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      <title>Transportation &amp; Mobility</title>
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      <description>Transportation &amp; Mobility</description>
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      <title>
      <![CDATA[ Praga Cars delivers the Bohema super sports car ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/industries/transportation-mobility/praga-cars-delivers-the-bohema-super-sports-car/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/274723</guid>
      <pubDate>Mon, 16 Dec 2024 13:43:46 GMT</pubDate>
      <description>
      <![CDATA[ Praga Cars have been manufacturing and developing high-performance sports and racing cars for private and professional racers for many years. The introduction of Praga Bohema represents a strategic step towards establishing Praga Cars in the world of super sports cars.
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      <![CDATA[ 
The new Praga Bohema sports car is the work of the Czechoslovak car manufacturer Praga Cars, a company with a rich history in the automotive industry dating back to 1907. Praga Bohema combines the brand&#8217;s historical legacy with modern technologies and innovations. The development of this supercar was driven by the desire to offer customers a unique combination of performance, luxury, and exclusivity. Bohema was designed to create a car that is not only fast and agile on the road or racetrack but also provides exceptional comfort and an adrenaline-pumping driving experience.







Chief Design Engineer Mr. Ondřej Chotovinský has extensive experience in the automotive industry. Throughout his career, he has participated in various projects for companies such as Ferrari, McLaren, Aston Martin, Gordon Murray, Honda, and London Taxi Company. On his journey, he’s the most grateful for the continuous opportunity to meet experts, innovators, and creatives with rich experience and unconventional ideas in the automotive industry. &#8220;I lead a team of passionate engineers responsible for a wide range of projects, from the conceptual phase through prototype production and testing to final production. Our engineering team closely collaborates with other departments, suppliers, and external specialists to integrate advanced technologies, materials, and innovative processes to ensure our solutions meet industry standards, fulfill high-quality requirements, and add value for our customers.&#8221;



Streamlining design and manufacturing



Praga Cars faced the challenge of unifying the various CAD systems the engineers were accustomed to using. Data work wasn’t yet fully optimized. Given the Praga Bohema project&#8217;s complexity, it was crucial to switch to a unified PLM/CAD system to streamline future development.



The transition to a new system usually requires time, training, and adaptation to new workflows. For CATIA on the 3DEXPERIENCE platform users, this transition is relatively smooth. However, for others accustomed to different CAD tools or those who have never worked with a PLM system, a new environment can initially be stressful. This change disrupts established workflows, potentially leading to a temporary decrease in productivity. To successfully overcome these challenges, we chose a strategy of gradual system implementation. This approach allowed us to handle gradual data integration better and minimize the risk of disrupting development.



&#8220;I first started working with the 3DEXPERIENCE platform on a project in Maranello in 2019. I was part of a small team where Ferrari wanted to test and evaluate the benefits of implementing the 3DEXPERIENCE platform into the development phase of a sports car. In addition to a more visually modern and attractive environment, we, as engineers, appreciated the improved user interface and straightforward integration with the PLM system. During the prototype phase, this solution significantly facilitated data navigation, saving precious time,&#8221; adds Chotovinský.




&#8220;After four years, we can say that the 3DEXPERIENCE platform has brought many quantitative advantages. The most significant include a 10-30% increase in engineer productivity and a 20-30% reduction in the conceptual cycle.&#8221; 



Ondřej Chotovinský, Chief Design Engineer, Praga Cars




The development department at Praga Cars has been using the 3DEXPERIENCE platform for four years now. The main reason for its implementation was to streamline collaboration and communication among engineers during the development of the Bohema supercar. This is crucial for startups, which often work in small teams and need quick and seamless information sharing. At the same time, it was necessary to set up basic methodologies for data and process consistency and integrity throughout the development.



For larger companies, implementing and using the 3DEXPERIENCE platform can be complicated due to the range of functionalities and organizational needs. This requires careful planning, user training, and adapting integration processes. &#8220;For our smaller team, the key challenge was retraining the engineers who initially used a wide range of CAD tools to a unified PLM system and adopting the platform as an efficient tool for future development. We overcame these challenges through a strategic approach and collaboration with TECHNODAT, which helped us successfully deploy the 3DEXPERIENCE platform and maximize its value.&#8221;



Today, they manage all development and production data in a single system. This has eliminated unnecessary administrative tasks, improved internal collaboration, and enhanced cooperation with suppliers, pushing the project toward successful implementation. 



What&#8217;s next for Praga Cars?



This year, Praga Cars aims to deliver several vehicles to its customers. Therefore, most of their efforts focus on final testing and the production phase. They need to set up manufacturing processes to meet deadlines and smoothly cover demand. &#8220;Once we manage to meet the expectations of our first customers, demand will increase, and we will start to see a return on our investments, allowing us to smoothly proceed to further steps and also transfer all of our experience into future projects in Praga.,&#8221; adds Chotovinský, reflecting on the future of Praga Cars.



This is a guest post from trusted Dassault Systèmes business partner, TECHNODAT.








Learn more about Dassault Systèmes Transportation &amp; Mobility Solutions





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      <title>
      <![CDATA[ Optimizing Battery Range and Thermal Comfort in Electric Vehicles ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/brands/simulia/optimizing-battery-range-thermal-comfort-electric-vehicles/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/274518</guid>
      <pubDate>Thu, 05 Dec 2024 08:00:00 GMT</pubDate>
      <description>
      <![CDATA[ This blog explores how Dassault Systèmes employs cutting-edge simulation technologies and advanced computational tools such as Computational Fluid Dynamics (CFD) and 1D system modeling to accurately simulate and optimize these complex interactions and thereby improving both battery efficiency and cabin comfort in EVs
 ]]>
      </description>
      <content:encoded>
      <![CDATA[ 
Introduction



The electric vehicle (EV) industry is making great strides towards sustainable mobility, pushing the boundaries of what’s possible with clean energy. Although EV sales currently account for around 16% of the global market, they’re expected to rise sharply to 50% by the end of 2035 (see figure 1) as manufacturers work to overcome significant challenges like manufacturing challenges such as material availability for battery and electric components, as well as availability of charging infrastructure, to vehicle thermal challenges such as battery range and thermal comfort, cost of EVs to be on par with ICE vehicles, model proliferation, multi-physics engineering development, range anxiety etc. Among the most pressing of these are battery efficiency and occupant comfort, particularly under varying climatic conditions.



Figure 1: Global EV sales forecastSource: https://ev-volumes.com/news/ev/evs-forecast-to-account-for-two-thirds-of-global-light-vehicle-sales-in-2035 







In colder climates, for example, EVs lack the waste heat from internal combustion engines that would otherwise help heat the cabin. This leads to increased power consumption by climate systems, which in turn reduces battery range. This blog explores how Dassault Systèmes employs cutting-edge simulation technologies and advanced computational tools such as Computational Fluid Dynamics (CFD) and 1D system modeling to accurately simulate and optimize these complex interactions and thereby improving both battery efficiency and cabin comfort in EVs.



