Institute of Fluid Mechanics and Hydraulic Machinery (IHS), University of Stuttgart

IHS Field Trip 🔊: Excited for our educational hydropower field trip from July 2nd to July 4th, 2024! 💡 Our students will explore sustainable hydropower generation and storage by visiting Austria's Kops II and Obervermuntwerk II pumped storage power plants of illwerke vkw. To round off the trip, we'll visit Voith Hydro's Brunnenmühle R&D center in Heidenheim to learn about the development and manufacturing of electromechanical power plant components from one of the industry leaders. Stay tuned for more updates on IHS hydropower activities! 🌀 #UniStuttgart #Hydropower #IHS #Excursion

🔎 We are proud of the inspiring insights given by Prof. Stefan Riedelbauch at the #FutureofHydro event. 🧠Our team at the Institute of Fluid Mechanics and Hydraulic Machinery (IHS), University of Stuttgart tackled the challenge of numerically predicting the behavior of hydroelectric turbines 🌀 across a wide operating range, pushing the boundaries of cutting-edge numerical approaches. 💡 These analyses play a crucial role in maximizing the flexibility of pumped-storage hydro, aligning perfectly with the core objectives of XFLEX HYDRO. 🙌 Grateful to our project partners for their invaluable collaboration and support! Together, we're driving innovation and shaping the future of hydroelectricity. XFLEX HYDRO, #hydropower #decarbonization, #renewablefuture, University of Stuttgart, EDP Renewables, GE Vernova, Voith Hydro

🔍 Research Flashback! 📚✨ Excited to revisit an impactful research paper, "ADVANCING STEADY-STATE FLOW FIELD PREDICTION IN MESHED DOMAINS THROUGH GRAPH NETWORKS," successfully published by our colleague Lukas Harsch. 🚀 Abstract: A model was introduced leveraging graph networks to advance the direct prediction of steady-state flow fields within meshed domains, employing an Eulerian representation of fluid flow. The versatile graph network architecture processed mesh-space simulations, offering a unique advantage in understanding both global physical systems and local structures. Key Highlights Revisited: 🔬 Innovative use of graph networks for advancing steady-state flow field prediction 🔄 Enhanced speed compared to traditional CFD methods 🔄 Ability to explore local structures for qualitative predictions This paper showcased its prowess by accelerating predictions compared to traditional Computational Fluid Dynamics (CFD) methods, maintaining a broad understanding of physical systems.  📄 Revisit the full paper here: https://lnkd.in/e4gbkgjC A heartfelt congratulations to our colleague for this remarkable achievement!  🙌🏼 Your continued thoughts and feedback are highly valued. Feel free to connect if you'd like to discuss progress since then or explore new avenues of collaboration. Let's keep pushing the boundaries of knowledge together!  🌐🔗 #ResearchFlashback #PublishedPaper #Innovation #Science #CollaborationOpportunities

Our colleague Hanbing Ma developed a Neural Network 🧠 capable of predicting pump operating points throughout the lifetime. Check out the corresponding paper 📄: https://lnkd.in/eGKnDTP3 In the proposed method, operating points are identified using a Convolutional Neural Network (CNN) with data collected from accelerometers and microphones, instead of a traditional flowmeter and manometer. The training and testing data were generated using the depicted test rig built at the IHS lab. A very good agreement between the prediction and measurement is achieved.

Wissenschaftlicher Mitarbeiter (m,w,d) gesucht: Wir suchen eine(n) wissenschaftliche(n) Mitarbeiter(in), um am spannenden Gebiet der strömungsinduzierten Vibrationen in Strömungsmaschinen der Wasserkraft zu forschen. Die Durchführung einer Promotion ist erwünscht. Die detaillierte Stellenausschreibung ist angehängt. Melden Sie sich gerne bei Fragen! English Version Open Position for a Doctoral Researcher: Flow-induced Vibration in Fluid Machinery for Hydropower. We are looking for a doctoral researcher to work in the exciting field of flow-induced vibrations in fluid machinery for hydropower. Pursuing a Ph.D. based on this project is highly desirable. The detailed job description is attached. Feel free to contact us if you have any questions!

We are pleased to offer you a glimpse of our automated design optimization framework for hydraulic machinery, developed by our colleagues Alexander Tismer and Stefan Fraas. The software package enables massively parallelized design optimization based on both hydraulic and structural aspects. Here is a live animation of an optimization process in action: A kinetic river turbine is optimized with regards to hydraulic aspects. On the left-hand side, the flow field with the current best individual (best design) is visualized and updated every time a new best individual is found. On the right-hand side, the power output of the current investigated individuals is illustrated (black dots) along with the best designs of each generation, highlighted in green. It can be observed that a substantial improvement in the efficiency of the turbine is reached. On its path towards the best design, the optimizer removes the backflow regions within the draft tube, which are colored in magenta (left CFD picture) and dark blue (right CFD picture). Additionally, a significant untangling of the streamlines is achieved.