and provides a more cost-effective, scalable, and easier-to-setup alternative to overset for various validated application. Furthermore, it also offers a more stable solution in scenarios involving tight gaps. Faster sliding mesh simulations on GPUs and CPUs Many applications like external vehicle aerodynamics with rotating wheels, require rigid body motion (RBM) to capture transient, unsteady flow phenomena. This employs the sliding mesh interfaces, also known as non-conformal interfaces. Traditionally, in such scenarios the interface intersection is performed at every time step. Because of the complexity of the intersector algorithm and its high interface data requirement, the performance of the sliding meshes when deployed on large core counts or used with GPUs, has been restricted. Case (cell count) Total sim time (min) No Caching Total sim time (min) Caching Number of CPU/GPUs Speed up Case 1 (120 M) 165.6 149.8 8 x A100 GPUs 10% Case 2 (150 M) 223 187.4 8 x A100 GPUs 16% Case 3 (38 M) 862 470 8 x A100 GPUs 45% Case 4 (140 M) 13 hrs 10.5 hrs 16 x V100 GPUs 19% Case 5 (136 M) 50.7 hrs 44.9 hrs 1600 Cores 12% The new Boundary Interface Caching strategy that is available in the new version of Simcenter STAR-CCM+ 2502 allows for the interface data to be calculated only once and reused for the subsequent time steps, thereby significantly reducing sliding mesh simulation time on both CPUs and GPUs. Faster Vehicle Thermal Management simulations on GPUs The automotive industry is constantly pushed to enhance energy efficiency while managing the heat generated during operation, a demanding aspect of vehicle design. At the same time the benefits of GPUs to solve CFD simulations faster and in a more energy efficient way are without a doubt. With Simcenter STAR-CCM+ 2502 we are further expanding the range of thermal management applications you can tackle on CPUs and GPUs through the porting of more GPU-native solvers: The new GPU-native Actual Flow Dual Stream Heat Exchanger method leverages GPU acceleration to perform complex VTM simulations. Further applications covered include faster CHT simulations of headlamps with the GPU-native segregated and coupled energy solvers for solid shell regions as well as faster Batteries CHT and Electronics cooling analyses thanks to GPU-native Orthotropic, Anisotropic and Transverse Isotropic material property methods. This will increase your throughput and hardware options while a unified solver architecture for CPU and GPU ensures consistent results. As a result, you can perform more thermal management simulations in less time, enhancing productivity and accelerating the development cycles. Native automation of advanced aerodynamics and turbomachinery workflows Complex simulations involving multiple physical phenomena or varied operational stages can be cumbersome to set up, often requiring intricate scripting and setup. The Stages feature within Simcenter STAR-CCM+ streamlines this process. It provides a user-friendly interface for defining and managing simulation stages, cutting down setup time and allowing engineers to focus more on analysis and less on setup. With Simcenter STAR-CCM+ 2502 stages become available for a further extended range of applications: With the support of harmonic balance and harmonic balance turbulence models you can tackle turbomachinery simulation workflows with ease. Stages support for Moving Reference Frame and Rigid Body Motion simplifies workflows with a change from steady state to transient, such as external aerodynamics with rotating parts. The Stages method not only accelerates the simulation workflow but also significantly boosts productivity and mitigates potential setup errors. Streamlined data exchange for E-Machine design Collaboration between electric machine designers and computational fluid dynamics engineers is often hindered by incompatible data formats and systems. The “Simcenter Data Exchange (SCDX)” format, newly implemented in Simcenter STAR-CCM+ 2502 , resolves these issues by ensuring smooth and efficient data transfer across different software tools and teams. This integration capability facilitates a more cohesive workflow, reducing errors, and enabling faster project completion through improved collaboration. These are just a few highlights in Simcenter STAR-CCM+ 2502 . These features will enable you to design better products faster than ever, turning today’s engineering complexity into a competitive advantage. Multiversion support for Simcenter X HPC Facing an urgent CFD project that requires immediate HPC capacity? Questioning massive CAPEX investments for on-premise HPC clusters? Tired the complex IT setup associated with running CFD software on 3rd party cloud-providers? Just not interested in waiting in the queue? Simcenter X HPC enables you to unlock productivity gains with the power of turn-key cloud simulation. Run your Simcenter STAR-CCM+ simulations anytime in the cloud, straight out of Simcenter STAR-CCM+ in 3 clicks. No queuing involved, no IT overhead, no on-premise HPC hardware investments. To make the most out of Simcenter X HPC, with the release of Simcenter STAR-CCM+ 2502 you will have immediate access to the most recent version. Alongside 2502, multiple versions of Simcenter STAR-CCM+ are now available on Simcenter X HPC, including previous versions 2410, 2406, 2306. Use clusters of sizes from 100s to 1000s of cores, instantaneously from a few clicks. Schedule a meeting with CAEXPERTS now and find out how Simcenter STAR-CCM+ can transform your simulation processes, reducing development time and increasing the accuracy of your projects. Talk to our experts and take your engineering to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Safety at work should be a priority in any operation, but in emergencies, quick and assertive decision-making can be the difference between life and death. To increase operator safety during activities carried out at height, it is essential to determine the maximum wind speed for safe operation. This is a classic example of an operation using rappelling. In these conditions, the operator faces high-speed winds, which can cause oscillations during work and can result in collisions with other equipment or objects. But how can you anticipate and plan responses to these events? This is where CAE simulation makes all the difference. CAE Simulation The acronym CAE (Computer-Aided Engineering) refers to Computer-Aided Engineering, a technology that uses software to simulate and analyze engineering projects. This tool allows you to predict the behavior of a product before its physical construction, assisting in the development and improvement of projects. CAE simulation is applied in several areas, including structural, fluid, thermal and electromagnetic analysis. The best-known and most widely used techniques are Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) . The benefits of CAE simulation for companies are: Greater efficiency, reliability and quality Cost reduction Reduced developer time in product development Elimination or reduction in the number of test prototypes to be built Increased competitiveness CAE simulation allows for the digital recreation of several scenarios, for example, by analyzing factors such as fall conditions, interaction with the environment, performance of safety equipment and emergency evacuation strategies. With this information, it is possible to improve equipment, enhance protocols and develop more effective training to increase the safety of workers and rescue teams. The Importance of Emergency Planning Emergencies such as falls, leaks or fires can occur even with rigorous preventive measures. The difference lies in how well the team and systems are prepared to deal with these situations. CAE simulation transforms the unpredictable into something controllable, allowing: Faster responses: Equipping teams with elaborate information and ready action plans. Damage reduction: Minimizing impacts on the worker and the operation. Tragedy prevention: Developing strategies to prevent similar situations from occurring again. Simulation The operator, weighing 90 kg, is suspended by a rope, simulating a rappel down a 50-meter tower. The analysis considers the acting forces, such as weight, wind force and turbulent interaction and the tension on the rope. The simulation allows for the inclusion of an air speed curve in relation to height, making the assessment of air flow more accurate, considering turbulence and interaction with the environment. Including the air speed curve in the simulation allows us to assess how wind currents influence the operator's oscillation during rappelling. The intensity and direction of the wind affect the operator's movement, impacting balance and stability. With this analysis, it is possible to predict these effects, helping to understand external forces and their impact on the safety and performance of the operation. In addition, the data obtained allows us to optimize the planning of activities at height and select the most appropriate equipment to reduce risks.   