Motivation: Why Thermal Management is Key for Electric Vehicles



Thermal management directly impacts an EV’s performance, as it influences both the efficiency of the battery and the comfort of passengers. In comparison to ICE vehicles, EVs must expend additional energy to heat the cabin, which can reduce range by more than 15% in hot or cold conditions and the range can drop by up to 40% in extreme weather conditions (see Figure 2).



Figure 2: Winter range for popular EV modelsSource: recurrentauto.com  







Thermal comfort is particularly relevant to drivers in extreme climates who rely on climate control features like seat warmers, cabin heaters, and defrosters. These features consume a significant portion of the EV&#8217;s battery power, creating a trade-off between energy used for passenger comfort and energy available for range.



Challenges in the EV Thermal Management Landscape



Real-world applications come with their own set of challenges. Extreme temperatures, complex thermal interactions, and fluctuating driving conditions all impact energy consumption in ways that require ongoing refinement of EV designs. For instance, urban stop-and-go traffic cycles and high-speed highway driving affect airflow and cooling needs differently, meaning that a single HVAC configuration is unlikely to suit every scenario.



Beyond comfort, EV designers also face the task of balancing battery health and efficiency. Batteries perform optimally within a specific temperature range. Excessive heating or cooling can accelerate degradation, affecting long-term performance and lifespan. Advanced simulations like those used in this study help identify the optimal trade-offs between battery protection and energy efficiency.



The Role of Advanced Simulation: To tackle this challenge, Dassault Systèmes has leveraged a co-simulation approach that integrates detailed 3D CFD models with system-level models. This approach allows engineers to simulate how heat moves through the vehicle and how energy is used by the climate system, helping to optimize both comfort and efficiency.



Methodology: A Multiscale Approach for Better Range and Comfort



A study conducted by Tesla shows that in electric vehicles, optimizing one of the components individually results in 15-25% improvement in e-drive efficiency, whereas optimizing the overall system as a whole improves the e-drive efficiency by 40%. Range is a problem, which should not be looked at through a single component in the system, but as a vehicle overall.



An advanced virtual twin can effectively assist in determining the optimal size of the HVAC system while enabling early-stage predictions for battery life, vehicle range, and passenger comfort. This study utilizes a co-simulation approach combining system-level and 3D CFD models i.e., by merging the results from 3D thermal optimization into overall system to optimize the efficiency to capture complex, multi-scale interactions and enhance overall vehicle performance. Through the integration of 3D CFD with Finite Element Analysis (FEA) thermal models, this approach provides precise predictions of passenger comfort levels and battery temperature distribution. Furthermore, by incorporating Dymola system behavior models, real-world driving scenarios for all vehicle systems can be simulated. 1D system simulation is performed with Dymola and the heat exchanger inlet temperatures are determined and provided as input to the 3D CFD simulation. As a next step, 3D CFD simulation is performed with PowerFLOW / PowerTHERM and the cabin outlet temperature is provided back to the 1D system model as input and the cycle repeats (see Figure 3). This approach benefits from the detailed response trends provided by 3D CFD analysis, facilitating quick adjustments via 1D models to optimize vehicle performance.



Figure 3: Coupled 3D CFD – 1D system Model simulation process  







Additional 3D CFD analyses assess Underhood airflow, allowing engineers to characterize how external air affects the HVAC heat exchangers under various conditions. This airflow data is then applied to the 1D HVAC system analysis to establish realistic boundary conditions.



Figure 4: Coupled 3D CFD – 1D system Model simulation  







System Model



The first step is creating a 1D model of the HVAC system using Dymola, which assesses energy consumption, cabin temperatures, and vehicle range under different drive cycles, vehicle aero changes and HVAC modes. This model helps determine the size and power needs of the HVAC system to balance thermal comfort with battery efficiency. Dymola Modelica library offers a wide variety of predefined models and libraries that make our life easier in modelling the system models, like…




Realistic drive cycles for various speeds (for e.g., high and low WLTP drive cycles)



Battery library to model range, ageing, time to charge and cooling



HVAC library to model driving modes, flow rates and thermal exchange



Driveline and Chassis to model aerodynamic properties of vehicle




Figure 5: 1D system Model of an EV  







3D CFD for Localized Thermal Comfort



Using CFD simulations, one can analyze how heat moves through the cabin, affecting each passenger’s thermal sensation alongside the temperature of electronic components like display monitors, mobile rest etc. This level of detail provides a realistic view of representation of different modes for different climatic conditions and comfort across different body areas and helps inform design decisions for optimal airflow and heating distribution.



Figure 6: 3D CFD Cabin simulation of an EV  







Underhood Cooling Airflow Analysis



A separate 3D CFD model simulates airflow around the vehicle’s heat exchangers, which are part of the HVAC system. This analysis captures how external air impacts cooling and heating, allowing for realistic boundary conditions and ensuring that all HVAC components operate efficiently.



The initial step involves using detailed 3D CFD analysis to characterize the external airflow that reaches the front end of the HVAC system&#8217;s heat exchangers under various vehicle operating conditions. A drive-cycle-specific Design of Experiments (DoE) approach is applied to select sample points that comprehensively represent these conditions based on their frequency of occurrence. This process, illustrated in Figure 7, defines the entire approach for characterizing airflow beneath the hood.



Figure 7: Underhood cooling airflow characterization 







Next, CFD simulations are conducted for each sample point to determine the heat exchanger’s inlet mass flow rate and back pressure. A response surface model is then constructed using 2D linear interpolation to estimate values between these sample points. This model provides realistic boundary conditions for the 1D HVAC system analysis, allowing further simulations with accurate inputs for the heat exchangers.



Real-World Application: Analyzing Drive Cycles and Compressor speeds for Optimal Efficiency and Comfort



A transient coupled 1D-3D simulation was conducted on a compact passenger EV. This vehicle features a battery pack of 384 prismatic cells with a total capacity of 15.6 kWh. The HVAC system is designed to cool the cabin in summer and serve as a heat pump during winter. For this analysis, the focus was on a cold-weather scenario, simulating a drive in -10°C ambient temperatures for 30 minutes physical time, driver and passenger human comfort model and 60% face, 40% foot HVAC flow split mode.



The Worldwide Harmonized Light Vehicles Test Procedure (WLTP) drive cycles were used, as it reflects typical urban and highway driving patterns globally. The simulations explored two different drive cycle speeds, a regular drive cycle and an additional low-speed WLTP cycle to represent city driving, allows for realistic evaluation of the HVAC system under variable conditions. Three different compressor speeds high, medium and low were analyzed in the study, with particular attention on the HVAC system’s energy consumption, as it directly impacts passenger comfort. Additionally, the expected range for each configuration was estimated by analyzing the battery’s state of charge at both the start and end of the drive cycle.



Figure 8: Regular and low-speed WLTP drive cycles  







Results



The thermal comfort levels achieved are comparable across all three scenarios. After 10 minutes, occupants feel comfortable in each case. The high compressor speed scenario reaches comfort level 0 in 4.5 minutes, the medium speed in 6 minutes, and the low speed in 6.5 minutes. After 10 minutes, the low-power scenario offers the same level of comfort as the high-power scenario while consuming 50% less energy. Further analysis indicates that overall comfort is primarily influenced by the comfort level of the body&#8217;s breathing sensation. This breathing sensation is less affected by design variations among the three speeds compared to other body parts, resulting in a smaller-than-expected overall change in comfort.



