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etd.lib.metu.edu.tr
research
https://etd.lib.metu.edu.tr/upload/12620584/index.pdf
Figure 2.65 : Francis turbine losses as function of the specific speed 70 Figure 2.66 : Flow rate versus guide vane opening for different heads 71 Table 2-5 : Results for the numerical simulation, standardized values Q H P effCFD PHI PSI nq QED Q11 nED n11 GV-angle a0 Servo piston travel [m³/s] [m] [MW] [%] [-] [-] [rpm] [-] [-] [-] [-] [°] [mm] [mm] 11.04 122.57 11.09 73.18 0.05 1.46 27.06 0.05 0.15 0.37 69.91 5 31.8 113.4 17.11 122.57 18.79 83.64 0.08 1.46 33.69 0.07 0.23 0.37 69.91 7.5 47.4 141.6 23.01 122.57 25.64 86.68 0.11 1.46 39.07 0.10 0.31 0.37 69.91 10 62.7 169.0 28.38 122.57 32.43 89.93 0.13 1.46 43.39 0.12 0.39 0.37 69.91 12.5 77.7 195.6 33.03 122.56 38.27 91.85 0.16 1.46 46.81 0.14 0.45 0.37 69.91 15 92.4 221.4 37.25 122.56 43.28 92.57 0.18 1.46 49.71 0.16 0.51 0.37 69.91 17.5 106.8 246.5 41.18 122.56 47.29 91.84 0.19 1.46 52.26 0.18 0.56 0.37 69.91 20 121.0 270.9 44.81 122.56 50.58 90.52 0.21 1.46 54.52 0.19 0.61 0.37 69.91 22.5 134.9 294.5 47.98 122.56 52.88 88.58 0.23 1.46 56.41 0.21 0.65 0.37 69.92 25 148.4 317.4 50.76 122.56 54.04 85.68 0.24 1.46 58.03 0.22 0.69 0.37 69.92 27.5 161.7 339.7 11.61 130.74 12.68 75.05 0.05 1.56 26.44 0.05 160.37 0.36 510.40 5 31.8 113.4 17.99 130.74 21.04 83.85 0.08 1.56 32.91 0.08 199.59 0.36 349.12 7.5 47.4 141.6 24.14 130.74 28.72 87.01 0.11 1.56 38.12 0.10 231.19 0.36 276.52 10 62.7 169.0 29.74 130.74 36.36 90.44 0.14 1.56 42.31 0.12 256.61 0.36 231.65 12.5 77.7 195.6 34.62 130.73 42.96 92.43 0.16 1.56 45.66 0.15 276.88 0.36 204.41 15 92.4 221.4 38.78 130.73 47.85 92.32 0.18 1.56 48.32 0.16 293.03 0.36 188.54 17.5 106.8 246.5 42.84 130.73 52.29 91.64 0.20 1.56 50.79 0.18 307.99 0.36 176.41 20 121.0 270.9 46.62 130.73 56.00 90.43 0.22 1.56 52.98 0.20 321.31 0.36 167.56 22.5 134.9 294.5 49.76 130.73
S
sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0960148124019906
Based on the optimized blade angles, the efficiencies are improved by 1.12 % and 1.42 % at N S = 150 and 270 respectively with a constant power output of 30 MW.