During the simulation, it was possible to determine the force applied to the rope, considering the environmental conditions and the characteristics of the rappelling operation. Factors such as wind speed and sudden movements directly influenced this force. In scenarios with strong winds, with a speed of 15 m/s, the force on the rope reached a peak of 3000 N, which can reach the breaking limit of the rope. This result highlights the importance of evaluating environmental and operational conditions to ensure safety, in addition to the need to carefully choose the materials and equipment used in risky activities. Commitment to life The application of numerical simulations goes beyond innovation; it is a demonstration of commitment to the safety and well-being of workers. In times of emergency, where decisions need to be made in seconds, having the confidence that every detail has been analyzed and planned can save lives. If you would like to explore how CAE simulation can transform workplace safety, optimizing prevention and response to emergencies, get in touch. Let's build safer environments together and protect what is most valuable: people. Schedule a meeting with CAEXPERTS and find out how CAE simulation can transform safety in your workplace! With advanced technology and detailed analysis, we help your company prevent risks, optimize processes and protect lives. Contact us now and take operational safety to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

Simcenter Amesim software, which allows engineers to virtually assess and optimize systems’ performance, can now be used to recreate the remarkable achievements of the Ingenuity Mars Helicopter, nicknamed “Ginny”, which completed its last flight on the Martian surface a year ago. This mission significantly advanced our knowledge of powered and controlled flight on another planet, serving as a source of inspiration for engineers and space enthusiasts. As NASA Administrator Bill Nelson stated, Ingenuity’s journey represents a groundbreaking milestone in human exploration. “Helicopter Above Perseverance on Mars” – Credit: NASA/JPL-Caltech Ingenuity’s Groundbreaking Flights on Mars When the Perseverance rover touched down on Mars in February 2021, it carried a small companion – the Ingenuity Mars Helicopter. This 4-pound (1.8 kg) robotic rotorcraft was the first powered, controlled aircraft to fly on another planet. Over the course of its mission, Ingenuity far exceeded its original design specifications. Instead of the planned five test flights, Ingenuity went on to complete 72 flights, traveling over 17 kilometers (11 miles) and reaching altitudes of up to 24 meters (79 feet). Ingenuity gained the ability to autonomously select landing sites in treacherous terrain. It also dealt with a malfunctioning sensor, cleaned itself after dust storms, operated from 48 different airfields, and performed three emergency landings. Remarkably, the helicopter even survived the harsh Martian winter. However, the extreme cold of winter posed a significant challenge. Designed to operate during the milder spring season, Ingenuity was unable to power its heaters throughout the frigid Martian nights. This resulted in the flight computer periodically freezing and resetting, a phenomenon known as “power brownouts.” To address this issue, the Ingenuity team had to redesign the helicopter’s winter operations to keep it flying. These flights provided invaluable data and demonstrated the viability of powered flight on Mars, paving the way for future aerial exploration. Ingenuity’s final flight on January 18th, 2024 marked the end of its groundbreaking mission, but the legacy of this little helicopter lives on. Its success has inspired the development of even more ambitious aerial platforms for future Mars exploration. Simulating Ingenuity’s Flights with Simcenter Amesim To celebrate the anniversary of Ingenuity’s last flight, Siemens has developed a system simulation demonstrator that replicates the key aspects of the helicopter’s flight using Simcenter Amesim . Ingenuity flight physics model in Simcenter Amesim Simcenter Amesim is a powerful multiphysics simulation platform that allows engineers to model and analyze complex systems, including flight dynamics, energy management, guidance, navigation, and control of aerial vehicles like Ingenuity. This demonstrator recreates the unique challenges of flying on Mars, including the planet’s thin atmosphere, low gravity, and extreme temperatures. By accurately modeling the physics of Ingenuity’s flight, the energy requirements of its systems, and the control algorithms that guided its movements, the demonstrator provides a realistic simulation of the helicopter’s remarkable achievements. Through this simulation, users can explore the design trade-offs and engineering decisions that went into Ingenuity’s development, gaining a deeper understanding of the technical challenges overcome by the NASA team. Additionally, the demonstrator serves as a valuable tool for testing and validating new aerial platform concepts for future Mars exploration. Experience the Thrill of Ingenuity’s Flights To commemorate the anniversary of Ingenuity’s last flight, we invite you to experience the thrill of this historic achievement through Simcenter Amesim-based system simulation demonstrator. Dive into the details of Ingenuity’s flight physics, energy management, guidance, navigation, and control, and see how this groundbreaking technology can be replicated and advanced using state-of-the-art simulation tools. The demonstrator can be accessed in the software by opening the built-in help feature. If you are not a Simcenter Amesim user, you can start your free trial now. Simcenter Amesim software is part of the Simcenter simulation and test solutions that you can leverage today to drive productivity, achieve better designs faster, and ensure successful space program outcomes. This is particularly crucial due to the increasing complexity of systems in every new generation of aircraft and spacecraft. As engineers strive to push boundaries and achieve innovation, they often encounter unexpected issues and face costly program delays. To address these challenges, a new approach is required, focusing on quicker and more cost-effective processes that not only uphold but also enhance performance and compliance. This entails driving digital transformation to gain a competitive advantage. Want to take your simulations to the next level? Schedule a meeting with CAEXPERTS and find out how Simcenter Amesim can optimize your projects, reducing costs and development time. Contact us now and take your solutions to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Simulating the performance of James Webb Space Telescope components Challenges Design a next-generation space telescope Coordinate systems supplied by multiple sources Operate at temperatures near absolute zero Results Standardizing on Femap shortens learning curve Visualization pinpoints potential flaws in components Finding and fixing potential problems long before the telescope is launched NASA Goddard Space Flight Center The NASA Goddard Space Flight Center is home to the United States’ largest organization of combined scientists, engineers and technologists that build spacecraft, instruments and new technology to study the Earth, the sun, our solar system and the universe. Named for American rocketry pioneer Dr. Robert H. Goddard, the center was established in 1959 as NASA’s first space flight complex. Goddard and its several facilities are critical in carrying out NASA’s missions of space exploration and scientific discovery. Building a time machine The use of Femap™ software from Siemens PLM Software is helping NASA develop a time machine. Scheduled for launch in 2018, the James Webb Space Telescope Observatory (JWST) will operate 1.5 million kilometers above the Earth. Its mission is ambitious: examining every phase of cosmic history “from the first luminous glows after the Big Bang to the formation of galaxies, stars and planets to the evolution of our own solar system,” according to the JWST website. The telescope will look back light-years into the past. Considered to be the next generation – not the replacement – of the Hubble Space Telescope, the JWST is an infrared telescope that enables the viewing of more distant, highly redshifted objects. The Hubble is used to study the universe in optical and ultraviolet wavelengths. The JWST will also be larger than Hubble, which is about the size of a large tractor-trailer truck. At 22 by 12 meters, the JWST will be almost as large as a Boeing 737. Fully deployed, the JWST will feature a reflecting mirror with seven times more collecting area than the Hubble. The telescope will be launched into space atop an Ariane 5 rocket from the European Space Agency’s (ESA) launch pad in French Guiana. The JWST will have a hot side and a cold side, with the hot side consisting of the observatory spacecraft, which manages pointing and communication, and a shield that blocks heat and radiation from the sun, Earth