Figure 9: (a) Overall comfort for different compressor speeds







Medium and high compressor speeds consume 33.5% and 50.2% more energy compared to low speed. Moreover, at low compressor speeds, the battery drained more slowly, providing a range increase of up to 21% under some drive conditions. This test showed that reducing HVAC power can significantly enhance range without sacrificing passenger comfort.



Figure 10: (a) Energy savings (b) Range difference








High Speed: Achieves maximum comfort quickly but consumes more energy.



Medium Speed: Balances comfort and energy efficiency.



Low Speed: Consumes the least energy, with only a slight delay in achieving optimal comfort.




Conclusion: Paving the Way for Smarter EV Design



As electric vehicles become more prominent, tools like co-simulation are essential in crafting a user-friendly and competitive product. By blending 3D CFD with 1D system modeling, manufacturers can optimize a vehicle’s thermal performance and ensure it meets consumer expectations for both range and comfort. Together, these simulations allow engineers to fine-tune vehicle parameters, such as compressor speed, in response to different driving cycles. For instance, reducing the compressor speed in the HVAC system can lead to significant energy savings without greatly impacting comfort levels.



This holistic approach promises a range of benefits:




Improved Range: Strategic HVAC adjustments can preserve battery life and extend driving range, reducing range anxiety.



Enhanced Comfort: Precision simulations ensure that passengers enjoy a comfortable cabin experience, even in extreme climates.



Energy Efficiency: Optimized HVAC systems lead to lower power consumption, helping to meet energy targets.




As simulation technology advances, EV designers are increasingly able to predict real-world outcomes in a virtual environment. By adopting these methods, the automotive industry is poised to make electric vehicles not only a viable alternative to traditional cars but a preferred choice for eco-conscious drivers worldwide. The workflow presented here also exemplifies Dassault Systèmes’ approach to MODSIM, integrated modeling and simulation and supports the company’s long-term strategy to bridge the gap between designers and simulation engineers, ultimately speeding up the product development process.











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <title>
      <![CDATA[ Build a Better Battery Cell with Simulation-driven Engineering ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/brands/simulia/build-better-battery-cell-simulation-driven-engineering/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/274102</guid>
      <pubDate>Tue, 26 Nov 2024 16:28:56 GMT</pubDate>
      <description>
      <![CDATA[ Simulation helps engineers to enhance battery cell design and develop new cell technology. In this blog post, we will introduce the Battery Cell Engineering workflows from SIMULIA on the 3DEXPERIENCE® platform and demonstrate how they can be utilized to create high-performance battery systems.
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      </description>
      <content:encoded>
      <![CDATA[ 
Introduction



Batteries are becoming increasingly important in our daily lives, from smartphones to electric cars to large-scale power grid storage. As electrification becomes more widespread, batteries with higher capacity, lower cost and weight, longer lifespans, and the ability to meet strict operating conditions and safety standards will be needed. Companies that can meet these requirements will have a significant competitive advantage.



The process of developing an improved battery begins at the cell level. Cells are the fundamental units of batteries, comprising electrodes and electrolytes. A battery pack is formed by connecting multiple cells, often with added structural, thermal and control elements.



Simulation helps engineers to enhance battery cell design and develop new cell technology. In this blog post, we will introduce the Battery Cell Engineering workflows from SIMULIA on the 3DEXPERIENCE® platform and demonstrate how they can be utilized to create high-performance battery systems.



Challenges of Battery Engineering











When developing a battery system, engineers must consider many competing design requirements. The following examples are from electric vehicles, but other industries have similar needs:




Capacity (driving range): The battery should have the maximum possible capacity to minimize the frequency of recharging and extend the overall lifespan of the device.



Charge time: The faster the battery charges, the sooner a driver can return to the road.



Weight: A lighter battery leads to quicker acceleration and improved energy efficiency.



Longevity: The car battery is a costly component. A longer lifespan reduces maintenance costs and increases resale value.



Temperature: Charging and discharging produce significant heat inside the battery, requiring it to be cooled in hot weather and warmed in cold weather.



Safety: The battery must withstand the stresses and vibrations of use and remain safe even in a crash.




To achieve all these design goals and find the best trade-offs, engineers need to understand not only how the cell behaves in the lab but also how it will perform during real operating conditions



Recent advancements in the battery industry have also made development more challenging. Suppliers and manufacturers create a more intricate supply chain as the industry expands. Developing battery cells, manufacturing at scale and integrating them into vehicles or devices can involve numerous players, each operating at different levels of detail, from the molecular to the system. Established cell manufacturers face competition from startups and joint ventures with other industries, such as automotive and energy, are increasingly common. Cell manufacturers are exploring new technologies such as solid-state electrolytes and sodium ion cells.



Why Simulate Battery Cells?











Test on a Virtual Twin Without a Prototype



Simulation enables engineers to meet these challenges. With simulation, engineers can analyze battery performance without a physical prototype using a virtual twin. This digital representation of the battery includes all the relevant data—such as geometry, electrode and electrolyte properties and their interactions—needed to represent its real-world behavior accurately.



Virtual twins need to capture the complex geometry of battery cells, such as layered cylindrical (“jellyroll”) designs. The 3DEXPERIENCE Battery Cell Engineering solutions help design the layered 3D battery cell geometry and convert them into detailed, realistic simulation-ready models. After simulation, every aspect of the cell, such as temperature distribution or ion concentration, can be visualized in 3D.



The virtual twin can be analyzed at any stage of development, from very early in the design phase to before constructing a physical prototype. This allows for comparing different concepts and optimizing design parameters to ensure that the design will meet the requirements before committing to a specific design. The risk of potential failure, costly rework, and project delays are minimal.



Optimize Electrochemistry for Efficient Performance











The performance of a battery in charging, storage and discharging is determined by its electrochemistry. This complex multiphysics, multiphase phenomenon is determined by the 3D structure of the cell and interplay between the electrode and the electrolyte. Analyzing these with testing is time-consuming and measurement limitations inherently constrain the insight provided.



The Battery Cell Engineering solution provides an extended, 3D porous electrode theory (PET) based on the Newman model to simulate the cell’s performance in real-world situations. This models the electrochemistry within the cell, considering both micro-scale and macro-scale details. &nbsp;The different aspects of physics – structural, thermal, electrochemical &nbsp;and pore pressure – are considered together. Engineers can analyze factors such as charge/discharge behavior at different charge rates under different mechanical and thermal conditions. As the simulation is in 3D, users can also assess and predict three-dimensional behaviors such as thickness deformation and stress caused by swelling.



Ensure Safety in Real-world Scenarios











Battery cells are designed to store high energy densities in a portable way, such as inside a smartphone or an electric car. As a result, they are exposed to many difficult and dangerous scenarios—extremes of heat and cold, bending, impact and penetration. Battery cells need to withstand these hazards—if they fail, they should fail safely.



Simulation can safely replicate dangerous real-world scenarios within a virtual environment. Events such as nail penetration, car crash or thermal runaway can be studied without the cost and risk of constructing and destroying a physical prototype.



