Salma Moutaouafiq

🚀 Simulating SpaceX Merlin Nozzle 🌌 Thrilled to share my latest CFD simulation project ! I focused on the nozzle of the SpaceX Merlin engine, a key component in their rocket engines. Simulation Details: Flow Type: Transonic/Supersonic, compressible flow Inlet Conditions: 9.72 MPa pressure, 3673.15 K temperature Simulation Duration: 0.042 seconds Key Insights: Flow Behavior: Captured the complex flow patterns and shock waves within the nozzle. Efficiency: Demonstrated the solver’s capability to handle high-speed compressible flows effectively. CFD continues to amaze me with its ability to turn complex physical phenomena into stunning visualizations. Every simulation feels like a piece of art, driving my passion to keep learning and exploring the field. Next Steps: I plan to redo this simulation with a larger outer domain and a 3D mesh to gain even more detailed insights. Excited to see where this journey takes me next! Feel free to connect and discuss more about CFD and aerospace simulations. Reference: https://lnkd.in/dCijnKr2 #CFD #SpaceX #MerlinEngine #sonicFoam #RocketScience #FluidDynamics #Engineering #Simulation #Passion

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Title: Introducing My Coaxial X8 Octocopter : Redefining Drone Innovation Description: 🚁 Excited to unveil my latest creation: the Coaxial X8 Octocopter! 🚀 With eight powerful arms and a sleek design crafted from my own vision and expertise in 3D Experience CATIA, this drone is set to revolutionize aerial technology. 🔍 What sets my Coaxial X8 apart? Let me break it down: 1️⃣ Coaxial Configuration: Utilizing coaxial rotor design, this octocopter ensures exceptional stability and maneuverability, even in challenging weather conditions. 2️⃣ Eight Arms of Power: With a total of eight arms, each equipped with high-performance motors, my drone delivers unparalleled lift capability and endurance for extended flight missions. 3️⃣ Custom Dimension and Design: Leveraging my skills in 3D Experience CATIA, I've meticulously crafted every aspect of this drone, from its dimensions to its aerodynamic profile, ensuring optimal performance and efficiency. 4️⃣ Versatility in Application: Whether it's aerial photography, surveying, or search and rescue missions, the Coaxial X8 is engineered to excel in diverse environments and tasks. 5️⃣ Safety First: Equipped with advanced safety features and redundancy systems, this drone prioritizes the safety of both operators and bystanders, making it ideal for professional and recreational use alike. Join me in pushing the boundaries of drone technology with the Coaxial X8 Octocopter. Together, let's soar to new heights! ✨ #DroneInnovation #CoaxialX8 #3DModeling #CATIA #AerialTechnology Access at - Tamil Nadu Centre of Excellence for Advanced Manufacturing - TANCAM

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Calcul formel pour les méthodes de Lie en mécanique hamiltonienne Pierre-Vincent Koseleff (École Polytechnique) https://lnkd.in/dMv6eXii Our first goal in this report is the comparison of the two Lie perturbation methods used in hamiltonian mechanics. The Deprit as well as the Dragt-Finn method are based on the use of canonical formal transformations. We particularly study the relations between these two transformations and conclude that they produce the same objects, for instance the normal form and formal first integrals. Therefore, we give methods to built such invariants and show that the Dragt-Finn method is less expansive, regards to the computational cost. The knowledge of the first terms of these series may provide results for the stability of hamiltonian systems near an elliptic equilibrium point. We give some estimates for the Dragt-Finn method, like it has been given for the Deprit transform. In order to study the relations between several canonical transform, we work in a free Lie algebra and study exponential identities. We give also general methods to compute these relations (including the Baker-Campbell-Hausdorff formula) using the Lyndon basis. These explicit identities and their methods of computation allow us to obtain symplectic integrators. We list all the integrators of low order and prove that the several methods of investigation are totally equivalent. The algorithms have been implemented using Axiom and some examples are given at the appendix.

