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

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

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

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

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

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

In early 2023, the International Maritime Organization (IMO) set ambitious targets to achieve a ecological marine industry and net-zero emissions by 2050, a dramatic acceleration from the previous target of 2100. The industry has been sailing in rough seas since then, with widespread implications for the life cycle management of ships, from concept design to in-service operation and decommissioning. Most industry stakeholders anticipated this development, having observed the sequence of progressively stricter environmental regulations. During the Clean Energy Action Forum 2022, American Bureau of Shipping (ABS) CEO and President Chris Wiernicki outlined so-called credible fuel pathways, which will be the main driver in ship operators' selection of new fuel types to meet zero carbon targets. The technological readiness schedule is at the heart of decision-making, which can be divided into short, medium and long-term periods. According to Wiernicki, such readiness is given if the following four pillars can be established: A solid business case Scalability Provision and use of certifiable data Mitigation of unintended consequences Short-term solutions for a ecological marine industry Short-term solutions are liquefied natural gas (LNG), methanol and first and second generation biofuels. According to Clarksons, 4.1% of the world's commercial fleet can use LNG as fuel. This represents 91% of the total share of the fleet capable of alternative fuels. Furthermore, the dominance of LNG is reflected in the current order books, with 33.3% opting for LNG, followed by Liquefied Petroleum Gas (LPG) with 2.3%, methanol (1.2%), ethane (0.3%) and hydrogen (<0.3%). %). Orders combining LNG with the “ammonia ready” option represent up to 10% of these orders. The main incentive for using biofuels is that the existing fuel infrastructure requires few or no adjustments. For smaller, short-sea and coastal vessels, suitable battery solutions are already available, and successful prototypes of fully electric and autonomous vessels can be referenced. Liquefied natural gas (LNG) tanker Medium-term solutions The medium-term period will see further methanol advancement, with ammonia rising, as compatible engines become more available over the next two years. Long-term projection In the long term, green fuels (fuel produced from biomass sources through various biological, thermal and chemical processes) will be available alongside blue hydrogen, i.e. hydrogen produced from natural gas and supported by capture and carbon storage (CCS). CCS technology readiness is crucial both onboard and ashore. Classification societies are leading the way in evaluating nuclear power as an option for large commercial ships that can accommodate the technology. Simcenter simulation chain – setting sail Each step of the journey to achieving the net zero goal brings distinct engineering challenges to the ship's operation; to name a few: Gas dispersion After treatment Swinging in tanks Tank boiling (process of unwanted temperature increase in tanks that compromises the cryogenic state of the gas). Post-treatment to reduce and remove emissions (purifiers) Cryogenic gas leaks during refueling (refueling process) Trip planning – fuel generation and emissions profiles Simcenter STAR-CCM+'s multiphysics capabilities for hybrid multiphase modeling are being leveraged to provide simulation-based predictions for these problems in both concept design and forensic analysis. At a higher systems level, Simcenter Amesim enables analysis of engine and propulsion configurations with varying levels of detail and fidelity, extending to trip planning, fuel consumption and emissions profiles. Simcenter Amesim The first coastal ships are already sailing completely electric, while the nautical industry has begun to embark on its electrification mission. Simcenter Amesim, Simcenter Motorsolve and Simcenter STAR-CCM+ offer many standardized solutions to consider battery pack sizing, powertrain design or help prevent thermal runaway. Uncontrolled battery – Simcenter CFD simulation Big ships, zero emissions, ecological marine solutions Turning to the larger ships and workhorses of global commerce, we want to focus on how Simcenter suite simulation technology is helping engineers address short- and medium-term solutions for alternative fuels. Among the four pillars of viable fuel pathways, when it comes to ammonia, mitigating unintended consequences must be addressed early in implementation. LNG and ammonia are stored in a cryogenic state, which means that any exposure to environmental conditions, for example through leaks during fueling, will lead to instantaneous vaporization (flash boiling), putting the crew at risk and presenting numerous environmental hazards . The sudden boiling of liquid spray from a leaking pipe into the environment occurs because the ambient pressure is below the saturation pressure of the liquid fuel. Depending on the direction of the spray and the distance from adjacent structures, dispersed vapor cloud precipitation may occur: the impact of liquid droplets remaining from the spray, mostly vaporized, and therefore highly flammable. Case Study: Bunkering Fueling is a critical operation on board ships at sea or in port. Successful fueling requires the safe transfer of fuel to the ship's tanks without overfilling, spills or leaks. Simulation can help in the first stages of the supply process At the beginning of the fueling process is planning the trip to determine the amount of fuel to be supplied. Simcenter Amesim's marine library allows you to connect your engine and powertrain model to the voyage and maneuver simulation environment, accounting for hotel loads, auxiliary engine fuel consumption, wind, waves, and current effects on power characteristics. After determining the amount of fuel, including the reserves to be filled, planning begins as to which tanks