Make Batteries a Better Investment with Longer Lifespan and Reliability



Batteries age over time (calendric aging) and through repeated use (cyclic aging). Calendric aging occurs, for example, when a battery is stored out of use, while cyclic aging takes place each time the battery is charged or discharged. The expense of the battery significantly influences the cost of an electric vehicle and one of the primary causes for the rapid depreciation and increased cost of ownership of electric cars is battery aging. Electric vehicles will become a more attractive investment for drivers and fleet managers if battery cells can last longer.



The Battery Cell Engineering solutions on the 3DEXPERIENCE platform provide comprehensive workflows to simulate these aging processes. It can model various battery aging mechanisms, such as formation &amp; growth of the SEI, lithium plating, and dissolution of the cathode. By analyzing these effects, engineers can optimize the battery&#8217;s lifespan and produce more reliable batteries that customers demand.



Explore Battery Science Down to Cell Chemistry Optimization 



Battery Cell Engineering on the 3DEXPERIENCE platform combines SIMULIA multi-physics simulation workflows with key capabilities from BIOVIA for scientific chemical and material engineering and CATIA for design and modeling. Together, these support battery engineers in designing, analyzing, optimizing and validating battery cells using 3D virtual twins.



All process stages take place in the same environment: the 3DEXPERIENCE platform, which provides a single source of truth for all the battery cell engineering data. Designers, analysts and other stakeholders can share information and collaborate reliably and securely. Unified modeling and simulation (MODSIM) helps to left-shift the analysis process so that cell designs can be optimized earlier and potentially identified and resolved, ensuring a more consistent, error-free and accelerated design cycle.



The highly detailed 3D Newman modeling in the Battery Cell Engineering tools on the 3DEXPERIENCE platform is the key to creating realistic simulations of the cell’s thermal and electrochemical behavior. These simulations provide the highest quality predictions about the cell&#8217;s performance and age, including any impacts from its use in different conditions. Microstructural simulations enable deep analysis of material characteristics within the electrodes. Meanwhile, mechanical simulation is used to test the cell’s behavior in events such as thermal stresses, mechanical indentation or nail penetration so that engineers can design for optimal safety throughout the cell’s lifespan.



Conclusion



From smartwatches and phones to electric cars and grid storage, battery performance is crucial to the success of devices large and small. This performance is determined at the battery cell level by the electrochemistry and multi-physical interactions within. Developing an efficient, safe and competitive battery requires understanding the cell&#8217;s complex three-dimensional behavior.



Dassault Systèmes provides a full Battery Cell Engineering solution on the 3DEXPERIENCE platform. This solution integrates the best design and simulation solutions into a workflow. Using these tools, battery designers can analyze battery performance accurately from the comfort of their desks without having to build physical prototypes.



With the Battery Cell Engineering solutions on the 3DEXPERIENCE platform, battery cell manufacturers can enable collaboration between all stakeholders and left-shift analysis in the development cycle. Potential safety and efficiency problems can be resolved early without extensive re-designs that cause delays and cost overruns. With simulation, battery cell manufacturers can develop innovative and competitive new products while cutting R&amp;D costs and time-to-market.



For more information, see the on-demand webinars:



https://events.3ds.com/battery-cell-engineering-faster-modsimhttps://events.3ds.com/future-aircraft-development-modsim











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <![CDATA[ Workflow: Modeling and Simulation of a Tower Crane ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/simulia/workflow-modeling-simulation-tower-crane/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/273797</guid>
      <pubDate>Fri, 22 Nov 2024 17:34:03 GMT</pubDate>
      <description>
      <![CDATA[ This blog post shows how multiple Dassault Systèmes applications can be combined to create a full workflow for Tower Crane Simulation.  
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      </description>
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      <![CDATA[ 








Introduction



A tower crane is a modern form of a balance crane with the same basic parts. Fixed to the ground on a concrete slab (and sometimes attached to the sides of structures), tower cranes often give the best height and lifting capacity and are used to construct tall buildings.



The conditions under which the crane operates determine its general dimensions very largely. The height is fixed by the height of the tower it is constructed and the radius at which the maximum load is to be lifted, as determined by practical considerations and the construction site.



During the design of these cranes, static loads are generally considered based on utility requirements/load-carrying capacity, but forces and torques on components resulting from motion simulations can also serve as useful input for detailed structural analysis.



Below is a concise overview of a tower crane workflow, highlighting the tools and processes used.



Workflow Overview



1. 3D Modeling:



&nbsp; &nbsp;&#8211; We created detailed 3D models of each component using&nbsp;3DExperience CATIA:



&nbsp; &nbsp; &nbsp;&#8211;&nbsp;Part Design App&nbsp;for individual components.



&nbsp; &nbsp; &nbsp;&#8211;&nbsp;Assembly Design/Mechanical Systems Design App&nbsp;for assembling the crane.



2. Dynamic Simulation:



&nbsp; &nbsp;&#8211; Using the assembled model, we employed the&nbsp;Motion Analysis App&nbsp;to create mechanisms and perform dynamic simulations, analyzing the crane&#8217;s movement and interactions.



3. Structural Analysis:



&nbsp; &nbsp;&#8211; To analyze the structural integrity of the hoist:



&nbsp; &nbsp; &nbsp;&#8211; We used the&nbsp;‘transfer loads’&nbsp;command from&nbsp;Motion Analysis&nbsp;to generate a simulation object that includes load data.



&nbsp; &nbsp; &nbsp;&#8211; This object is compatible with structural analysis apps like&nbsp;Structural Model Creation&nbsp;and&nbsp;Mechanical Scenario Creation, where we finalized couplings and computed structural simulations.







4. Cable Modeling:



&nbsp; &nbsp;&#8211; Since the Motion Analysis app currently lacks the capability to simulate cables:



&nbsp; &nbsp; &nbsp;&#8211; We utilized&nbsp;Simpack&nbsp;to model the cable elements.



&nbsp; &nbsp; &nbsp;&#8211; We exported our assembly from 3DExperience to Simpack using the connector, which converts the model into a format compatible with Simpack, complete with mass and inertia data.



5. Enhancing the Simpack Model:



&nbsp; &nbsp;&#8211; In Simpack, we extended the model to include:



&nbsp; &nbsp; &nbsp;&#8211; Pulleys for cable support.



&nbsp; &nbsp; &nbsp;&#8211; Connections and excitations to represent motors.



&nbsp; &nbsp; &nbsp;&#8211; The cable element itself.



&nbsp; &nbsp;&#8211; This allowed us to simulate the full behavior of the Tower Crane, including cable dynamics. We reviewed results and animations within Simpack to analyze crane performance.



To view the full workflow, visit the SIMULIA Community post, which includes a PowerPoint presentation and a video that provide a more detailed explanation of the workflow and findings.&nbsp;











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <title>
      <![CDATA[ Electric scooter brand achieves phenomenal growth with solid technical foundation ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/industries/transportation-mobility/electric-scooter-brand-achieves-phenomenal-growth-with-solid-technical-foundation/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/272233</guid>
      <pubDate>Fri, 08 Nov 2024 06:00:00 GMT</pubDate>
      <description>
      <![CDATA[ Indian startup River Mobility takes product from ideation to creation in 20 months thanks to technology from Dassault Systèmes.
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      <![CDATA[ 
In 2021, India-based startup River Mobility developed Indie – a stylish and practical electric scooter designed to meet the specific needs of people who live and work in India’s thriving cities.