and moon. The cold side of the JWST, operating at temperatures near absolute zero, is where the science will happen. Four major instruments will be in operation, including the near-infrared camera or NIRCam, provided by the University of Arizona. Other major instruments include the near-infrared spectrograph (NIRSpec), provided by the ESA, with additional instrumentation provided by the NASA Goddard Space Flight Center (GSFC); the mid-infrared instrument or MIRI, provided jointly by the ESA and NASA’s Jet Propulsion Laboratory (JPL) and the fine guidance sensor/near infrared imager and slitless spectrograph, provided by the Canadian Space Agency. All in all, there are more than 1,000 people in 17 different countries working on JWST, including academic and industrial partners ATK, Ball Aerospace, ITT, Lockheed Martin, Northrop Grumman (the prime contractor) and the Space Telescope Science Institute. Multiple analysis applications feed into Femap Designing, testing, building and assembling JWST is a team effort, taking place on three continents. The instruments now under development are being tested using a variety of computer-aided engineering (CAE) solvers for modal, thermal, thermal distortion and structural analysis. Gluing all this analysis and simulation work together is Femap , the JWST team’s standard application for pre- and postprocessing. “We use Femap as the pre- and postprocessor,” says Emmanuel Cofie, who leads thermal distortion analysis on the ISIM (integrated structural instrument model). “The mechanical design team provides us with CAD files and we use Femap to generate meshes for our mathematical model and, after finite element analysis, to extract results and view the condition and state of the structure under the various load conditions. It is the primary tool we use for visualization of the structure in its operational/launch states before actual environmental testing.” Because there will be only one opportunity for the JWST to succeed, every part and assembly of every system needs to be thoroughly tested on Earth to ensure that all instruments will function flawlessly under expected conditions. Simulating the JWST’s performance on Earth is the only way to determine that the observatory will function once it is in place. It’s a one-of-a-kind, custom job. Using CAE solvers in conjunction with Femap , NASA engineers conduct simulations to ensure each part does not interfere with another and that parts and assemblies have sufficient strength and can withstand extreme heat or cold and vibrations experienced during launch and normal operating conditions. “ Femap is a very usable tool that is at once very affordable and also provides high value,” says Mark McGinnis, thermal distortion working group leader at Goddard. “It enables us to carry out our mission of analyzing the structural and thermal performance of parts and systems. Femap is easy to learn and use, and works well with any solver.” He estimates that the software is used frequently by at least 75 NASA engineers at Goddard. “For example, we will import a back plane sub-assembly model from a contractor and populate it with 18 mirrors to visualize how they come together,” says McGinnis, “We need to be sure the interface grids are coincident as they are supposed to be, and then use it to build the more than 8 million required grids, which makes a very large model from a computing standpoint. We assemble the model using Femap .” Most of the engineers working on the JWST have used Femap as far back as the mid-1990s. Cofie recalls using Femap during the development of Hubble. “We used it for a lot in those days and we continue to use it,” he says. “ Femap helps us understand loading conditions so we can take a structure, run the analysis and see what gets hot and what gets cold. It helps us visualize whether or not a model is feasible.” McGinnis agrees that the visibility Femap provides in postprocessing is a key advantage. “An engineer can easily understand the mathematical results of an analysis conducted with a solver,” he says. “But visualizing analysis results using Femap is an important benefit, showing you exactly what is going on.” Schedule a meeting with CAEXPERTS and find out how advanced simulation can transform your projects! Just as NASA used Femap to ensure the success of the James Webb Space Telescope, we can help your team identify potential failures, optimize performance, and shorten the learning curve. Talk to our experts and take your engineering to the next level! WhatsApp: +55 (48) 98814-4798 E-mail: contato@caexperts.com.br

The analysis of an axial-flux machine requires three dimensions because of its inherent three-dimensional magnetic flux paths. Thus, to accurately predict its performance, you must consider the complex interactions between the 3D magnetic components, especially for NVH and structural performance. Unfortunately, 2D analysis, with its faster solving speeds, neglects one of the components, whereas the 3D FEA is time-prohibitive in design exploration. Save weeks of work with an improved axial-flux EMAG workflow To reduce the cost of electric vehicles, manufacturers must lightweight their powertrains by optimizing their motor’s power density, a characteristic that axial-flux motors excel at. However, the requisite 3D design exploration will be slow and prohibitive. The solution is to explore early concepts with equivalent analytical models in Simcenter E-Machine Design . Once your initial design converges, it is automatically sent to the 3D environment of Simcenter 3D for a detailed multi-discipline analysis of the motor. Hence, integrating Simcenter 3D and Simcenter E-Machine Design , released in 2024, provides an enterprise solution that quickly searches for an optimum axial-flux design. The selected design is then automatically imported into Simcenter 3D , bringing the EMAG geometry and analysis into a multi-discipline validation analysis that includes NVH, Structural, and CFD-thermal. The import includes the automatic generation of the EMAG geometry and mesh. The air core axial-flux machine To further support axial-flux machines, the 2412 release of Simcenter E-Machine Design , now supports a new stator air core template for axial-machines in addition to those of the radial-flux topologies. These are important in ultra-precise position servo control and near-silent running applications. Air core stators eliminate the no-load cogging torque ripple. Accurate system-level e-motor thermal representation Accurate system-level thermal representation of the e-motor affects the sizing of the cooling circuit and battery capacity. Therefore oversimplifying the motor’s thermal model will result in costly powertrains. A solution has long been desired for this problem. That is an accurate, fast, and easier-to-extract thermal representation of the e-motor in the form of a lumped-parameter thermal network (LPTN). However, analysts must work for weeks to create an LPTN, which may not even adapt to changes in rotor topology. For example switching from an interior PMSM to a spoke-type, or PM assisted rotor topology. The new release of Simcenter E-Machine Design 2412 , and Simcenter AMESIM addresses this challenge. The Simcenter E-Machine Design radial-flux synchronous machine (BLDCs, PMSMs, etc) thermal models, are automatically converted into LPTN in Simcenter AMESIM . The created LPTN model is customizable at the AMESIM system level while supporting multiple operating points. Release 2412 Highlights This 2412 release of Simcenter E-Machine Design accelerates the development of axial-flux machines. It allows you to quickly search for a design, that is then automatically imported into Simcenter 3D . This fast tracks the consideration of EMAG in a multi-discipline NVH, Structural, and CFD-thermal validation analysis of axial-flux machine design parameters. In addition, for radial-flux synchronous machines (BLDCs, PMSMs, etc), together with Simcenter AMESIM , barriers to accurate and fast e-motor LPTN thermal models have been addressed. Schedule a meeting with CAEXPERTS and discover how to optimize your axial flux motor design with Simcenter E-Machine Design solutions! Reduce analysis time, improve thermal accuracy, and accelerate your innovation with our expert support. Contact us now and take your designs to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