will receive the fuel. Here, a model of the tanks, pipes and valves, ballast system and fuel intake through the supply itself is essential for predicting and monitoring performance and ensuring the desired hydrostatics of the ship. These items and action plans for leaks or spills are typically discussed at the pre-bunker conference – which can be digitally supported by rapid time models for quick responses to what-if scenarios. The key to success The Simcenter Flomaster simulation environment allows you to generate result envelopes for different supply scenarios, but can also be used upstream in the design phase to size pumps and lines to achieve the ideal time for filling, emptying and sounding tanks. Ultimately, critical metrics from the simulation can be automatically entered into the petroleum ledger in accordance with MARPOL Annex I. Avoid possible system failures and hazards The interest now is in possible system failures and dangers to the crew and the environment. Models can be configured to produce explicit responses to relevant regulations, for example IMO IGC and IGF codes for LNG. LNG is generally stored at around -162°C and presents cryogenic hazards such as frostbite and human skin burns, and fire and explosion risks during the transition to a gaseous state within the flammable range. In the case of ammonia, skin burns, irritation and inflammation of the crew's respiratory system and eyes may occur. High concentrations of gas in the air, especially in confined spaces, can lead to explosions or fatal results to human life. Learn how CFD simulation can help you analyze unintended consequences To better understand the dangers of flash spray boiling and gas dispersion, we analyzed the problem of refueling at sea, where we simulated the cryogenic dispersion of ammonia from a tube due to a leak during fueling. Using simulations with Simcenter STAR-CCM+, the complex physics of the instantaneous boiling of cryogenic gas dispersion can be studied. Metrics sought include the extent of the plume, the concentration of ammonia at certain points and whether or not rain will occur. While outdoor deck workers are unlikely to be exposed to fatal concentration levels exceeding 2,000 ppm for 30 minutes or more, stinging or burning sensations in the eyes and respiratory system may occur from exposure to as little as 70 ppm above. in the same time frame, according to the National Institute of Health. These levels are not unattainable within closed rooms such as engine compartments, which highlights the importance of employing simulation technology for risk assessment and countermeasure design. In our fictional supply scenario, liquid ammonia was discharged from a leak horizontally in the receiving vessel's supply infrastructure at a pressure of 8 bar, causing rapid dispersion into the environment. Over the course of 10 seconds, approximately 1 ppm can be measured in typical locations for work operations. No rain was detected on the deck, that is, the entire leaked mass passed into a gaseous state. Stay integrated – all hands on deck for a ecological marine future In this post, engineering problems related to ecological marine targets were presented, from conceptual design to onboard operation and assisting in the generation of mandatory documentation. There will certainly be implications of your specific component or problem studies for fundamental aspects of overall ship design. In the likely event that you become overwhelmed by the complexity and interconnectivity of spanning the design space, Simcenter HEEDS will provide nautical reference points for port solutions. Reveal the ultimate solution: Leverage your insights for the best results The role of Simcenter HEEDS is twofold. On the one hand, its workflow manager acts as a spider in the web of Simcenter CAE tools through real-time I/O mapping and administration. Additionally, its multidisciplinary design optimization can be leveraged in the subtool, holistic process, or a combination of both. Setting realistic optimization constraints and leveraging the powerful Simcenter HEEDS post-processing suite to gain key insights will be critical to a safe return to port. Time to anchor There is an ocean of engineering problems when it comes to the design and operation of ships and floating platforms, and Simcenter has many more tools available than those mentioned in the blog in question. Have you ever wondered about the safety and comfort of passengers on your speedboat? Simcenter Madymo is the beacon to follow. Move full steam ahead towards your ecological marine future using performance engineering integrated with Simcenter. If you are navigating towards a sustainable future in the maritime industry, it is time to act. CAEXPERTS is here to help you chart your course toward short, medium and long-term solutions for greener shipping. Join us for a strategic conversation about the challenges and opportunities that await, and discover how we can help you successfully navigate this new landscape. Schedule your meeting with us now and prepare for a journey towards a more sustainable future for our industry.

The iron & steel industry are highly material and energy intensive industries. Energy constitutes a significant portion of the cost of steel production, from 20% to 40%. Thus, improvements in energy efficiency result in reduced production costs and thereby improved competitiveness. Another challenge with the steel production is that the CO₂ emissions are high. On an average, primary steel plants emit three tons of CO₂ per ton of steel. The global best is 1.4 tons of CO₂ per ton of steel. In order to strengthen the green competitiveness and achieve the goal of low-carbon and clean production, Steel industry adopts four strategies of energy savings: (a) Increase the energy efficiency: waste heat recovery, enhancing the efficiency of energy system/equipment optimizing the operation and energy management. (b) Develop and utilize low-carbon fuels such as bio mass, (c) Maximize the value of fuel gas and (d) Develop the end-of-pipe technology such as CCS. Steel Production Process In the primary ironmaking process, the raw material like iron ore, coke, and lime are melted in a blast furnace resulting