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⚙️ 𝗗𝗼 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗮𝗿𝘁𝘀 𝗼𝗳 𝗮 𝗡𝗔𝗦𝗧𝗥𝗔𝗡 𝗺𝗼𝗱𝗲𝗹? The most used joints by far are RBE2 and RBE3. You better not confuse them. I still remember my early days with NASTRAN—when I made a rookie mistake confusing RBE2 and RBE3 elements. Trust me, it wasn’t pretty, I ended up paying with 8 days of my holidays! 🔧 𝗥𝗕𝗘𝟮 𝘃𝘀. 𝗥𝗕𝗘𝟯: 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? •   𝗥𝗕𝗘𝟮 (Rigid Body Element 2): RBE2 elements create a rigid connection between nodes. They force all connected nodes to move together as a single rigid body. Mathematically you force the movement of several nodes (dependent) to move like the central node (independent). This is perfect when you need to simulate a stiff connection, like attaching a bracket to a structure. But be careful—using RBE2 can overly stiffen your model and lead to unrealistic stress concentrations. •   𝗥𝗕𝗘𝟯 (Rigid Body Element 3): Unlike RBE2, RBE3 elements distribute loads in a more flexible way, without adding stiffness to the model. Mathematically the move of the central node (dependent) is the weighted average of several nodes (independent). They’re ideal for simulating connections where you want to distribute loads across multiple nodes, like attaching a force to a surface area. RBE3 is great for maintaining realistic deformation while still accurately representing load distribution. 🔍 𝗟𝗲𝘀𝘀𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗲𝗱: Each has its place in structural modeling—RBE2 for rigid connections and RBE3 for flexible load distributions. Understanding when to use each is key to accurate simulations. #Engineering #FiniteElementAnalysis #NASTRAN #StructuralAnalysis #RBE2 #RBE3 (*) Image credit: Nader Zamani in his Youtube channel

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External aerodynamics of a train by TCAE 🔬Delve into the world of external aerodynamics with our detailed video tutorial on TCAE, an intelligent and budget-friendly simulation software 📊! We guide you through the basic steps of leveraging TCAE to model and analyze the aerodynamic performance of a moving train 🚅💨. This comprehensive guide vividly illustrates how this software can enable academic researchers to conduct complex studies without breaking the bank. Master how to emulate real-world situations and navigate intricate data points with ease 🎯. Don't forget, researchers, we've included a full case study that you can readily download at https://lnkd.in/g6pV326P. This valuable resource allows you to explore the methodologies and results of this study in-depth, providing you with a rich context for understanding the practical application of TCAE in studying train aerodynamics 📚📈. For a more interactive learning experience, check out our YouTube video at https://lnkd.in/gmp47jJE 🖥️ Discover the incredible utility of TCAE in transforming mere data into insightful, applicable knowledge. Stay tuned for more research-based tutorials designed to empower and educate you for the future in aerodynamics 💡🌐. Let's continue to push the boundaries of knowledge together! #Aerodynamics #ResearchTutorial #TCAESimulation #TrainPerformance #AcademicResearch 🎓🔎🚄

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✈️I am excited to share my final project for the Numerical FluidDynamics course. In particular, using Harlow-Welch discretization, the flow behavior within a contraction channel is been analyzed, The examined flow is considered an incompressible viscous flow, so the ’no-slip’ condition is applied to each wall. Inflow and outflow are both considerd as parabolic flow (Poiseuille). Several studies have been carried out by varying the contraction ratio and the Reynolds number. 💨This revealed that at low Reynolds the flow symmetry is maintained, while at higher Reynolds it is lost as time progresses, and also vortices are formed at the entrance and within the contraction. It is also noted that as the Reynolds and the contraction ratio increase, the flow within the contraction is increasingly subject to separation and vortices. So this work shows the relationship between these variables in determining flow behavoir. #aerospace #engineering #HarlowWelch #FluidDynamics #NumericalFluydDynamics #NumericalSimulation