CAD-embedded CFD simulation The new Simcenter FLOEFD 2412 software release is now available in all its CAD-embedded CFD versions, and Simcenter 3D embedded version. This release delivers electronics applications enhancements for PCB thermal analysis workflows. For example, in the area of processing imported EDA data and component placement, there are new ways to add libraries and quicker methods to swap thermal models of components within the EDA Bridge utility. Users are also able to more easily create of 2-resistor and LED component thermal models directly in the interface. Learn more about improved handling of missing material for thin gaps in complex CAD assemblies and read on below to discover many other topics such as examples for simulation automation, new plots and goals location functionality, and even batch simulation processing capabilities. For NX users – You can also learn about the newly released NX CFD Designer , a new accessible native NX simulation tool, powered by Simcenter FLOEFD technology, that supports designers with a subset of common fluid flow and thermal analysis capabilities. PCB thermal analysis enhancements EDA Bridge: Libraries and swapping PCB component thermal models Positioning and replacing component models with IC package thermal models of suitable fidelity as part of the process of EDA data import into the electronics cooling tool environment can be a manually time-consuming task. In response to a popular user request, in Simcenter FLOEFD 2412 , there are enhancements to the EDA Bridge utility so that that you can more easily replace components with thermal models contained within an existing library source. Libraries – adding sources Users can add libraries of component sources from either local files, mapped network locations or multiple locations. This way you can leverage your organizations component libraries as you expand them, or if you are importing component libraries from Simcenter Flotherm XT. You add sources from within the component library in EDA Bridge .  This operation does not modify the location files of the sources and you can remove library sources without impacting the files where they are located.  You can also refresh sources so they are up to date when in the Component Library window. Replacing components  in EDA Bridge – new methods You now have the following replacement options from within EDA Bridge : 1) Replace automatically (Replace Matching) Matched by Package Name, Part Number or Thermal Model ID Specify model level type to use 2) Replace manually (Replace Selected single or multiple components) There is also scripting based support for the above replacement operations so that you can leverage these new capabilities in your automated workflows. Watch the short illustrative video below. A workflow is shown starting from a detailed thermal model being added from Package Creator utility as a source into a library and then the steps within EDA Bridge are shown for replacing a selected simple component using the “replace matching” option. The model is then PCB and replaced component are then transferred from EDA Bridge to Simcenter FLOEFD for NX . Additional new option awareness: When transferring from EDA Bridge to Simcenter FLOEFD , you can now select to import components from EDA Bridge as parts or bodies, depending on your preference. You can therefore specify if you want a single multi-body part or an assembly of parts based on your preference. Component Explorer: 2R thermal and LED component model creation When you have a board level model with a high number of components and you want to create and edit the component thermal representations, this can be time consuming and there is also potential for errors in a repeated manual entry approach to this task. You can now specify LED or 2R components using Component Explorer directly in Simcenter FLOEFD 2412 quickly and also leverage Excel import of tabular feature data. 2R models procedure Specify the power in the 2-resistor column Select the component type Or for a list of components you can fill in the Component List Excel spreadsheet with 2 resistor parameters and then import it into component explorer to create all features instantly. This is illustrated in the following video: How to export all component temperatures quickly – a reminder on using the Heat Flux Plot While on the topic navigating and selecting multiple components, a reminder for users that you can use the heat flux plot to select multiple components or all components and then export in tabular format to Excel component temperatures. Learn how in this video below. It starts from a solved model looking at surface temperatures and flux plots enabled and shows the steps to export component temperatures: LEDs : specifying features individually or for multiple models The following video shows the selection of all LED components in Component Explorer and then either editing amp value and LED type directly for selected LEDs or importing a table of updated information via Excel spreadsheet. Component Explorer: Surface source power listing and summation It is advantageous in thermal management studies to have a clear way to see different sources power across a board and to consider overall power budget contributions. Within component explorer a new column has been added to reflect surface source power. The following video compares viewing a single source vs viewing a group of sources in Component Explorer. Smart PCB and Simcenter FLOEFD API Automation: SVG export/import To aid automation of script driven, automated what if analysis by leveraging the Simcenter FLOEFD API , it was identified by users as advantageous to be able to modify copper regions on a Smart PCB after it is imported into Simcenter FLOEFD 2412 . To support this, functions have been added to the API to export and import a Smart PCB to and from a set of SVG files. Export, creation script commands and SVG tags so you can fill areas with copper are detailed in documentation on Support Center for users. Material handling in CFD simulation of small gaps in assemblies: Fill Thin Slots feature Thin gaps are often in CAD assemblies. For example, these may be between heated components and heatsinks on a PCB or they are gaps between glued parts may exist in models. These gaps are meant to be filled with Thermal Interface Materials (TIMs) or glue during assembly procedure. For simulation models with unfilled gaps, their presence may impact thermal results. In Simcenter FLOEFD 2412 , you can opt to fill mesh cells inside a gap with specific solid material automatically in accordance with thickness criteria value. This is faster than creating specific geometry parts in the CAD model to fill the gap. The mesh and shape created is viewable with postprocessing tools stage. The following video illustrates the steps involved to leverage this new feature. Structural analysis: 2nd order elements This release introduces 2nd order elements to reduce reliance on overmeshing for some models to maintain accuracy e.g models such as thin plates with significant temperature gradients across them as a temperature load. This is selected in calculation control and setting the Nastran element type to 2nd order. 2nd order elements enable you to leverage a more Coarse mesh while approaching close accuracy to within an order of magnitude of a fine mesh 1st order solution. Minimum and maximum goals locations It is useful to have coordinates of minimum or maximum parameter points in a simulation model when exploring the design space or operational scenarios. Equally valuable are specifying coordinates for a specific objective function in a parametric study. You can now designate a point of volume or a surface goal within the Equation Expression field. Functions to locate a point of volume or surface goal are available in Equation Goal expression are written in the following ways: GoalLocationX({Goal Name}) GoalLocationY({Goal Name}) GoalLocationZ({Goal Name}) Where Goal Name is the name of minimum or maximum surface or volume goal. In the following video you can observe the set up. In this simple demonstration video below there is a lens and the aim is to leverage radiation modeling in a transient study around the source movement to look at the hotspot intensity and position created on a surface. The goal is to track the hotspot movement across the surface using a plot of position of the maximum value of radiation flux. Exploring results: Bubble chart for parametric studies You can now compare design points of resulting parametric study for multi-parameter optimization using a bubble plot. This means you can evaluate up to 4 parameters in this clear way on one chart. Simulation automation: new examples for the Simcenter FLOEFD API The new Simcenter FLOEFD API was introduced in version 2312, with Python support added in version 2406. In latest Simcenter FLOEFD 2412 release new examples and enhancements have been added. You can now find the following examples: Porous Media Equation Goal Smart PCB Export/Import Radiation Surface and sources LED and Two Resistor examples Solid Material You can also find out about controlling number of cores for a calculation, importing power maps, setting subdomains and a whole list of other enhancements. Information on these scripting commands and enhancements is located in the help reference and support center supporting document. Batch results processing for intermediate results If you are using any command line driven operations for batch analysis or server based batch analysis and you want to export intermediate graphical and Excel based results data, then please note you can do this as of Simcenter FLOEFD 2412 . This has advantages for tracking parameter fields during a simulation run on the server side