in molten iron (hot metal). The key methods are BOS (Basic Oxygen Furnace) and the more modern EAF (Electric Arc Furnace). In the Secondary steelmaking, the molten steel produced from both BOS and EAF routes is treated to adjust the steel composition. The secondary steelmaking processes involve Stirring, Ladle furnace, Ladle injection, Degassing, CAS-OB (composition adjustment by sealed argon bubbling with oxygen blowing). In Continuous casting the molten steel is cast into a cooled mold causing a thin steel shell to solidify. The shell strand is withdrawn using guided rolls and fully cooled and solidified. The strand is cut into desired lengths depending on application; slabs for flat products (plate and strip), blooms for sections (beams), billets for long products (wires) or thin strips. In primary forming, the steel that is cast is then formed into various shapes, often by hot-rolling. Hot rolled products are divided into flat products, long products, seamless tubes, and specialty products. Secondary forming techniques give the steel its final shape and properties. These techniques include cold rolling, Machining (drilling), Joining (welding), Coating (galvanizing), Heat treatment (tempering), Surface treatment (carburizing). Siemens’s Simcenter STAR-CCM+, a Computational Fluid Dynamics (CFD) based offering is used in Steel industry for (a) enhancing the efficiency of energy system and equipment (b) optimizing the operation and energy management (c) Analyzing and comparing various technologies for process optimizations. Simcenter STAR-CCM+ provides detailed analysis of fluid flow, heat transfer and other physio-chemical phenomenon in the equipment at the actual scale, and operating conditions which otherwise is not possible using experimental techniques. This technology gives detailed threedimensional understanding of the process parameters like flow pattern, temperature, mixing profile, chemical composition, heat transfer, combustion, chemical reactions, casting, etc. Simcenter STAR-CCM+ also offers a very robust Discrete Element Method (DEM) capability to model solid particulate flows. Basic Oxygen Furnace Basic Oxygen Furnace The basic oxygen furnace (BOF) is a part of the steel making process where pure oxygen is used to convert molten pig iron into steel by oxidizing carbon. In top-blown furnaces, a supersonic oxygen jet is blown through a vertically oriented lance onto the molten metal bath, creating a cavity at the bath surface. Important parameters are the resulting shape and size of this cavity because they contribute to the interfacial contact area between the oxygen and the metal. The fast decarbonization reactions at the molten metal / gas interface lead to the formation of carbon monoxide (CO), which may react with oxygen in the top space of the furnace to produce carbon dioxide. This latter process is generally referred to as the post-combustion reaction and is highly exothermic (ΔHR = -283 kJ/mol). In order to optimize the energy efficiency of the process and to increase the amount of scrap additions that may be remelted in the bath, there is a strong interest in promoting the post-combustion of carbon monoxide and the transfer of the energy released by this reaction to the liquid metal. Alternatively, bottom-blowing converters are used where the oxygen is injected at the bottom of the furnace. This is leads to additional agitation and mixing, similar to the results shown in the ladle section. Converter geometry, lance configuration, number dimension and positioning of the bottom inlets as well as the flow rates affect the flow field and therefore the oxidation process and this offers opportunities to improve the process and its efficiency. To simulate a top-lance BOF with a focus on the jet penetration and its interaction with the molten metal Simcenter STAR-CCM+ offers the Volume of Fluid (VOF) method as well as the EulerianmMultiphase model with a model extension to capture the free surface correctly, the so-called LargemScale Interface (LSI). Both methods support reactions in each phase and recently a surface reactionmmodel for VOF was introduced to consider reactions only at the free surface, where oxygen gets in contact with the carbon in the liquid melt. Case Set-up and description In the case presented here a pure oxygen jet from above interacts with the melt. The transient VOF simulation is done on a 2D axisymmetric domain with 125.000 hexahedron cells, assuming ideal gas behaviour for the gas phase. Both phases are modelled as multi-component. The gas phase consists of O₂, CO, CO₂ and N₂ while the liquid phase contains Fe and C. To model the decarbonisation, two surface reactions at the interface are applied, forming CO in the gas phase: C(l) + O₂(g) → 2CO(g) C(l) + CO₂ → 2CO(g) 2CO(g) + O₂(g) → 2CO₂(g) Figura 1: Left: Oxygen jet entering the BOF and penetrating the liquid melt. The black line indicates the free surface. Right: Red shows the liquid melt, blue the gas phase, yellowish color indicates liquid droplets. Results Simulation results in Fig. 1 show the deep penetration of the oxygen jet into the melt. Smaller and larger melt droplets are lifted up and splash against the wall. The depth and the form of the cavity is permanently changing since this case is inherently transient, resulting on the one hand in a larger surface area and on the other in an additional mixing due to these fluctuations. Fig. 2 shows the oxygen distribution in the gas phase. At the lance a pure oxygen jet enters the furnace. A part of the oxygen is consumed by the decarbonization at the free surface and another part is converted in the gas phase to carbon dioxide. A closer look to the free surface (Fig. 3 and 4) shows that a lower Carbon content is only found in the vicinity of the free surface. It also indicates that the wiggles are increasing the free surface and therefor the reaction rates significantly, since the lowest C content is found there. On the gas side, a higher CO mole fraction is found in the wiggles but also on the right-hand side close to the free surface. This is an area where the gas velocities are not that high (see Fig. 3) and CO is not transported efficiently into the bulk. Ladle Stirring In a