“Indie is inspired by the modern Indian who is always on the move, who wants to make the most of every day,” said Vipin George, co-founder and chief product officer at River Mobility, in a press release. “The unique combination of design elements and technical specs give Indie a distinct character, something that inspires you to do more and be more.”



Since Indie was unveiled, River Mobility has never looked back. The company – which grew from two to 84 employees in the space of a year – has raised US$68 million from its global investors who include Japanese trade company Mitsui &amp; Co. as well as global automotive firm Yamaha Motors.



“We are highly impressed with River’s potential in the Indian two-wheeler market,” said Daishi Mochizuki, general manager of Personal Mobility at Mitsui &amp; Co. in a press release. “Their agility and efficiency of the development process from concept to commercialization of the product is commendable. Indie in India is just the beginning, we believe River will become a global brand and are pleased to be a part of their journey.”



With this backing, River Mobility has set up a 120,000 square foot research and development facility with a production capacity of 100,000 units per year and has plans to open an additional manufacturing facility with a capacity of 500,000 units per year. The company has also opened its first store in Bengaluru, which was quickly followed by expansion across India. By the end of 2024, the company plans to have a presence in over 50 cities in the country and – according to reports by the Business Standard – it expects to open 100 stores by as soon as March 2026.



How does a company grow so rapidly and with such success? One contributing factor is River Mobility’s adoption of Dassault Systèmes software, which has not only enabled it to take its product from ideation to creation in just 20 months. Thanks to the 3DEXPERIENCE platform’s scalability and ease of use, it has also facilitated the rapid expansion of River Mobility’s team.







Read the full customer story and watch the motion graphics video to discover how River Mobility has embraced Dassault Systèmes’ cloud technology throughout the depth and breadth of its business. Find out how it is leveraging CATIA’s multi-CAD design functionality to create critical components quickly and easily and uncover how it is improving both internal and external collaboration. You can also discover the company’s plans for the future and find out why it believes that Dassault Systèmes technology will enable it to grow faster than other emerging competitors.
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      <![CDATA[ General Motors Puts the Spring in Suspension with Simpack ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/simulia/general-motors-puts-spring-suspension-simpack/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/272092</guid>
      <pubDate>Tue, 05 Nov 2024 14:35:11 GMT</pubDate>
      <description>
      <![CDATA[ American multinational automotive manufacturer General Motors relies on SIMULIA to model complex multibody dynamics for leaf spring suspension systems.
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      <![CDATA[ 
Challenge



Find a more efficient, accurate way to model the complex configurations and multibody dynamics of leaf spring vehicle suspension systems. Traditional approaches were intricate, challenging and resource-intensive, making it difficult to model tailor-made suspension for different vehicle programs.



Solution



Create a multibody dynamics modeling tool using the SIMULIA Simpack solution, with support from the Dassault Systèmes SIMULIA team.



Results




Increased efficiency – simulations that used to take a day can now be done within an hour



Fast, accurate simulation results delivered by Simpack’s robust solver



Confidence in supporting future vehicle innovation








Sometimes old ideas are the best ones, but that doesn’t mean it’s simple to apply them to new designs. Take the leaf spring, for example. It’s one of the oldest forms of vehicle suspension, dating back to the horse-drawn carriages of 17th-century France, and it’s still in use today.



“Leaf spring is a super-old technology, but it is very cheap to make and provides a really good suspension system for vehicles,” said Ameya Apte, vehicle dynamics and load simulation engineer at General Motors (GM).



Essentially, leaf springs consist of long, flat steel plates that are curved into an arc and stacked together in a pack. A second and even a third pack might be added, each containing different shapes and quantities of leaves. That opens the door to endless configuration possibilities.



“Different leaf spring configurations provide different suspension rates for a vehicle,” Apte explained. “A smaller vehicle would need a smaller leaf pack with fewer leafs, while a larger vehicle would generally need a larger leaf pack with more leafs. &nbsp;Basically, the amount of load that the rear axle carries is what primarily affects the leaf design. It depends on what kind of ride and what kind of handling you want from the vehicle.”



Simulation is essential to fully analyze the different ways the leaves of each spring interact with each other.











“If you’re looking at the stresses inside a leaf, it’s practically impossible to do that in a test, but you can do it in simulation,” Apte said. “Similarly, if you want to measure the force between two leaves you can put in a load transducer, but that also modifies the model itself.”



But from a simulation modeling perspective, factoring in those multibody dynamics was a complex and resource-intensive task. As a result, modeling tailor-made leaf springs for each vehicle program could be time-consuming, error-prone and difficult to standardize as a process.



“Leaf springs have been around for hundreds of years, so people might think it&#8217;s a simple technology to model, but the opposite is true,” Apte said. “One challenge with leaf springs is that the leaves themselves articulate with the suspension, so there is friction between them that needs to be modeled. In addition, the bushings attached between the chassis and the leaf springs also have frequency-dependent properties that must be modeled.”



Carmakers typically use finite element analysis solutions to model and simulate the complex geometries, materials and physics involved in vehicle design. But for GM’s leaf springs, Apte wanted a multibody dynamics tool that would support the more efficient modeling of these complex systems. He and his team used SIMULIA technology to create the tool they needed.



“The tool that we developed allows us to make leaf springs that are standardized and tailor-made for different applications,” Apte said. “Users simply type in the parameters they want for the spring – such as the number of leaves in a pack, how they are distributed across the primary, secondary and auxiliary pack, their shapes, tapering and so on. With that information, the tool will create a model that can be used to predict vehicle dynamics performance and loads.”



Two key ingredients contributed to the tool’s success. One was the comprehensive support provided by SIMULIA’s technical, back-end and R&amp;D teams. The other was SIMULIA Simpack – a technology for developing and simulating high-fidelity multibody systems.



Apte was particularly impressed with Simpack’s solver, which was miles ahead of other tools he had worked with.











“With Simpack, you practically do not have to modify solver parameters to get a model to run,” Apte said. “You might modify parameters to increase or reduce run times or get better fidelity in some part of the simulation, but you generally don’t need to do it to run a model. That robust solver is very useful because it allows us to look at the results, understand the physics happening behind them and then send the model out to our design engineers.”



As well as supporting more efficient leaf spring simulation, the tool also delivers accurate results, so Apte and his team can simulate with confidence. “If a simulation for leaf springs takes a day to do with FEA tools, with Simpack it can be done within an hour,” Apte said.



Looking ahead, Apte believes the solver he has in this tool will stand the test of time, even as the industry continues to evolve and new technologies emerge.



“I think the physics solvers that we have now will be supercharged, rather than eradicated, by disruptive technologies like artificial intelligence and blockchain,” Apte said. “The time it take to get a new vehicle to market has reduced significantly over the decades, and we see a potential to reduce it further with new tools. In the long term, learning these new tools and imagining a future where we can combine them to create the right applications will be a huge part of our job.”











Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts? The SIMULIA Community is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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      <title>
      <![CDATA[ Transportation and Mobility Are Transforming ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/transportation-mobility/transportation-and-mobility-transforming/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270754</guid>
      <pubDate>Mon, 28 Oct 2024 04:46:00 GMT</pubDate>
      <description>
      <![CDATA[ Explore T&M trends reshaping OEMs and suppliers
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      <![CDATA[ 
The transportation and mobility (T&amp;M) sector is evolving rapidly, driven by rising vehicle complexity, technological advancements, and shifting consumer demands. Important trends—such as electrification, the growing role of software in vehicle design and functionality, and changing business models—present startups and SMBs in the T&amp;M industry with both exciting opportunities and significant challenges. 



This article explores those opportunities and challenges and highlights the technological capabilities that smaller original equipment manufacturers (OEMs) and suppliers need to navigate the changing T&amp;M industry landscape.



Electric Vehicles and Alternative Fuels



The most significant trend in the T&amp;M industry is the shift from traditional internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs), which run entirely on rechargeable batteries, and hybrid electric vehicles (HEVs), which run on a combination of battery power and traditional fuels. Consumer demand for more sustainable vehicles and government and industry policy targets are driving OEMs to develop new batteries that charge more rapidly and extend these vehicles’ ranges. Such improvements are essential for furthering their widespread adoption, but the complex chemistry and design requirements of EV batteries make it difficult to optimize their designs for efficiency while keeping costs down. In addition, most EVs today rely on lithium-ion batteries, which require the mining of rare elements and raise their own environmental concerns.



Hydrogen-powered vehicles, particularly fuel-cell electric vehicles (FCEVs), represent another alternative to ICE vehicles and conventional HEVs. These vehicles emit only water vapor and can be refueled more quickly than BEVs charge. Despite these advantages, the costs and safety challenges of producing, storing, and distributing hydrogen fuel have thus far limited the market for hydrogen-powered vehicles.



Software-defined Vehicles and Increased Connectivity



The growth of software-defined vehicles (SDVs) represents another important transition for T&amp;M companies. Unlike traditional vehicles defined by mechanical and electrical hardware, SDVs are built around a central software architecture that controls nearly all vehicle functions. This enables the smart, connected functionality and personalized driving experiences customers increasingly expect. SDVs allow for over-the-air (OTA) updates that can improve functionality and facilitate the addition of new features throughout the vehicle’s lifestyle. Advanced software is also crucial to the ongoing development of autonomous navigation systems. However, integrating high-performance computing platforms, advanced sensors, and increasingly sophisticated software systems into vehicles requires an overhaul of the vehicle design process. Companies must therefore rapidly build software development expertise and ensure their new vehicle architectures and capabilities comply with safety and cybersecurity standards.



Assisted and Autonomous Driving



The rise in software-defined functionality in today’s vehicles has coincided with the development of assisted and autonomous driving systems. Advanced driver-assistance systems (ADAS) provide numerous safety enhancement features, such as adaptive cruise control and lane-centering. These features, many of which now come standard on numerous makes and models, automatically adjust a vehicle’s speed or lane position to maintain a safe distance from others on the road. Today’s vehicles are also often equipped with enhanced warning systems that alert drivers to potential frontal collisions and blind spot threats. Some systems even offer traffic sign recognition to keep drivers informed of speed limits and navigation changes.



Autonomous driving systems, which enable vehicles to operate without human intervention, are also becoming more advanced. These systems require a significant amount of computational power, and their technical complexity makes it difficult to implement the high levels of automation required to satisfy regulators’ safety concerns. As a result, autonomous driving remains largely experimental.



New Business Models



The T&amp;M industry is also facing disruption from alternative business models that redefine the relationship between vehicles and consumers. Subscription-based models allow customers to access a fleet of vehicles—with maintenance and insurance included—for a recurring fee. This enables them to switch between models and brands without committing to a lease or purchase agreement. Similarly, mobility-as-a-service (MaaS) models integrate multiple modes of transport, such as ride sharing, car sharing, and public transportation, into a single on-demand service available through a digital platform. This model may be particularly attractive to urban customers who value convenience and flexibility more than car ownership.



Advanced Digital Tools Provide Critical Capabilities



The increased complexity of vehicle systems and their growing reliance on software requires startups and SMBs to adopt design and development solutions that enable them to share information and collaborate across engineering domains and functional departments more efficiently. These companies must also be able to streamline the integration of numerous vehicle systems and verify and validate the behaviors and performance without relying extensively on costly, time-consuming physical prototyping and testing. In addition, cloud-based technologies can provide smaller companies the scalability, flexibility, and powerful computing resources they need to manage large amounts of data and integrate new services, all without significant hardware investments. By embracing modern digital tools and the capabilities they provide, T&amp;M startups and SMBs can navigate the challenges of the industry’s landscape, reduce time-to-market, and more readily pursue opportunities to innovate and capture market share. 



To learn more about how startups and SMBs can manage the challenges of design and manufacturing in the T&amp;M industry, check out Lifecycle Insights’ Transportation &amp; Mobility Industry Trend Report: A Guide for SMBs and Startups.











Disclaimer: This post was written by Lifecycle Insights and may not reflect the official position of Dassault Systèmes.
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      <title>
      <![CDATA[ Developing an electric truck from the ground up ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/transportation-mobility/developing-an-electric-truck-from-the-ground-up/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/271170</guid>
      <pubDate>Fri, 25 Oct 2024 05:00:00 GMT</pubDate>
      <description>
      <![CDATA[ Harbinger is redefining the medium-duty truck segment with its innovative electric vehicle featuring a reimagined, scalable chassis architecture – developed entirely on the 3DEXPERIENCE platform.
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      <![CDATA[ 
In the US, Harbinger is seizing the opportunity to bring something truly transformational to the medium-duty truck segment. The California-based startup recognizes that the need for more energy efficient and cost-effective medium-duty trucks is greater than ever, driven by skyrocketing demand for last-mile delivery and distribution services. And it believes its pioneering chassis for battery-electric Class 4 to Class 7 vehicles – that’s everything from commercial walk-in delivery vans and box trucks to school buses and recreational vehicles – has the potential to revolutionize this sector and make electrification a truly viable and cost-effective option.



Because of the distances they travel and how they’re used, these types of trucks are well suited for electrification. Yet this also is a sector that’s highly cost sensitive. By making an electric chassis from the ground up, Harbinger has set out to compete directly with gas and diesel models while also enhancing safety, driver comfort, and vehicle performance – all tailored specifically for commercial fleet operators.



“We saw a unique opportunity to deliver a compelling electric vehicle in the medium-duty segment without being forced to compromise on performance, durability or affordability,” said John Harris, Harbinger’s CEO and co-founder.



A crucial part of Harbinger’s vision is its use of Dassault Systèmes’ 3DEXPERIENCE platform on the cloud. This powerful environment brings together all of the capabilities the company needs to manage the entire product development lifecycle in one place.



“When we look at building a product like this, we need a CAD, PLM and manufacturing solution, and we needed all of those things to talk to each other natively,” Harris said. “Having all of that in one ecosystem is really important to achieving efficient design and use of resources internally as well as keeping us moving at a high speed.”