and avoids processing and time overheads of copying huge data binary files frequently back locally. Introducing NX CFD Designer Siemens Digital Industries Software supports clients in the creation of a digital twin of their product and also promotes flexible open ecosystems for streamlined development by providing tools to suit the wide variety of engineering user personas and applications. In a move to further democratize CAE tool use and to foster earlier and wider use of CFD simulation in particular, NX CFD Designer has been introduced as of December 2024. NX CFD Designer is a new accessible CFD simulation tool specifically for designers working in NX that empowers earlier decision making. Designers access a set of common fundamental fluid flow and thermal simulation capabilities without leaving NX . Results are viewed on parts geometry and assemblies directly and you can explore performance of different design options. NX CFD Designer features guided simulation set up, automatic meshing, a unique solver technology from Simcenter FLOEFD , and easy results post processing and viewing options provide simulation driven insights for design improvement. NX CFD Designer is included within the NX installation kit and is accessible through NX Value Based Licensing. It is based on Simcenter FLOEFD technology so as a design requires more advanced analysis types at any point, such as transient simulation, then models are fully transferable to Simcenter FLOEFD for NX for further study. Schedule a meeting with CAEXPERTS and discover how the new version of Simcenter FLOEFD 2412 and the recently released NX CFD Designer can revolutionize your CFD and thermal simulation workflows. Take the opportunity to explore the enhancements and advanced features that drive analysis efficiency and accuracy, optimizing product development. Contact us now and take your engineering to the next level! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

3 ways to fight contamination in public buildings, transportation and production facilities Until 2020 Computational Fluid Dynamics aka. CFD for clean air was clearly not something the general public took notice of. But when the coronavirus pandemic spurred the global community into action unlike any other time in recent history, the need for clean, healthy air suddenly became more evident and a bigger matter of public attention than ever and with it CFD made it into mainstream media. And while COVID-19 seems long gone, and with it the public attention, the general need for clean purified air in public facilities, offices and transportation remains an important factor of life quality. But it’s is not only humans that need a certain air quality standard for a healthy living, likewise there are many products for whose production hygienic standards are very high and air contamination can pose a large harm to their respective industrial production facilities. In this constant challenge for healthy and uncontaminated air, computational fluid dynamics (CFD) simulation can be a crucial asset. CFD simulation software from Siemens ’ Simcenter portfolio is used by industry in various applications to ensure air gets purified and we can breathe safely and manufacture correctly performing products efficiently. 3 ways CFD simulations help to ensure clean air It goes without saying that CFD simulations cannot (and should not) be used as public health guidelines. But CFD simulations, if applied right, can help in 3 ways: Understand transport of unwanted human or non-human source exhalations, concentration and mitigation CFD simulation offers multiple approaches to model droplets and aerosols and their transport in space and time. CFD simulations can show where these particles travel to, how long they stay in air and what surfaces they impinge on. This would generally be useful in a small indoor setting with some sort of controlled airflow (rooms, cars, trains, planes, clean rooms, food production facilities, etc). Outdoors, the number of variables increases and forming any conclusive Improve/Redesign indoor environments for safety CFD simulations have long been used to understand indoor air flows and design HVAC systems and indoor environments for comfort and safety. CFD combined with design optimization can help analyze hundreds of ‘what-if’ scenarios for indoor environments. For example, you can analyze multiple venting configurations and air curtains to ensure most of the droplets and aerosols in a room are removed. Air purifiers and disinfectants can be designed and arranged to focus on areas of high droplet concentration. Design equipment to remove hazardous substances and purify air How do you sterilize and purify indoor spaces? Here, CFD simulations can help in designing purifying equipment. Today companies use such CFD methodologies to predict the spread of contaminating particles, hazardous gases or even viruses in almost anything, from public buildings, like airports, offices, schools or train stations, through public transport, like in airplanes, buses or trains to industrial facilities for wafers, food or pharmaceuticals. The following examples show how Siemens customers have used CFD simulations in the fight for clean air. CFD for clean air in (public) transport For the transportation industry (planes, trains and automobiles….ships too), COVID-19 has brought the indoor spaces into greater focus. Since then continued efforts have been made to ensure clean air in any type of vehicle – especially in those that move a large number of people. Airbus – Understanding cough droplet propagation in aircraft cabin Siemens and Airbus are using Simcenter STAR-CCM+ to understand the transport of particles/droplets from a human cough in an aircraft cabin. Using CFD simulations, the team has modeled the transport of cough droplets in an aircraft cabin. The impact and effectiveness of face masks in reducing droplet transmission in an aircraft is modeled too. The joint team developed the CFD methodology that tackles three things: Simulation of cough droplets from an average human Challenges in modeling aircraft cabin environment Steps involved in understanding risk of virus transmission from cough droplets Norton Straw (now element Digital Engineering) – ventilation in trains A UK Rolling Stock Owning Company (Trains, for readers from the rest of the world) contacted Norton Straw (now element Digital Engineering ) to help minimize transmission on-board the trains. Using CFD simulation with Simcenter STAR-CCM+ , the engineers at Norton Straw analyzed the airflow in the train cabin resulting from many mitigation strategies – windows open, plastic shield between passengers, different ventilation air flow, etc. The simulation results, also presented with Simcenter STAR-CCM+ Virtual Reality (VR), helped the manufacturer assess the ventilation effectiveness of different cabin configurations UES/USAF – Evaluating biological agent transport in aircraft How do you identify bioaerosol contamination hot spots in a medical aircraft and confirm decontamination after exposure? UES, Inc. partnered with the US Air Force Research Laboratory’s 711th Human Performance Wing (HPW) to find the answer using Simcenter STAR-CCM+ . Simcenter Engineering Services helped the team to simulate a cough from an infected passenger in a C-130 Hercules aircraft with multiple passengers. The results from the CFD simulations will be used in guiding improved procedures and sampling strategies for bioaerosol detection and surface decontamination. This helps the US Air Force make critical decisions regarding transport of infectious patients. CFD for clean air in buildings Another area where CFD simulations can be of great use is immobile indoor spaces where again the airflow and ventilation can be controlled. HOLT Architects/ME Engineering – Creating safer office spaces with CFD HOLT Architects , in association with M/E Engineering , published some interesting results on their strategies for reducing airborne transmission of viruses. M/E Engineering are well known for their expertise in CFD modeling. Using Simcenter STAR-CCM+ , they have helped HOLT architects study droplet transmission in their Ithaca, NY office. This study and the redesign of the ventilation system is helping employees work safely in the office. CAD models of the actual office space were used. When COVID was at its height CFD simulations of multiple coughs with and without a face mask were analyzed with Simcenter STAR-CCM+ . The analysis considered office arrangement, furniture, air flow patterns, barriers and location of people. Even if masks in office spaces are a thing of the past again, this kind of environment-specific cough simulation can help redesign HVAC and indoor ventilation systems. The smaller the space (and indoors), the lesser the variables that control droplet transmission. What if an infected patient coughs? What if the HVAC system is changed? What if a window could be open? Where to face sterilization/disinfection devices? These and other questions, CFD projects can answer. JB&B – CFD simulations show