ladle furnace, argon is injected through a refractory-lined lance or through a permeable refractory block in the bottom in order to maintain a uniform temperature and composition. A benchmark exercise for such a furnace is described below. The geometry used and further details are as specified by the German Steel Society’s (VDEh) 7th meeting in 2010. Problem Description The ladle holds 185t of steel at 1600oC. Argon is introduced from the bottom giving rise to heterogenous gas plumes causing the steel to be stirred. The goal of the simulation was to determine the time required to achieve complete mixing. Figure 5: Details for ladle stirring benchmark. Red region: slag and yellow region: the molten metal A volume-of-fluid (VOF) modelling method was used in STAR-CCM+ to account for the interface between gas and liquid. A discrete particle tracking algorithm to track the injected bubbles (with amRosin Rammler size distribution) and ideal gas law dependent density based on the height of the molten metal was used for the injected gas. Two-way-coupling with consideration of drag, lift and turbulent dispersion force is employed between the gas and the liquid phase. A numerical tracer is introduced to track the extent of mixing and the required mixing time. Results of ladle benchmark The results from this study are shown below in Figs. 6(a) to 6(c). Fig. 6a is showing a snapshot of the bubble distribution and rise in the domain. Figure 6b shows the velocity contours of the melt cause by the injection of the Argon jet, clearly showing a developing jet flow with velocity decreasing with height. The velocity magnitude was within 15% of the analytical results. Fig. 6c shows a pictograph from a water-based experiment on a scaled model of the ladle. It indicates that the flow filed matches the simulation results qualitatively (since no velocity measurements were performed in the water experiment). Fig. 7 shows that the mixing time results from simulation (⁓120s) compares well with those from experimental measurements (⁓120-140 seconds) indicating that CFD simulation allows detailed insights in to the flow behaviour. Further geometries can be investigated using simulation combined with an automated direct optimization approach to find an engineering solution to mixing. Figure 6: (a) Plume of rising bubbles (b) Argon jet velocity field (c) Photo of a scaled water experiment Figure 7: Comparison of time required for complete mixing with experimental values. Continuous Casting After a steel alloy has been manufactured, molten steel needs to be processed for further use. We can discern two kinds of wrought material following the steel production: either ingots that can be used down the line in specific shape casting processes or continuously casted steel rods of various cross section geometries. Production Challenges To ensure good overall product quality certain process aspects are key: (a) Transport and location of non-metallic inclusions and slag inside the ingot or strand (b) Temperature management of the alloy to ensure desirable metallurgical properties (c) Defects such as macro and micro shrinkage defects These aspects are closely linked to one another as well as the overall manufacturing efficiency. Thus, the continuous or ingot casting process is a conglomerate of different physical phenomena and engineering challenges involving Heat transfer (radiation, conduction and convection), Phase change (solidification in the metal & boiling due to spray cooling), Material transport, Joule Heating, Magneto Hydro Dynamics (stirring in the strand), Metallurgy including shrinkage defects, Chemical reactions (Exothermic sleeves and powders). Figure 8: Flow pattern during steel ingot solidification. Creation of alpha pore and shrinkage defects Problem & Results Using a pseudo transient approach, Simcenter STAR-CCM+ was used to predict the shell thickness along the strand as well as the position of the solidification tip. The validation work was based on the work of Ushyima where the shell thickness is analytically determined. The caster is assumed to be prefilled with superheated steel, the walls are set to be convective. Inlet speed is also given and at the outlet the casting speed is applied. Figure 9: Shell thickness validation for single strand caster. Left plot shows the comparison of results. Right plot shows the outline of the geometry after Ushijima The volume of fluid multi-phase approach is used to investigate the slag-melt- air interaction. Phase change modelling is enabled inside the VOF model. By expanding the computational domain beyond the fluid domain alone to include i.e. the mold or rollers the effect of assumptions in boundary conditions can be mitigated. Simcenter STAR-CCM+ has a set of criteria functions for defect analysis inside the cast part. Material properties are also key to accurately predict flow and solidification behavior inside the cast. The software’s open structure allows one to import own temperature depended material data or you use the materials on offer inside the dedicated metal material database. Results (Fig. 9) indicate that STAR-CCM+ can predict the shell thickness accurately and can be used to evaluate the casting process effectively.z. Conclusions Simcenter STAR-CCM+ has been used for the detailed analysis of basic oxygen furnaces, ladle mixing and continuous casting. Details including fluid dynamics, decarbonization reactions on the surface as well as oxidation reactions in the gas phase have been accurately modelled. The Volume of Fluid (VOF) method as well as the Eulerian Multiphase model with a LSI extension can be used to capture the free surface correctly. Siemens Simcenter STAR-CCM+ opens the door to further optimization and process improvement. Do you want to take the efficiency and sustainability of your steel industry to a new level? At CAEXPERTS, we can help you design energy-saving strategies and advanced technologies to reduce CO₂ emissions. Schedule a meeting with us and discover how we can help your company optimize processes, reduce costs and achieve your production goals in a more sustainable way. Don't delay, get in touch now and take the first step towards a greener and more competitive future!