While basic technology solutions may be tempting for startups, Harbinger advocates investing in a proven industry platform that delivers all the best practice workflows and functionality needed now and in the future. Switching systems midway through a complex engineering project can be highly disruptive; Harbinger’s choice of a robust, scalable platform ensures continuity and reliability throughout the vehicle’s lifecycle. By unifying data across its processes, Harbinger makes sure all of development stays on course. Later on, when its trucks are fully in operation, it will collect valuable customer feedback, refine its products, and manage inventory and supply chain demands with precision.



Collaboration is another critical challenge: Harbinger faces the complexities of connecting its different teams working across different time zones and countries. With the 3DEXPERIENCE platform on the cloud, teams around the world can access the same data, review real-time design updates and track changes. This seamless, global collaboration is essential for keeping projects on track and adapting to changes quickly.



Looking ahead, Harbinger is committed to scaling its operations. Its decision to adopt Dassault Systèmes solutions wasn’t just about meeting immediate needs; it was about preparing for the future. With the capabilities to support advanced engineering and manufacturing processes built right into the 3DEXPERIENCE platform, Harbinger is well-positioned to continue leading the charge in electrifying the medium-duty vehicle market.



Discover how, by combining a pioneering development approach with powerful digital capabilities from Dassault Systèmes, Harbinger is setting a new standard for what’s possible in electric truck innovation. &nbsp;




Get the full story

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      <title>
      <![CDATA[ Step inside a campervan unlike any other ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/transportation-mobility/step-inside-a-campervan-unlike-any-other/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270588</guid>
      <pubDate>Mon, 14 Oct 2024 13:05:54 GMT</pubDate>
      <description>
      <![CDATA[ Visitors to the upcoming 3DEXPERIENCE Conference Eurocentral 2024 event will be able to see how Kiwi Van Manufaktur develops bespoke campervan interiors using Dassault Systèmes’ tools.
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      <![CDATA[ 
When a carpenter, an automotive engineer and a software developer come together, great things can happen. It’s the combination that started German company Kiwi Van Manufaktur.



Benedict Schlagberger and brothers Thomas and Alexander Mussner all share a passion for camping and became fascinated with converting camper vans. Kiwi Van Manufaktur designs and produces both custom and small series campervan interiors that offer revolutionary design, unlimited comfort and innovation without compromise. And it does this by leveraging Dassault Systèmes’ 3DEXPERIENCE platform on the cloud.



The Kiwi Madeira, for example, is a compact luxury camper and its interior has been collaboratively developed, visualized and validated using Dassault Systèmes design applications on the cloud, before being built in Kiwi Van Manufaktur’s state-of-the-art factory. It is designed to provide ultimate freedom for owners and boasts self-sufficient technology, a variable seating area, a spacious wet room and even a mobile workplace. The extendable heavy-duty garage offers space for up to four bicycles and allows e-bikes to be charged using the integrated charging device. An extra through-loading system for long sports equipment, such as skis and surfboards, as well as a 110-litre freshwater tank also find their place in the rear.



The van has already garnered a lot of attention on social media, including from popular YouTubers Clever Campen, Frank D. Camping and CamperTobi. During a visit to Kiwi Van Manufaktur’s factory, CamperTobi referenced the Madeira’s quality and highlighted the service hatch, which was specially manufactured using a 3D printer – nothing from the Bavarian custom builder is off-the-shelf. CamperTobi was also impressed by the floor plan, saying he’d never seen anything like it anywhere else.



Want to see the van for yourself? Kiwi Van Manufaktur will be showcasing its incredible craftsmanship at the upcoming 3DEXPERIENCE Conference Eurocentral 2024. Taking place in Munich from October 16-17, the event will enable attendees to discover the types of groundbreaking products that are being developed using Dassault Systèmes’ technology, and also hear from industry pioneers and disruptive thinkers about their business challenges and strategies – all under the theme of “Re-imagine the future, transform today.”



Read the full customer story.



Register for the 3DEXPERIENCE Conference Eurocentral 2024 event.
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      <title>
      <![CDATA[ Rapid Aerodynamic Development using CFD and Machine Learning ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/simulia/rapid-aerodynamic-development-using-cfd-machine-learning/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270037</guid>
      <pubDate>Fri, 04 Oct 2024 13:24:46 GMT</pubDate>
      <description>
      <![CDATA[ This blog illustrates how aerodynamic data obtained from the 3DS CFD PowerFLOW software combined with data-driven methods can enable car manufacturers to obtain clean 3D contour plots of the vehicle’s surface X-force distribution, the associated integrated vehicle drag force and more within several minutes on a single GPU.
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      <![CDATA[ 
Summary



The vehicle market is evolving rapidly. New players are entering the market, the drive for vehicle electrification is ongoing, many variants of a vehicle are investigated prior to freezing the design, and sustainability is a major topic. In this context, vehicle aerodynamics is ever more crucial. It directly impacts the vehicle range and plays a major role in meeting regulation targets. Vehicle manufacturers must also keep in mind the need for a shorter time-to-market, where one must design faster and not permit late-stage redesign. This all means that a faster assessment of vehicle aerodynamics is imperative. In the past and present, computational fluid dynamics (CFD) has opened the door to virtual aerodynamic testing, allowing manufacturers to test their vehicle shapes before developing a costly and time-intensive prototype that then needs to be experimented on using a wind tunnel. While high-fidelity CFD, such as the PowerFLOW software offered by Dassault Systèmes (3DS), will remain an integral part of the aerodynamic development process of major OEMs, the growth of machine learning (ML) and continual improvement of its algorithms has opened doors to speed-up computational aerodynamics. The current work illustrates how aerodynamic data obtained from the 3DS CFD PowerFLOW software combined with data-driven methods can enable car manufacturers to obtain clean 3D contour plots of the vehicle’s surface X-force distribution, the associated integrated vehicle drag force and more within several minutes on a single GPU (after training of the ML model). This represents a significant reduction in computational cost and time, as running a high-fidelity aerodynamics CFD simulation requires several hundred CPUs and a couple of hours. More importantly, the error between the ML predicted drag force and the true PowerFLOW value is not larger than 3%. The geometry investigated is the open-source DrivAer car model with varying wheel spoke number and width.



Introduction



The use of the term digital twin is becoming more and more widespread. It represents a concept in which a digital replica of an object, or even a living being, is created to better understand, engineer, and optimize the real-life twin. So far, in the realm of vehicle aerodynamics, low-fidelity and high-fidelity computational fluid dynamics (CFD) software has been used to predict and improve the aerodynamic behavior (e.g. drag, lift, and yaw characteristics) of the car, thereby bypassing the need for costly physical prototypes until the very final stages of the vehicle design cycle. However, a proper digital twin of a vehicle should be able to illustrate the effect of geometry changes in near real-time, which, so far, using conventional computing architectures is not possible. Quantum computing, a very nascent technology, may solve this, but, for now, the only option is to resort to fast surrogate machine learning (ML) models. These models have learned from past aerodynamic data to predict the new aerodynamic performance of vehicle shapes that were not seen during the ML model training phase. This prediction speed-up solves the global problem of an increasing need for a shorter time-to-market in an ever-more competitive market landscape, where there is a drive for vehicle electrification and there are stricter sustainability regulations as well (see figure 1).