opening windows is key to healthy schools Simcenter simulation from JB&B shows how opening a window in a classroom dilutes contaminants from an infected student Jaros, Baum & Bolles (JB&B) , an engineering consulting firm, worked closely with the New York Times to show how schools can reduce COVID-19 exposure in classrooms by opening windows. The engineers at JB&B used CFD simulations to show how contaminants from an infected student circulate in a classroom for three scenarios – windows closed, windows open and with a fan and air cleaner installed. The story in New York Times is a must-read and is a brilliant visual representation of how to generally keep infected contaminants to a minimum in a classroom setting. CFD to design sterilizing and purifying devices Excelitas Noblelight – Developing UVC air purifiers with CFD simulation Excelitas Noblelight GmbH has been developing specialty light sources since the invention of the quartz glass lamp in 1904. Light, whether ultraviolet (UV), infrared (IR)or middle wave range, is at the heart of everything they do. The company has harnessed the power of light to solve a wide range of challenges in the manufacturing and process industries. With the help of CFD they also design and manufacture consumer products like the Soluva® air purifier, for removing airborne viruses from healthcare settings, public transport and classrooms. Engineering simulation is not only used during the product development phase, but also to understand the best way to deploy products in the field. We use CFD simulation to help our customers understand their processes and where to locate our UV or IR emitters to make them most effective. Dörte Eggers, simulation engineer at Excelitas Noblelight Norton Straw (now element Digital Engineering) – Novel air sterilization device In a similar fashion, abovemnetioned Norton Straw (now element Digital Engineering ) used Simcenter STAR-CCM+ to develop a novel concept of air sterilization device. Using CFD simulations and design optimization, the company has designed a small, light and energy efficient sterilization device. The device won the Innovate UK Covid Response Grant . The easily manufacturable device is currently being produced with additive manufacturing. Treating the recirculated air in indoor environments with such an air sterilization device is a solution for rail, automotive and building applications. Velocity contours inside a plate and fin heat exchanger inside the award-winning air sterilization device. CFD simulation using Simcenter STAR-CCM+ CFD for clean air in production facilities The application of CFD for clean environment does not stop at providing healthier environments for human beings. Also production lines of products that require high hygiene and material purity standards need to be kept free from hazardous gases, particles, mist or dust. FS Dynamics establishes a CFD method to assess contamination in lithography machines At the 2024 Realize Live Conference Europe , CAE Simulation Consultant FS Dynamics presented a high fidelity CFD methodology to assess contamination in lithography machines a key production facility in the Semiconductor Industry. Their work addresses the longstanding challenge of contamination modelling in lithography machines with a moving wafer using Computational Fluid Dynamics (CFD) simulations. Traditionally, the morphing and remeshing technique had been employed for capturing wafer motion while assessing airflows and contamination routes, despite its inherent slowness due to the remeshing bottleneck. FS Dynamics developed a refined methodology that leverages the overset meshing technique, a previously overlooked approach due to assumptions of its unsuitability for contamination modelling with low contaminant species concentration. Exceeding expectations, the novel CFD approach not only proves to be suitable for contamination modelling with moving wafers but also turned out to significantly reduce the computation time, enabling faster development cycles and more agile design iterations for super-clean Lithography machines. Creaform Engineering uses CFD for contamination-free vaccine filling lines Cleanroom for vaccine manufacturing. All the features were accounted for in the CFD simulations performed with Simcenter STAR-CCM+, including the walls, furniture, HEPA filtration, HVAC system, physical barriers with gloved access (windows surrounding the production line), conveyor, filling needles, capping machine and many measurement instruments control panels, as well as the Restricted Access Barriers with the accumulation table for vials The design of cleanrooms for manufacturing vaccines and other medications is precisely the topic of one of Siemens articles: Life Sciences Special Report, a compilation covering a range of applications of CFD, from medical device design to pharmaceutical manufacturing processes. In that article, companies Creaform and Laporte collaborated to perform a highly-detailed simulation of a cleanroom for vaccine manufacturing, to gain predictive insight to complement the traditional smoke tests in the process of cleanroom commissioning. The CFD simulations were used to demonstrate the effectiveness of the aerodynamic barriers and ensured proper flow path around non-sterile components of the machines. As pointed out in the article, “Not only was the regulatory compliance of the cleanroom at stake but with the high production rate of the line (hundreds of vial fillings per minute), contamination would represent a considerable financial and time loss because it leads to the waste of vaccine doses.” Clean Air uses CFD to virtually test fume cupboards Fume cupboards are essential for laboratories that generate airborne hazardous substances during experiments, processes and scale-up. They are designed to capture and remove gases, vapors and aerosols to reduce the risk of exposure to a safe level. In the 30 years that Clean Air Limited (Clean Air) has been designing, manufacturing and installing fume cupboards, protecting people has always been its priority. One of Clean Air’s unique selling points is its commitment to lead the fume cupboard industry in environmental safety and sustainability. Sulfur hexafluoride (SF₆) is used to prove the effectiveness of a fume cupboard during testing, but it has been identified as the most damaging greenhouse gas. The equivalent of approximately three tons of carbon dioxide (CO₂) is released during type testing, and another ton is released during onsite testing. Most fume cupboards are tested with the on site test, so roughly 1t CO₂e per cupboard then 3t per ‘type’ of fume cupboard. To reduce the carbon footprint Clean Air worked with Siemens partner Maya HTT and developed a new process that replaces design testing with computational fluid dynamics (CFD) simulation, ensuring that performance and safety is guaranteed without impacting the environment. Schedule a meeting with CAEXPERTS and discover how computational fluid dynamics (CFD) can transform your projects and ensure clean and safe air quality in critical environments! Whether optimizing industrial facilities, redesigning ventilation systems or designing purification devices, our experts are ready to help you implement innovative solutions with Simcenter STAR-CCM+ . WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br

Simcenter STAR-CCM+ In-Cylinder Solution , an add-on to Simcenter STAR-CCM+ , offers an in-cylinder-specific workflow, which involves minimal inputs, streamlined pre-processing and automated post-processing capabilities, all built around a fully automated grid generation, which relies on a morph-map approach. Complemented by class-leading models (spray, liquid film, ignition, combustion, emissions) and embedded design exploration capabilities, it helps you realize ICE CFD simulations in a productive way, enabling you to numerically predict the next, more efficient and more powerful engine design. In one of the most interesting examples, illustrated below, ammonia and diesel jets at various injection timings and angles have been studied and, for certain conditions, insufficient or too strong interaction of the two fuel sprays, for instance misfiring, can occur. Being able to rely on CFD for an accurate prediction of the phenomenon, reduces the need for extensive testing and allows for detailed understanding on how to design an engine, in which the scenario can be avoided. Simcenter STAR-CCM+ In-Cylinder Solution While we all continuously hear about the electrification of automotive powertrains, the reality is that the internal combustion engine will not disappear anytime soon and will be a staple of powertrains for decades to come. The push to downsize the internal combustion engine and the integration into hybrid powertrain platforms present many new challenges for engine development which can only be overcome using extensive CFD simulation. The In-Cylinder Solution , add-on to Simcenter STAR-CCM+ allows you to perform accurate in-cylinder CFD simulations of engines easily. Default settings and automatically-created