For nearly 40 years, Simcenter Femap has been an essential tool for analysts to model complex engineering structures. The latest version, version 2401, includes several new features and improved functionality based on user feedback. Many of our customers use composite materials to reduce weight in their designs, and with new features in Simcenter Femap 2401, it is now easier to model composite structures. This includes having direct access to layups calculated through the Siemens Fibersim product . Layup Builder – Overview Simcenter Femap 2401 introduced a new, more efficient way to create composite finite element structures with different layouts. This is done through a new dockable panel called Layup Builder. In previous versions, creating composites required creating a Laminate property and a corresponding Layup to define stacking, followed by assigning that property to an area or region of the mesh. This process had to be repeated for all other areas. See how the new Layup Builder can provide a different approach and potentially a more efficient way to create composite finite element models. Layup Builder – Using external data The new Layup Builder dockable panel in Simcenter Femap 2401 offers an alternative to manually creating layups by providing the ability to define a general “Ply Stackup”. Once defined, any subset of lines in the Layup Stack can be applied to any part of the model to automatically generate the required layups. One method for defining a “Layup Stack” is to use data provided by another application. As seen in another video, Layup Stacks can be defined manually or loaded from an existing layup, but this video focuses on the “Attach composite HDF5 file” method. Incompressible fluid splashes There are two essential aspects of unrest: (a) predicting its onset and (b) mitigating the effects of unrest once it begins. Avoiding splash-inducing resonance will also reduce liquid pressure on the walls of the container. However, a side effect may be that the vessel is tuned to a resonance that could cause catastrophic failure. Therefore, to avoid splashing and reduce the risk of container failure, it is best to consider the coupled effects of the liquid and container to determine the coupled hydroelastic modes. This technique ensures that the modes that initiate oscillation and failure do not occur. Simcenter Femap 2401 includes extended support for incompressible fluid dynamic analysis. While previous versions of Simcenter Femap included support for modeling incompressible fluid implicitly as a virtual fluid mass, Simcenter Femap 2401 allows users to model incompressible fluid explicitly as a fluid mass defined with solid elements. The defined fluid mass will allow users to model complicated fluid volume shapes and extended fluid volume types, such as volumes with splash-free surfaces. We can use these features to calculate the coupled hydroelastic modes of the structure. Analysis Set Manager Simcenter Femap 2401 makes it easy for ABAQUS users to configure their analyzes using familiar terminology and methodologies. Additionally, improvements have been made to the Simcenter Nastran Multi-Step Nonlinear Structural Solution (Sol 401) due to the large number of strategy parameters it uses. Improvements in quality of life To make the functionality more discoverable, Simcenter Femap implemented a Command Finder in version 2301, enhanced it for version 2306, and improved it again for version 2401. Would you like to maximize your efficiency in modeling composite structures with Simcenter Femap 2401? Schedule a meeting with us at CAEXPERTS and discover how new features like Layup Builder and extended support for incompressible fluid dynamic analysis can optimize your designs. Don't miss the chance to explore these innovations and boost your productivity. Get in touch now!

Since Gordon Moore, co-founder of Intel, postulated the law that bears his name about doubling the number of transistors every two years, there have been dramatic improvements in the computational capabilities of electronic devices. Reducing component sizes coupled with increased demand for computing power has resulted in ever-increasing power densities, requiring optimized and advanced cooling configurations to maintain a safe operating temperature. Electronic thermal management is a separate topic and beyond the scope of this blog. However, I would like to discuss one of the consequences of increased electronic performance – noise! Anyone who works during the summer in an office is no doubt accustomed to their computer or laptop fans spinning as the number of applications running in parallel increases, or even more fun, getting to run advanced Computational Fluid Dynamics (CFD ) or Simulation using the Finite Element Method (FEM). Fan noise, while simply considered an unavoidable inconvenience, is the result of a complex interaction between the fan itself and the airflow it generates. For this reason, it can sometimes be called flow-induced noise or aeroacoustics. Despite the need to properly cool all these electronic components in the cramped space of a modern laptop, people have come to expect the noise generated to be non-intrusive. Noise-canceling headphones can help you here, but they are far from an ideal solution during a hot summer day. What's more, acoustic performance has become one of the key indicators of high-quality laptop brands – combining the gentle hum of fans with a set of clear, vibrant, well-placed speakers that play your favorite tunes. This puts a lot of pressure on the engineers who develop these systems. Let's discover how state-of-the-art acoustic simulation tools can help dedicated engineers predict acoustic performance earlier, faster and more reliably. The inevitable “bad” sound… When analyzing the noise signature of a fan, there are typically two components, tonal noise and broadband noise, as shown in Figure 1. Tones can be clearly visible as higher sound pressure levels resulting from periodic interactions of the incoming air. with the fan blades (blue circles). The broadband noise component is caused by random loading forces on the blades that can be induced by factors such as turbulence ingestion or boundary layer development (green line). Figure 1. Free-field response detailing tonal peaks (blue circles) and broadband noise (green line) Considering that fan noise is the result of the interaction between aerodynamic flow and acoustic wave propagation, both airflow and acoustics need to be simulated. Acoustic wave propagation can be included directly in a CFD simulation already used to evaluate the design's cooling performance, but this – although possible – can present significant challenges. These challenges are mainly caused by the significant differences in length scales between the acoustic waves and the flow. This means that high-order physics schemes and exceptionally long calculation times are required, so this approach is not always viable. Hybrid approaches have been developed in response to this, in which sound generation and sound propagation are separated. CFD data is used to reconstruct sound sources due to flow effects, while acoustic simulation models are used to propagate sound waves caused by these sources. This offers the advantage of enabling more efficient low-order flow simulations and taking advantage of efficient acoustic resolution technologies. Figure 2 illustrates how a model is prepared for an acoustic analysis showing the air finite element mesh around the laptop (2A.), the internal mesh (2B), the connections between the inside and outside, the ventilation openings (2C) and the inlet grille under the laptop (2D). The next step in an acoustic analysis is to define the source region – this can be obtained from CFD or directly from the test data. The equivalent acoustic source is calculated using Simcenter 3D and introduced into the FE model. Once resolved, the sound field generated within the laptop and radiated from it can be analyzed. Simcenter 3D allows the acoustic engineer to understand how sound leaves the laptop, its direction, and also reflections from the immediate environment. Figure 2. FE mesh for acoustic simulation of a laptop: 2A the air around the laptop; 2B the internal mesh of the laptop geometry; 2C the laptop openings and 2D the entrance grille. Notebook OEMs need to understand the sound generated