Fig. 1: The need for ML model based surrogates in CFD







The idea is to embed the trained ML model(s) in the design stage such that the degree of fidelity that was previously only possible at the detailed design stage is now possible during the conceptual design phase as well, providing conceptual vehicle designers more aerodynamic insight into their proposed vehicle shapes and preventing late-stage design failures (see figure 2). Specifically, the goal of this work is to demonstrate that ML can be used as a surrogate model to reduce the use of CPUhrs intensive PowerFLOW CFD simulations for aerodynamic drag prediction, while significantly accelerating the compute time from several hours to merely several minutes.



Fig. 2: How ML models are embedded into the aerodynamic development workflow







Methodology



As a use-case, the following optimization study is analyzed: a design-of-experiment (DOE), where the tire spoke number and spoke width of the open-source DrivAer car geometry (see figure 3) are varied, with the optimization goal being the identification of the lowest vehicle drag configuration. In figure 4, the DOE design space is depicted. Red squares represent the simulation set that is used to train the ML models, green circles are the validation data points used in the ML training process to observe ML model convergence, and the blue triangles represent the blind test sets that will be used to see how well the ML models generalize to unseen vehicle geometries. The design space extremes are illustrated in figure 5.



Fig. 3: DrivAer car geometry











Fig. 4: DOE design space



Fig. 5: Design space extremes







Fig. 6: Flow and surface field representations from the PowerFLOW simulation – the one of interest for this study is the surface X-force distribution (sample data is shown for the 3DS E-car geometry)







The study can be broken down into two segments: the CFD portion and the ML model part. During the CFD phase, 40 high-fidelity simulations are prepared in PowerDELTA, set-up in PowerCASE, run with PowerFLOW and some results are subsequently visualized in PowerVIZ as a sanity check that the data is reasonable. Each simulation takes about 6 hours and requires up to 300 CPU cores. Sample aerodynamic result visualizations are depicted in figure 6 for the Dassault Systèmes (3DS) E-car geometry. For this study, the critical surface field representation is the surface X-force distribution. Once the CFD data is collected, 34 simulation points are used to train deep learning neural network models that are written in Python with 3 simulation points used as validation points. A schematic of the feed-forward neural network models applied is depicted in figure 7 with the output data again represented by the 3DS E-car geometry. The input layer of the neural network models is fed with x-, y-, and z-coordinate data, as well as the spoke number and spoke width. This information is then propagated through the hidden layers of the neural network and, finally, at the output layer one obtains the surface X-force distribution of the vehicle. Training the neural network models takes approximately 3 days on a Windows workstation with a single GPU card. Meanwhile, the inference step, where the surface X-force distribution for vehicle geometries not seen during the neural network model training phase is predicted, merely takes several minutes.



Fig. 7: Schematic of the deep learning neural network models used in this external vehicle aerodynamics DOE study (sample output is for the 3DS E-car geometry again)







Results



Before assessing the predictive capabilities of the trained ML models, it is imperative to check that the models are not under-fitting nor over-fitting the training data. This is done by looking at the mean-square-error (MSE) of the models as a function of training epoch. Two sample plots are provided in figure 8. Although for the 3DS E-car, similar orders of magnitude were achieved for the DrivAer car study presented in this article. Evidently, the MSE decreases almost monotonically and stays low as well, illustrating that the models should generalize well when exposed to unseen vehicle geometries. Furthermore, the MSE has an order of magnitude of O(10-3), which is reasonable as well.



Fig. 8: Sample MSE plots (for the 3DS E-car geometry)







Now, one can look at the ML models predictive capabilities. For the blind test points, table 1 shows the drag force deltas between the true PowerFLOW results and the ML model outcomes and, more importantly, the percentage error. Evidently, the percentage error is around 2% for all three test cases, which is a very good outcome. Beyond that, the trained ML models were applied to all simulation points: training points, validation points, and test points. The associated accuracy of the predications is depicted in figure 9. Here, one can clearly see that the error lies within the 3% error bound. It is also interesting to note that there is a bias in the ML model results; the ML models consistently under-predict the integrated drag force. Having looked at the integrated drag force, surface X-force distribution plots provide further insight into the predictive capabilities of the ML models. Figure 10 shows these surface contour plots from different angles and viewpoints for run 37 (similar plots are obtained for all other runs). It is quite clear to see that there is almost no difference between the true PowerFLOW results and the ML predicted results. This substantiates the claim that the ML models are good surrogates for expensive high-fidelity PowerFLOW aerodynamic simulations.



Table 1: True PowerFLOW drag force vs. ML model predictions for the test points







Fig. 9: Overall predictive capability assessment of the ML models







Fig. 10: PowerFLOW result vs. ML result for surface X-force distribution of run 37







Conclusion



Overall, a multi-model machine learning approach based on simple feed-forward artificial neural networks has been validated for the use of external aerodynamic drag force prediction of clay-model like vehicles. Once the ML model(s) have been trained on a single GPU for ~ 3 days, inferences (i.e. predictions) of drag force for unseen vehicle geometries take ~2 minutes as opposed to running a CPUhr intensive high-fidelity PowerFLOW simulation that takes ~6 hours on up to 300 CPU cores. Importantly, the percentage error between the true CFD PowerFLOW data and the ML model predictions of integrated drag force lies at less than 3%.



Future Work



A number of avenues for future work exist. One still needs to tune the ML model hyper-parameters to accelerate the ML model training and inference steps, while decreasing the ML model output error. It is also imperative that one extend the ML model predictive capabilities to allow good predictions of open-grill vehicle geometries with engine bay components. Furthermore, one also needs to build ML models to allow prediction of fluid volume velocity, thereby allowing the identification of separation lines around the vehicle. Finally, one could also envision the construction of generative AI models that create a new/optimized vehicle geometry with minimal separation lines and, therefore, close to zero low pressure drag enhancing wake regions.







Firoz Gandhi is a computational engineer with specialization in CFD. He completed his Master’s in computational modeling &amp; simulation from TU Dresden and has experience working on several research projects touching various aspects of fluid dynamics such as turbulent flow, multi-phase flow, external and internal flows, etc. While working on his Master’s thesis, Firoz developed a custom algorithm to model the wall-slip effect for a non-Newtonian fluid and implemented it in OpenFOAM, achieving impressive accuracy.&nbsp; During his internship at Dassault Systemes, Firoz contributed to multiple projects, managing some independently while assisting colleagues on others. His passion for methodology development has recently led him to explore the integration of machine learning with CFD, a field he aims to build his career in.



















Interested in the latest in simulation? Looking for advice and best practices? Want to discuss simulation with fellow users and Dassault Systèmes experts?&nbsp;The&nbsp;SIMULIA Community&nbsp;is the place to find the latest resources for SIMULIA software and to collaborate with other users. The key that unlocks the door of innovative thinking and knowledge building, the SIMULIA Community provides you with the tools you need to expand your knowledge, whenever and wherever.
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