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mendeley.com
article
https://www.mendeley.com/catalogue/a33e4916-7667-32ed-9d2a-93ffad537dd5
# Francis turbine blade design on the basis of port area and loss analysis. In this study, a Francis turbine with specific speed of 130 m-kW was designed on the basis of the port area and loss analysis. The meridional shape of the runner was designed focusing mainly on the combination of the guide vane loss analysis and experience. The runner blade inlet and outlet angles were designed by calculation of Euler's head, while the port area of blade was modified by keeping constant angles of the blade at inlet and outlet. The results show that the effect of the port area of runner blade on the flow exit angle from runner passage is significant. A correct flow exit angle reduces the energy loss at the draft tube, thereby improving the efficiency of the turbine. The best efficiency of 92.6% is achieved by this method, which is also similar to the design conditions by the one dimension loss analysis.
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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
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iopscience.iop.org
article
https://iopscience.iop.org/article/10.1088/1755-1315/1079/1/012029/pdf
Blade angles at trailing edge and its distribution were selected as the design variables to maximize the average efficiency and minimize the sediment erosion. A
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S1364032118308281
Variable-speed operation of Francis turbines might help to increase the operating range and flexibility of the hydraulic turbines, promoting additional
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asmedigitalcollection.asme.org
article
https://asmedigitalcollection.asme.org/fluidsengineering/article/142/10/10121…
Previous studies suggested variable speed operation (VSO) of Francis turbines as a measure to improve the efficiency at off-design operating conditions.
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empoweringpumps.com
article
https://empoweringpumps.com/cfturbo-francis-turbine-design-for-hydropower-gen…
# Francis Turbine Design for Hydropower Generation. Home » Francis Turbine Design for Hydropower Generation. CFTurbo Francis Turbine Design for Hydropower Generation. As the world shifts to greater reliance on sustainable energy sources, the design and optimization of relevant turbomachinery devices are imperative. The CFturbo software allows its users to build and optimize all components of Hydro Turbines, as shown in this introductory case study of a Francis turbine. The Francis turbine is a longstanding monument in the world of turbomachinery, dating back to the mid-19th century. The Francis turbine was invented in the mid-19th century by engineer James Bichens Francis to produce hydroelectric power. A baseline geometry was prepared using the Hydro Turbine module within the CFturbo software. Figure 2 Francis Turbine Design – CFturbo, 3D View. Using a CFturbo engineered Python script solution in conjunction with the Replace Part Operation within Star-CCM+, 25 unique Francis Turbine CFturbo designs were created and simulated using a mesh of approximately 8.5 million polyhedral cells and a steady-state solver.
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pubs.aip.org
article
https://pubs.aip.org/aip/adv/article/13/7/075208/2901828/Numerical-study-of-t…
The blade profile of a Francis turbine determines the inlet and outlet velocities and circulation under a constant guide vane opening, which, in
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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.