by the cooling architecture and investigate ways to minimize the impact on the user, such as directing noise away from the user through a rear-facing jack. Additionally, sound engineers can understand how the laptop screen shields some noise at various screen angles and user positions, as well as in the closed position if docked or connected to other displays. … and the much sought after “good” sound As mentioned in the introduction, the sound quality of a laptop is considered an indicator of the brand's high quality. Therefore, it is pertinent for the engineer to understand the behavior of the speaker and how it works in the laptop chassis. To optimize sound and maximize quality for the user, the engineer must start with the notebook's stand-alone speaker and work through subsequent integration into the notebook chassis to the notebook's behavior in a realistic user environment – ​​see Figure 3. Figure 3. Acoustic simulation steps to ensure the fidelity of results at the operational level when the product is in use. At the speaker level, as in all simulations, a geometry is defined from which the FE model is created and merged. This structural vibration model of the speaker is then coupled to a small volume of air near the speaker membrane. Specific acoustic radiation conditions are applied to the external surface to allow prediction of far-field sound radiation characteristics. Simplified 1D models based on the Thiele-Small model are used as inputs for the coil loads. These models contain all relevant electromagnetic coupling effects in the speaker driver, and their input parameters are easily obtained from the supplier (or from simple measurements). After solving the model, the sound radiation can be analyzed and post-processed to provide directivity data, impulse responses, and distortion data. Figure 4 provides a graphical representation of this typical workflow. Considering the performance of the speakers and the association with the perceived quality of the laptop, the engineer would be interested in quantifying the intensity of the acoustic source and the uniformity of the speaker's sound field. The image on the right of the video above visualizes the radiated sound waves. The next step is to understand how integrating the speaker into the laptop affects acoustic performance. In a laptop, the behavior of the speaker is strongly influenced by the coupling of the speaker membrane with the volume of air behind it and by the viscothermal effects that occur in the grilles that cover and protect the speakers from dirt and dust. The speaker model in the video is therefore extended to also include the back of the speaker membrane to model the interaction with the rear cavity inside the laptop and the effect of the grille and air volume between it and the speaker membrane, as shown in figure 4. Figure 4. Visualization of a possible laptop speaker configuration illustrating the air volume or rear cavity behind the speaker and speaker grille. The grid effect can be explicitly simulated by: Model the fluid in the holes and apply specific visco-thermal properties of the fluid or Using simplified equivalent transfer admission relations. Figure 5. Comparison of speaker performance before and after installation. Figure 5 illustrates the effect that installation conditions can have on speaker performance after you integrate it into your laptop. For the isolated speaker, the left image in Figure 6 shows that the speaker source intensity is uniform above one kilohertz. In comparison, the image on the right of Figure 6 illustrates a performance degradation above one kilohertz. There is very low sound radiation between the frequency range of four to six kilohertz and this is explained by the interaction between the speaker membrane and the resonance found in the rear cavity. This is a more realistic assessment of the performance of the speakers in the laptop and provides design engineers with valuable information to further optimize their product. The final step in the process is to evaluate how the laptop will perform in the intended user environment – ​​a typical office, for example. However, doing this with a finite element model would require significant time and computing power. An alternative method is to use Ray Acoustics, one of the advanced acoustic solvers available in Simcenter 3D. This technology is based on ray tracing, allowing us to effectively simulate the propagation of sound in wide spaces, over long distances and at high frequencies, much faster than finite element or boundary methodologies ever could. The model discretization and solution times are independent of frequency, making it perfect for solving problems where the geometry is larger than the acoustic wavelengths. Simcenter 3D provides frequency and time domain results as output from this solution. To simulate the office environment, three main modeling features are available in Simcenter 3D: Edge and surface diffraction – useful for typical dividing walls in an office environment Curvature effect correction – accurately captures discretized and mesh surfaces Absorption – surface and air absorption Particle tracking – takes into account late reverberations and diffuse reflection effects typically found indoors The lightning acoustic simulation model can directly calculate sound quality parameters such as reverberation times, clarity values ​​or sound transmissibility indices. Simcenter 3D can also directly incorporate binaural effects into the acoustic response without the need to model the human head – essentially obtaining the sound pressure levels that the listener's left and right ears experience. Figure 6. Typical office environment with visualization of sound propagation – individual ray paths are visualized and the binaural impulse response Figure 6 (left) illustrates a typical office environment with all reflective or absorbent surfaces discretized using a simulation mesh (gray surfaces). The microphone surfaces near the laptop and the subject's head are set to visualize the sound fields, as seen in the upper right corner of Figure 6. Ray tracing models provide information about how different speaker combinations perform. spread to the person's ear. A clear view of how much sound is radiated to the user and how much is being reflected from different surfaces can be unlocked using these ray tracing visualizations. Putting it all together – a cacophony or a symphony? All simulation steps discussed provide quantitative and visual information about the acoustic performance of the individual component through to product integration and incorporation into the real-world environment. However, despite everything, isn't it better to be able to hear the results of the simulations? Simcenter 3D Acoustics offers a sound processing and auralization tool that takes simulation results and combines them with measured sounds, like music, to create acoustic scenes you can hear! Only fan noise Fan noise + low-cost speaker Fan noise + high-end speaker The videos above allow you to test three different scenarios from a laptop: Fan simulation noise A piece of music played in the office environment using a low-cost speaker The same song played on a high-end speaker. In the first scenario, both tonal and broadband components of the noise are present – ​​auralization allows you to check how loud the sound sounds and how irritating or distracting it can be. In scenario two, one can investigate how some music can mask the sound – the fan noise is masked, but some low-frequency components of the music are missing. Scenario three employs a state-of-the-art speaker, providing much clearer and richer sound to the music. Simcenter 3D Acoustics allows you to understand the sound quality and noise of your electrical components. With these features, you can design around inherent noise and give the user of your products a more pleasant listening experience. Interested in optimizing the acoustic performance of your electronic products? Schedule a meeting with CAEXPERTS now and discover how our state-of-the-art acoustic simulation tools can help you predict, understand and improve the sound behavior of your devices quickly and reliably. Don't let noise get in the way of the perceived quality of your products - let's work together to ensure an exceptional listening experience for your users. Schedule your meeting today!