post-processing output aim at giving the engineer a “running start”: you don’t need to be a CFD expert to set up and carry out one of the most challenging CFD simulations around! Simple Problem Set Up The In-Cylinder Solution add-on opens up a minimal interface which shows only those inputs required for setting up an in-cylinder simulation, presenting a top-down workflow: you start at the top and work your way down through various levels. You do not have to be an expert Simcenter STAR-CCM+ user to set up and run in-cylinder simulations using the add-on, as it uses an application-specific workflow and simplified interface. However, expert users can use those in-cylinder simulations as the starting point for performing more complicated multi-physics engine simulations that exploit the full range of Simcenter STAR-CCM+ simulation capabilities. The Simcenter STAR-CCM+ In-Cylinder Solution has been specifically developed to make setup quick & easy and leave time for the analyst to spend on engineering the solution rather than setting up the problem with lots of mouse miles and button clicks. From fast setup of typical multi-hole injectors which can easily be customized for spray targeting, to quick selection of fuels, to automatic setup of common post-processing outputs like liquid and vapour penetration plots and fuel mass tracking, the add-on has been designed and developed to make the simulation setup easy and allow engineers to derive the most value out of the simulation process. The geometry shown was obtained from the DYNAMO (Dynamic Analysis Modelling and Optimisation of GDI Engines) project which has been partially funded by the Advanced Propulsion Centre, UK. You are now being offered the tool and capabilities to setup a full-, half- or sector model, to simulate both a four- and two-stroke engine configurations, all within only a couple of minutes. Automated Meshing The In-Cylinder Solution add-on employs a simulation driver to run a transient mesh motion process. You only need to create a single initial mesh, comprised of trimmed cells and prism layers to capture boundary layer flow features. The entire mesh movement is automatically taken care of by the code, which automatically morphs & maps the grid to account for the motion of the piston and valves. The tool performs quality checks on the mesh as it morphs, automatically creating a new, undistorted, mesh when necessary and mapping the simulation results onto it. The mesh is automatically refined in critical areas in line with best-practices: around the valve, the valve seat, the valve throat, up into the ports and around the gasket gap. This is performed automatically for every simulation and does not require any manual intervention by the user. On the other hand, users have complete control over the mesh setup and can add additional regions of refinement, e.g. around a spark plug, as dictated by the scope of their analyses. The employed morph-map approach has been extensively tested and is highly conservative of mass for all practical applications. Using version 2206 or newer also allows you to reuse generated & stored meshes. This effectively eliminates grid generation time in the second engine cycle onwards, particularly useful in LES studies were many cycles have to be simulated to accurately capture cycle to cycle variability. Each mesh station in the cycle is saved as a file with .CCM extension in a pre-specified output directory and the °CA in the filename acts as the mesh identifier. This approach is particularly beneficial in LES studies, where a higher number of engine cycles need to be simulated in order to accurately capture cycle-to-cycle variability. In the latest version of Simcenter STAR-CCM+ , you can also include geometry parts, which will be meshed in a static way, thereby saving the time the morpher would spend to morph and map the grid. Be it intake plenum, body of a pre-chamber or else, this functionality comes handy whenever cell vertex movement is not important. The grid can be coarser in those areas, something that previously posed a few challenges. Note you can now benefit from a streamlined specification of initial & boundary conditions, for those parts, which enables you to arrive at the same setup that would normally require up to 3x more clicks, i.e. to manually include static-meshed parts. Liquid film activation with static parts is not currently supported, but will be addressed in future releases. Cold Flow  An in-cylinder simulation is amongst the most complex CFD simulations you can perform.  The combination of high-speed flows, mesh motion requiring an extremely high level of mass conservation , and very small time scales (fractions of a crank-angle degree typically need time steps in the order of 1E-6 [s]) means a lot of work goes into the setup, and the numerics must be carefully selected to accomplish stable runs with reasonable turn-around times.  This is even before we start layering on complex physics models upon including liquid fuel injection, e.g. Lagrangian spray, droplet-wall interaction, wall fluid film, and combustion, e.g. ignition, flame propagation, emissions formation, knock. For this reason, a lot of simulation performed early in the development process is concentrated on so-called cold-flow . This involves modeling the transient process of the airflow in the cylinder, typically with the objective of maximizing the trapped air mass and examining the bulk motion – swirl and tumble – that this flow induces. Often we also look at the evolution of turbulence to better understand the potential for fuel and air mixing and, specifically in spark-ignited engines, what the turbulence levels around the spark plug are at the intended time of ignition / start of combustion. Transient Intake Port Performance Evaluation in Cold-Flow Conditions Simcenter STAR-CCM+ In-Cylinder Solution allows you to set up cold flow simulations for multi-valve engines with the automated setup of mesh motion, letting you go from raw CAD geometry to running simulation in just minutes. Either URANS or LES can be employed, depending on the exact scope of your simulation projects and the effects to be captured numerically. Charge Motion / Mixture Preparation With Simcenter STAR-CCM+ 13.04, we took a big step forward with capabilities to set up and run charge motion simulations. This builds upon our previous cold flow capabilities by including the setup of liquid fuel injection and modeling the ensuing mixing process. Charge motion simulations allow engine manufacturers to improve combustion quality, by controlling the mixing of inducted air with injected fuel by identifying and rectifying rich or lean mixture regions, especially in critical parts of the cycle, such as when the piston approaches TDC and during spark ignition. The latter is especially important in today’s direct injection designs, in which the injection of fuel directly into the cylinder greatly impacts the bulk flow and turbulence level – the insight provided by simulation is more important than ever. Another critical role, a charge motion simulation usually plays, is the assessment for potential formation of harmful emissions. Again, ideally, we want to achieve high-quality mixing of fuel and air, especially challenging in direct injection systems, in which, at high load operating points, there is fuel injection during large portions of the engine cycle. Simulation tells us not only where we have lean and rich pockets of charge, but how fast the liquid fuel is evaporating, how much is impacting on surfaces in the cylinder, and whether it’s forming films or pools on those surfaces. All of these act as indications of the magnitude of harmful emission formation which, unless somehow mitigated, will need to be “cleaned up” downstream of the engine using expensive aftertreatment devices in the exhaust line. Over the years OEMs have developed large databases for design guidelines based purely upon charge motion, using just the bulk motion inside the cylinder, metrics of fuel and air mixing quality, and levels of turbulence around the spark plug which tell them whether combustion is going to be good or not, saving them valuable engineering time, especially in the early design stages of a combustion system. A wide variety of break-up, droplet-wall impingement, as well as liquid film models provide the needed toolset for users to be successful in this kind of simulations, prior to carrying out more advanced, combustion, studies. Moreover, temperature-dependent properties applied by default in versions 2210 or newer, significantly reduces manual interaction: As far as high fidelity in simulating fuel sprays is concerned, adopting constant properties is far from sufficient. The burden on the user, however, to manually switch properties of the Lagrangian phase to temperature-dependent values, is quite high. Using Simcenter STAR-CCM+ In-Cylinder Solution , this step is fully automated by making use of data stored in a database, being shipped with the software. The benefit becomes more apparent in plots like the one on