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🌀 Vortices enable the complex aerobatics of peregrine falcons 🐦 📝 Paper: https://lnkd.in/eY82_uKB 📥 Latest newsletter: https://bit.ly/41N6Ixq #engineering #cfd #aerodynamics

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In the world of fluid dynamics and Computational Fluid Dynamics (CFD), Lagrangian Coherent Structures (LCS) are pivotal in uncovering hidden flow behaviors. These structures, derived from the Lagrangian viewpoint, provide valuable insights into the dynamics of complex flows. LCS act as natural boundaries that delineate regions of coherent fluid motion. They help identify vortices, separation zones, and mixing regions, crucial for understanding transport phenomena in various applications, from ocean currents to aircraft aerodynamics. Understanding LCS not only enhances predictive modeling accuracy but also aids in optimizing designs for efficiency and performance. By leveraging LCS analysis, engineers and researchers can make informed decisions, leading to innovations in diverse fields such as environmental science, aerospace, and oceanography.

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A fabulous view of the simplified process to drive product creation with #topologyoptimization and #generativedesign. Early adopters should be able to increase margins by material reduction and improved product performance. #3dprinting is one option but the optimization could be translated to other forms such as die casting for part and assembly reduction.

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🔍 Utilizing advanced simulation in Abaqus, this dynamic explicit analysis of a rubber seal employs a hyperelastic polymer material model, specifically the Ogden strain energy potential, to accurately predict the mechanical behavior under operational loads. 🛠️ 📊 The visualization showcases von Mises stress distribution across the seal, highlighting areas of highest stress and potential vulnerabilities. Accompanying energy graphs plot the internal and strain energy over time, providing insights into the material's response to dynamic stresses. 📈 🌟 This analysis is pivotal for optimizing rubber seal designs to enhance durability and performance, ensuring these components can withstand severe conditions without failure. Perfect for industries requiring high reliability in material applications. 💪 🔗 Dive deeper into how simulation technology is revolutionizing product testing and development in the engineering field! #Engineering #Simulation #ProductDevelopment #Abaqus #MaterialScience

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Curious how AI can be integrated into the MBSE approach? Stop by our booth at the Cyber Systems Symposium and see how Studio SE, Ltd. is supporting the integration of AI into the MBSE approach ✈ 🔬 👩‍💻 #mbse #systemsengineering #catia #modeling #dassault #MBSEtraining #cybersymposium 

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Our presentation "𝐇𝐮𝐦𝐚𝐧 𝐁𝐨𝐝𝐲 𝐌𝐨𝐝𝐞𝐥𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐛𝐲 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐦𝐞𝐬𝐡 𝐦𝐨𝐫𝐩𝐡𝐢𝐧𝐠: 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐫𝐢𝐜 𝐓𝐇𝐔𝐌𝐒" at the Automotive CAE Grand Challenge 2024 was a success. If you missed it, learn about the parametrization of the Total HUman Model for Safety (THUMS) that we have developed through the mesh morphing of THUMS AM50 (Adult Male 50th anthropometric percentile) to different body sizes in the full presentation, available at this link: https://lnkd.in/d9v_S_Wj 𝘐𝘯 𝘵𝘩𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦, 𝘢 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦 𝘰𝘧 𝘮𝘰𝘵𝘰𝘳𝘣𝘪𝘬𝘦 𝘢𝘦𝘳𝘰𝘥𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 𝘪𝘯 𝘝𝘙. #simulation #meshmorphing #rbf #rbfmorph #safety #automotive #virtualreality #thums