E
engineeringmechanics.cz
article
https://www.engineeringmechanics.cz/pdf/20_2_139.pdf
et al.: Development of High Specific Speed Francis Turbine for Low Head HPP blade, the automatic mesh generation, CFD calculations with post-processing and the own optimization cycle with defined objective function. ed., Kluwer Academic Publishers, Dordrecht, 1992, 500 p., ISBN 0-7923-1505-7 [2] Obrovsky J., Seda B., Zouhar J.: Experience with hydraulic design of low specific speed turbine, 4th IAHR International Meeting of the Workgroup on Cavitation and Dynamic Problems, 2011, Belgrade, Serbia [3] Sado K., ShyangMaw L., Yasuyuki E.: Virtual model test for a Francis turbine, 25th IAHR Symposium on Hydraulic Machinery and Systems, 2010, Timisoara, Romania [4] Skotak A., Obrovsky J.: The utilization of optimization for water turbine blade shape uprating, Fluent Userˇ zs Meeting, 2006, Hrotovice, Czech Republic [5] Skotak A., Obrovsky J.: Shape Optimization of a Kaplan Turbine Blade, 23rd IAHR Sympo-sium on Hydraulic Machinery and Systems, 2006, Yokohama, Japan, paper 233 [6] Skotak A., Obrovsky J.: Analysis of the flow in the water turbine draft tube in Fluent and CFX, 25th CADFEM Userˇ zs Meeting, 2007, Dresden, Germany [7] Storn R., Price K.: Differential Evolution – A Simple and Efficient Heuristic for Global Op-timization over Continuous Spaces, Journal of Global Optimization, Kluwer Academic Pub-lishers, 1997, vol.
A
asmedigitalcollection.asme.org
article
https://asmedigitalcollection.asme.org/fluidsengineering/article/142/10/10121…
An optimization algorithm is proposed and applied to the runner of a low specific speed Francis turbine, with an optimization strategy specifically constructed
S
scribd.com
article
https://www.scribd.com/document/223228283/Conceptual-Design-Optimization-of-F…
# Conceptual Design Optimization of Francis Turbines. ## Uploaded by. Hydraulic turbines have been studied, designed, built and put into operation for nearly 250 years. This work presents a conceptual design methodology for Francis turbines. It combines simplified models for the turbomachine fluid flow with numerical optimization techniques. ## Share this document. ## Footer menu. ## Support. ## Legal. ## Social. ## Get our free apps. Scribd - Download on the App Store. Scribd - Get it on Google Play.
B
blog.adtechnology.com
article
https://blog.adtechnology.com/machine-learning-hydraulic-turbine-francis-runn…
# Machine Learning for Hydraulic Francis Runner Design Optimization. A new methodology uses **3D Inverse Design** technology coupled with **Reactive Response Surface (RRS) Machine Learning** to rapidly optimize Francis hydraulic turbine runners. This approach requires only **10 input parameters** to explore a vast design space and, in just a few hours, discovered optimized designs that showed significant performance gains, including **5-9 percentage points higher efficiency** and an **8-28% increase in shaft power** over the baseline model. In this blog we look at how ADT’s Reactive Response Surface + CAE technology (RRS+CAE) is driving better hydraulic turbine design through Machine Learning. ## • The Francis Runner performance challenge - and the solution • Where to start - Generate a meanline Francis runner design • 3D Inverse Design is the enabling technology for Machine Learning • How to establish a baseline for turbine performance • Optimization of a Francis runner via Machine Learning • RRS gives design choices and performance gains • Final validation of the Machine Learning solution • Conclusions.
I
ideas.repec.org
article
https://ideas.repec.org/a/eee/renene/v238y2025ics0960148124019906.html
As the access to this document is restricted, you may want to search for a different version of it. ## More about this item. See general information about how to correct material in RePEc. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form . If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. Upload your paper to be listed on RePEc and IDEAS.
M
mdpi.com
article
https://www.mdpi.com/2071-1050/14/16/10331
The proposed method aims to improve the hydraulic performance of the turbine, enhance and suppress the vibration of the turbine, and expand the operation range of the turbine on the basis of the actual situation given that Francis turbine frequently operates in low- and ultralow-load areas under the condition of multi-energy complementarity and continuous adjustment of operating conditions. The super-transfer approximation method was used to select the weight co-efficient of water turbine operating conditions, and a multi-objective optimization function with the efficiency and cavitation performance of the water turbine as optimization objectives was constructed to ensure that the optimized water turbine can achieve the optimal performance in the full working condition range. A multi-objective and multi-condition optimization design method for Francis turbine runner based on the super-transfer approximation method is proposed in this work to improve the hydraulic performance of the turbine in the full working condition range and broaden the working range of the turbine given that the Francis turbine frequently operates in low- and ultralow-load areas under the condition of multi-energy complementarity and continuous adjustment of operating conditions.