You don't need us to tell you that the planet is getting hotter. In our humble opinion, the greatest engineering challenge of our time is converting the systems that helped create the world as we know it into sustainable products. With Application Engineering for Combustion CFD, we help many customers across multiple industries simulate the burning of methane, propane, gasoline, diesel, and more. Of course, the goal is always to use simulation to design efficient combustion systems that minimize emissions and maximize performance. However, for these combustion systems, internal combustion engines, gas turbines and process burners, the heat is turned on to reduce their carbon footprint. Excellent design of combustion systems that burn fossil fuels can only get you so far. A major focus for engineers is now to adapt or design from scratch combustion systems that burn alternative fuels such as electronic fuels, hydrogen or ammonia. Clean energy The decarbonization of the automotive industry is what we imagine comes to most people's minds when considering the “cleaning” of combustion systems. There are many articles and columns discussing the long debate between internal combustion engines (ICEs) and battery-powered vehicles! We won't add anything here (except the fact that Simcenter STAR-CCM+ is the tool to design both). Ultimately, ICEs will be needed to advance industries where batteries are not viable and if they can function in a way that produces negligible CO₂ emissions, then they should be part of the mix in the future of transportation. Fortunately, Simcenter STAR-CCM+ has been adding tools to ensure that simulation can be leveraged to design such systems. And so, over the past few years, Simcenter STAR-CCM+ has been continually improved to enable the analysis of new fuels and emissions. Fuels proposed for use in In-Cylinder applications may have different fundamental reactive properties. An important example is Laminar Flame Speed ​​(LFS). In Simcenter STAR-CCM+ you can directly calculate the LFS for any fuel mixture and use it with Flamelet, ECFM and Complex Chemistry combustion models. Below we can see an example of an ICE using a mixture of Gasoline and Hydrogen as fuel, with the simulation being carried out using Simcenter STAR-CCM+ In-Cylinder Solution: The methodology allows a quantitative prediction of gross emissions depending on the hydrogen fraction: Exhaust emissions for different fuel mixtures at equal Qth. Naval applications Of course, the shipping industry is not immune to the need to burn cleanly. Maritime transport is responsible for around 2.5% of all greenhouse gas emissions, but it is essential as 90% of all global goods are transported this way. While there may be use cases where electrification is a viable approach, for large operators, batteries (alone) may never be the solution. Engineers designing marine propulsion systems are having to rock the boat with the use of conventional fuel and explore other (decarbonized) fuels during the design process. Ammonia and fuel cell engines can reduce carbon emissions One of these options is the use of Ammonia. Ammonia can be burned without generating CO₂ and is relatively easy to store, making it a good candidate as a future fuel for the shipping industry. Once the logistics (distribution, availability) of ammonia are resolved, it could be the number one topic for the development of internal combustion engines in the near future. However, despite the potential, ammonia has its own challenges, such as the need for high ignition energy. For this reason, ammonia can be used in conjunction with small amounts of diesel to start the combustion process. Ultimately, this still provides much cleaner combustion than traditional use of fossil fuels. Simcenter STAR-CCM+ can be used by designers to understand this process. In the example below, Ammonia and Diesel jets are studied at various injection times and angles. Under certain conditions, for example insufficient or too strong interaction between the two fuel sprays, misfires may occur. Being able to accurately simulate this reduces the need for extensive testing and allows for a detailed understanding of how to design to avoid such a scenario. Using the Complex Chemistry combustion model, excellent prediction of misfire occurrences as well as regular combustion can be predicted. How green is your simulation? Most industrial CFD simulations rely on High Performance Computing (HPC) and crunching the numbers requires energy that inevitably carries its own carbon footprint. This article from the British Computing Society highlights the challenge and states that CO₂ emissions from HPC data centers are expected to grow by 2 to 9 times over the next 10 years. The obvious course of action is to clear the energy that powers your CFD simulations, but of course this is probably not within your control and furthermore leaves us with nothing to do! With the latest versions of Simcenter STAR-CCM+, combustion engineers can now leverage GPU-native Flamelet combustion modeling. Leveraging GPU hardware allows engineers to run their simulations using significantly less power, reducing emissions and costs, as well as achieving faster responses (a rare win-win situation). So let's take a combustion system, in this case a pressurized annular combustor, and add some hydrogen to the fuel to reduce the CO₂ emissions generated. Great, but nothing in life is free, and we've talked in the past about how the simple act of adding hydrogen can disrupt the thermoacoustic stability of the combustion system. All is not lost as this system can be analyzed using high-fidelity LES combined with the Flamelet Generated Manifold combustion model to accurately predict thermoacoustic behavior. And even better, all of this can run natively on GPUs. The 50% reduction in computing time is evaluated here by comparing a 640-core CPU solution (commonly used CPUs with 32 cores per CPU node, 2.4-3.3 Ghz, 256 MB L3 cache) with a of GPU on 8 NVIDIA A100 cards. The end result is a solution that is 50% faster and consumes 60% less energy, in addition to being 42% cheaper. Even more importantly, the frequencies of thermoacoustic instabilities generated due to hydrogen addition are also excellently predicted and can therefore be eliminated or mitigated: Predicted and measured thermoacoustic spectra for a pressurized annular burner with CH4/H2 flame What we have here, folks, is a case of Simcenter STAR-CCM+-inspired carbon emissions creation : we save on emissions while designing to save on emissions! Interested in designing cleaner, more efficient combustion systems to reduce your carbon footprint? Schedule a meeting with CAEXPERTS today and find out how we can help you meet application engineering challenges for combustion CFD. With our experience and the advanced tools of Simcenter STAR-CCM+, we can explore sustainable solutions for your projects, from simulating alternative fuels to optimizing marine propulsion systems. Don't waste time, get in touch now!