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Many car companies and magazines mention the drag coefficient (Cd) as a measure of aerodynamic performance, but did you know that this is only partial information?   The Cd tells us about the aerodynamic efficiency of a shape and allows aerodynamicist to compare different objects independently of their size or speed.   Imagine that a bicycle and a truck have the same Cd at 0,6. We can all understand that the air resistance of a bike is much lower than a truck’s. This is where the frontal area (A) comes into play, a larger area means that more air needs to move around the object, increasing the force required to move this object forward.   This is explained by the drag force equation F = 0,5.ρ.Cd.A.v2   So, the correct value that manufacturers should provide us with is the Cd x A (or Cd.A) as it is a better indicator of a car’s drag.   We can look at 2 examples:   Example 1: New vs old, the Audi A6 In 2024, Audi has moved from ICE to full electric for the A6. Online I found the EV A6 has a drag value of 0,21 vs 0,26, which means a reduction of 19%. What it does not say, is that the frontal area increased from 2,33 m2 to 2,46 m2 (+6%), which gives us a Cd.A of 0,517 m2 vs 0,606 m2, meaning a reduction of 15%. While 15% is still impressive, it is worth noting that the size of modern cars impacts their efficiency and therefor their range.   Example 2: Electric sedan vs SUV, Porsche With SUVs replacing every sedan car on the road, we should mention the challenge with their efficiency. 2024 Porsche Taycan (Sedan): Cd = 0,22, A = 2,33 m2, Cd.A = 0,513 m2 2024 Porsche Macan 4 (SUV): Cd = 0,25 (+14%), A = 2,69 m2 (+15%), Cd.A = 0,673 m2 (+31%) At highway speed, the Macan SUV will require 31% more power than the Taycan, meaning a shorter range with the same battery size.   This illustrates the importance of this value; you can see that the 2024 Porsche Taycan has a lower Cd.A, giving it a lower drag force than the 2024 Audi A6, even though its Cd is higher. Only looking at the Cd would thus lead us to the wrong conclusion that the Audi A6 would be more aerodynamic.   An even more extreme example of this is the 2024 Porsche Cayenne GTS which has a Cd.A of 0,972 m2 (Cd = 0,34), 90% higher than the Porsche Taycan.   When we are talking about increasing electric vehicle range, increasing the battery size should not be the first solution. We should develop smaller cars with smaller batteries which would also lead to shorter charging time, less weight and lower price.   I am always glad to get Cd values, but this is only partial information that can give customers misleading information when comparing efficiency of vehicles. In my opinion, we should look into optimizing the Cd.A and focus on developing cars that are smaller and more efficient.   I did an extensive study on the Lamborghini Countach where I talk more about this topic. You can find it here: https://lnkd.in/eiNmtKbp #Aerodynamics

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Simulation Practices: Pressure Gradient isosurface with velocity coloring on a flow across a two-wheeler simulation. Software: OpenFOAM Solver: pimpleFoam Turbulence Model: LES Visualization in Paraview.

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Role of residual values in CFD ? Residual values play a pivotal role in ensuring accurate and reliable Computational Fluid Dynamics (CFD) simulations across various engineering applications. For example, in aerodynamics simulations for aircraft design, monitoring residual values helps engineers validate the airflow patterns around the aircraft surfaces. High residual values may indicate areas of flow separation or turbulence, guiding engineers in refining wing designs for improved aerodynamic performance and fuel efficiency. In automotive engineering, CFD simulations of vehicle aerodynamics rely on residual values to assess drag and lift forces accurately. By optimizing the geometry based on residual convergence, engineers can design vehicles with reduced aerodynamic drag, leading to enhanced fuel economy and vehicle stability at high speed. for more details click on the below link- https://lnkd.in/g6zqkPjV #cfd #residual #cfdengineer #automotive #convergence

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What is Aeroelastic flutter ? "Aeroelastic flutter is a generally low to medium- frequency range vibration in aircraft caused by the interaction between aerodynamic loads and the structure, leading to deformation and potentially amplifying vibration through the coupling of bending and torsion modes, which can result in structural failure if unchecked. When considering aerodynamic damping, mode coalescing is not necessary, but the frequencies of both modes approach each other, leading to a close frequency proximity, which can still lead to an interaction between the modes and potentially exacerbate the flutter phenomenon." #Aeroelasticity #Aeroelasticconcepts2 #Flutter #Aerospace

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