the right. Capturing mixture preparation accurately is of utmost importance in order to have the correct mixing of fuel & air before proceeding to the stages of in-cylinder combustion. Fuel mass is depicted here with constant versus temperature dependent properties. Combustion, Knock & Emissions Simcenter STAR-CCM+ In-Cylinder Solution offers combustion capabilities, e.g. ECFM-3Z and ECFM-CLEH, an advanced (ISSIM) as well as a standard ignition model, knock models (Tabulated Kinetic Ignition – TKI), complemented by emission models, such as CO, NORA NOx and soot emission models, for example Soot Sectional Method. With three releases per year, we are continuously increasing the breadth of capabilities with further high class combustion model options and sub-models to capture knock & predict emissions. The increasing interest in modeling of alternative / non-carbon fuels being the driving factor, all combustion models offered in the code, are fully compatible with any fuel of type CxHyOzNw, such as hydrogen (H₂) and ammonia (NH₃). At more extreme operating points and pressure / temperature conditions, certain assumptions that were valid previously, such as the ideal gas law, may not hold any longer. To stay on the safe side, real gas using the Redlich-Kwong model, offered in the tool, helps users to accurately predict effects the ideal gas law can’t, such as Van der Waals forces, compressibility & non-equilibrium thermodynamic effects, variable specific heat capacity etc. To generate useful combustion chamber design information without relying on a detailed model, the Specified Burn Rate (Wiebe) model can be employed, specifying the burn rate via a form factor together with the duration of combustion. The approach can be, as well, used to generate heat transfer boundary conditions for use in an engine CHT analysis. The graph shows a cylinder pressure curve from an industrial 4-stroke diesel engine. Here the application of the real gas model improves the prediction of the cylinder pressure for both a part and full load operation condition, with the peak pressure more closely matched to test data. Propagating Flame Simulated with Simcenter STAR-CCM+ In-Cylinder Solution Modelling combustion & emissions in some cases requires libraries of pre-tabulated chemistry. Instead of generating those using either DARS or third-party tools – or making use of tables for standard fuels available on Support Center – users can now take advantage of the ECFM table generators, in versions 2210 or newer: the capability to generate tables for laminar flame speed, engine knock, soot, equilibrium, the latter needed in simulations with ECFM-CLEH combustion model. This provides additional flexibility by removing reliance on external tools. Conjugate Heat Transfer (CHT) Going beyond standard simulations, with downsizing for efficiency in today’s engine designs, effective thermal management is critical. Designs reaching peak levels of thermal efficiency without exceeding thermal design limits are studied using full-engine conjugate heat transfer simulations. Simcenter STAR-CCM+ In-Cylinder Solution also provides a single user environment to simulate both the fluid and the solid side, i.e. the in-cylinder & engine CHT models. The exchange of heat transfer boundary conditions between the two models, as well as the ability to automate the workflow, are points that can be realized in a straight-forward manner: Users are offered an automated way of calculating and exporting cycle- averaged boundary heat transfer data (averaged spatial heat transfer coefficients and reference temperatures), which will, in turn, enable thermal boundary conditions to be applied in a subsequent engine CHT analysis. The workflow therefore becomes significantly more streamlined and efficient. Traditionally, the in-cylinder / CHT approach required usage of multiple CFD packages. Different file formats required and data mapping between software packages have always posed operational challenges. Now that the coupled in-cylinder/engine CHT analysis can be carried out entirely within Simcenter STAR-CCM+ , the overall process is greatly simplified and allows for automation of the combined simulation cycle through JAVA scripting. Workflow / Post-Processing The application-specific workflow of Simcenter STAR-CCM+ In-Cylinder Solution requires minimal inputs by the user, thereby decreasing the overall turnaround times. Several functionalities enable a seamless pre- and post-processing of in-cylinder simulations. Only a small subset of those is depicted here. The add-on presents users a grouped list of UI objects decreasing “mouse scrolling miles” to find the object of interest. Usage of subfolders enables categorization based upon the domain part (cylinder, ports/valves) or nature (mesh, solution, physics). The benefit of this will be even more apparent in multi-cylinder cases, planned to be incorporated into future versions. Generating / exporting plot & scene hardcopies, the capability allowing users to fully customize the filename, results in a file list already in chronological order, with °CA / degCA as a direct indicator. Hence, the list can also be used for video generation without manual conversion of image filenames. Another useful functionality, Cyclic Mode of plots, allows visualizing data in a cyclic pattern, particularly useful in in-cylinder analyses. Leveraging the feature allows users of the add-on to compare engine cycles by plotting the corresponding 2D (X-Y) curves on top of each other, highlighting differences out of the box; that is without any form of manual interaction, since the mode is by default active in all relevant plots. Finally, with another useful and recently introduced post-processing feature, visualizing integrated heat release rates, mass fraction burned (MFB) 10-50 or 90%, as well as the combustion duration, is zero clicks away, thereby taking productivity to the next level. Stop exporting heat release curves and carrying out tedious computations manually, in spreadsheets, in order to assess the performance of your engine design. All tree objects needed to evaluate those quantities are generated automatically. Automated Design Exploration Unleashing the power of the Simcenter STAR-CCM+ as a platform, with the embedded tool Design Manager, users can leverage the automation capabilities, scalability, and flexibility of the platform to easily and quickly execute design studies in order to optimize their engines for the next generation. Additionally, since the In-Cylinder Solution add-on automatically creates a parametric model, you are only a few mouse clicks away from easily sweeping multiple operating conditions to understand the bulk motion and turbulence at different speeds & loads. A swap of geometries has also been introduced allowing for easy setup of geometric design variation studies and the re-use of existing simulation setup on another geometry. Validation against experiments Both the In-Cylinder Solution and Simcenter STAR-CCM+ have been extensively validated for engine simulations, using both proprietary and public domain engine designs. One example is our validation of the University of Michigan Transparent Combustion Chamber-III (TCC-III) Optical Internal Combustion Engine, which is a 2-valve head, 4-stroke, spark-ignition engine with a pancake-shaped combustion chamber. The results demonstrate excellent correlation with global thermodynamics variables, including cylinder trapped mass, pressure, and temperature, and, compared with visualization from the experimental rig, the major features of the flow field are also well captured. Another detailed validation study has been conducted with the Research and development department of Daimler AG , proving excellent correlation between high-speed / high resolution PIV measurements and Simcenter STAR-CCM+ predictions in a state-of-the-art GDI engine configuration. Committed to the IC Engine Market Siemens Digital Industries Software is completely dedicated to the IC engine simulation market, recognizing that internal combustion engines are here to stay and that only advanced simulation can deliver the cleaner more efficient engines that society deserves. As part of Simcenter STAR-CCM+ , the In-Cylinder Solution add-on receives updates three times per year and we will continue to add features that address these simulations. At any time, a dedicated team of outstanding engine CFD experts will be there to support you, solving even the toughest problems in in-cylinder CFD. Would you like to take your engine engineering to the next level? Schedule a meeting with CAEXPERTS experts today and find out how the In-Cylinder Solution , integrated with Simcenter STAR-CCM+ , can revolutionize your in-cylinder engine CFD simulations. With a streamlined workflow, fast setup, and powerful automated post-processing capabilities, we are ready to help you numerically predict the most efficient and powerful engine designs. Don’t miss out on your innovation transfer – contact us today! WhatsApp: +55 (48) 988144798 E-mail: contato@caexperts.com.br