Increase simulation speed, performance, and reliability. Enhancements to Solid Edge Simulation and Simcenter FLOEFD for Solid Edge increase simulation performance and speed with improvements to meshing, structural analysis, and interoperability. The new compact IGBT model makes it easy to incorporate electronics into your simulation studies, while the Cartesian Mesh Generator and improved mesh quality checks enable faster meshing and simulation workflows, resulting in a mesh speed of two to three times faster. Solid Edge Simulation Increased interoperability allows Solid Edge Simulation users to import solid temperature results from a Simcenter FLOEFD study into Solid Edge. A new command within Solid Edge Simulation's "External Loads" option, "Import Solid Temperature", is now supported, in addition to the previous commands "Import Fluid Pressure" and "Import Fluid Temperature". This command is only available with linear static studies and is useful for finding thermal stresses and strains in solids. The new option to apply force at any user-defined point allows greater control of simulation studies. The option has been added to the Force command bar, allowing users to apply forces to any 2D or 3D point in geometry, increasing capabilities alongside the traditional "Point" option that supported vertex selection. The visualization of mesh quality controls has been improved to facilitate the identification of areas of poor quality. Colors are now clearer and easier to decipher, and users can increase efficiency by being able to detect errors instantly. Users can also change the filled color of failed elements in the "Check Element Quality" dialog box for better visibility. Simcenter FLOEFD para Solid Edge Compact IGBT model and Cartesian mesh generator The Insulated Gate Bipolar Transistor (IGBT) compact model is now comprised of compact electrical element and two-resistor (2R) component models, providing an efficient representation of the IGBT element. The IGBT element can be described by specifying junction thermal resistances up or down. A set of IC plots for different temperature values ​​can also be defined for detailed analysis and characterization by users. The IGBT edit definition dialog box allows users to easily add the model to a specific analysis. The junction temperature is used to determine the Electrical Power from the IV graph, which is then applied to the model through the 2R component. The Cartesian mesh generator in Simcenter FLOEFD for Solid Edge ensures optimized performance, providing faster meshing while using fewer resources. With accelerated operations, including smart printed circuit board (PCB) importing and meshing, overall efficiency is improved with faster simulation and meshing workflows. Enhanced visualization capabilities and continuous updates after parameters are changed further enhance the overall workflow. Enhanced structural analysis Simcenter FLOEFD for Solid Edge can be connected to the Nastran Simcenter 3D (SC3D) Solver, unlocking the power of nonlinear analysis. The SC3D Nastran solver 401 introduces additional settings in the Calculation Control Options, allowing you to fine-tune simulations. Settings include adjusting scratch memory, refining contact mode, controlling solver steps, setting end time, and exploring large displacement scenarios. The enhancements also provide greater flexibility and control in nonlinear analysis and greater accuracy when working with large displacements. Tolerance-based contacts enable accurate simulations with enhanced contact options. Tolerances can be specified to create gapped body contacts, linear and angular tolerances can be defined, and glue contacts can be created seamlessly to ensure accurate and reliable simulation results. The User Defined Maximum Aspect Ratio in Local Mesh feature enhances the structural mesh with greater accuracy. By specifying maximum proportions for specific components, efficiency can be improved with greater control over the meshing process, especially for thin structures. Additional Enhancements With the powerful Component Explorer, users can manage component attributes, experience simplified organization, and employ efficient component management. Thermal component lists can be exported and imported, allowing easy editing of properties such as material, volume source value, two resistor power (2R), and light-emitting diode (LED) current. Users can simply export the list of components to Microsoft Excel, make the necessary changes, and then seamlessly import the list, simplifying their workflow. Teamcenter FLOEFD item customization offers greater flexibility and adaptability. Users can customize the Simcenter FLOEFD data model for Solid Edge to align with specific requirements and conveniently store FLOEFD data model files using local storage or Teamcenter storage. This customization can also seamlessly integrate FLOEFD into an existing data management system. Simcenter FLOEFD 's enhanced Linux solver for Solid Edge now supports co-simulation with Functional Mock-ups (FMUs) exported from other software, enabling integrated and comprehensive analysis. This solution ensures smooth compatibility and allows users to leverage all essential information stored within the FMU exported from FLOEFD, including model geometry. Increase the efficiency of your simulations right now! With enhancements to Solid Edge Simulation and Simcenter FLOEFD for Solid Edge, you can experience a significant increase in the speed, performance, and reliability of your analyses. Schedule a meeting with CAEXPERTS today and discover how we can optimize your simulations for guaranteed success.