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I
iopscience.iop.org
article
https://iopscience.iop.org/article/10.1088/1755-1315/22/1/012026/pdf
# Design optimization method for Francis turbine. -Multi-objective shape optimization of runner blade for Kaplan turbine A Semenova, D Chirkov, A Lyutov et al. Blade shape design is carried out in one kind of NURBS curve defined by a series of control points. The system was applied for designing the stationary vanes and the runner of higher specific speed francis turbine. As the first step, single objective optimization was performed on stay vane profile, and second step was multi-objective optimization for runner in wide operating range. As a result, it was confirmed that the design system is useful for developing of hydro turbine. We have also developed a hydro turbine by using design of experiments (DOE) and multi-objective genetic algorithm (MOGA) as the optimization method [1] [2] [3]. In the design system, blade profile was defined by a flexible curve, and the optimization method adapted to Particle Swarm Optimization (PSO), which is one of the swarm intelligence techniques.
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
T
tandfonline.com
article
https://www.tandfonline.com/doi/full/10.1080/19942060.2025.2541680
Specifically, the turbine efficiency increased by 5.4% at 20% Pr and by 2.83% at 50% Pr. The optimized blade geometry significantly shrinks the low-pressure
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.
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
icce2018.emu.edu.tr
research
https://icce2018.emu.edu.tr/Documents/proceedings/TURB-02-Deniz%20Sarper-Fran…
Table 1: L9 Taguchi Design Experiments/Factors A B C 1 20 60 20 2 20 70 22 3 20 80 24 4 22 60 22 5 22 70 24 6 22 80 20 7 24 60 24 8 24 70 20 9 24 80 22 Hydraulic turbine efficiency is defined as the ratio of shaft power to hydraulic power that depends on head and flow rate values. Nomenclature A Taguchi factor that represents guide vane angle ( ̊ ) B Taguchi factor that represents runner inlet angle ( ̊ ) C Taguchi factor that represents runner outlet angle ( ̊ ) g Gravitational acceleration (m/s2) H Turbine head value (m) Q Turbine discharge value (m3/s) u Velocity in x direction (m/s) v Velocity in y direction (m/s) w Velocity in z direction (m/s) Greek Letters 𝜂turb Turbine efficiency (%) Pshaft Shaft power (W) 𝜌 Density (kg/m3) μ Dynamic viscosity (kg/ms) References [1] http://www.eie.gov.tr/yenilenebilir/h_turki ye_potansiyel.aspx [2] Y.
Y
youtube.com
video
https://www.youtube.com/watch?v=CavfXOt3Dew
How to Design Wind Turbine Blade Geometry for Optimal Aerodynamic Efficiency
Engineering with Rosie
122000 subscribers
2388 likes
101573 views
10 Nov 2020
This is part 3 of my series: “How Does a Wind Turbine Work?” In this video I show you how to use the blade element momentum theory, BEM, that we discussed in the last videos, to design an efficient wind turbine rotor.
Topics include:
00:33 Lift equation
00:47 Optimum aerodynamic conditions with constant circulation along the span
01:05 How the local wind speed and angle vary along the length of the blade
01:22 How to change the chord and twist angle along the blade span
01:50 Why designers normally modify the chord distribution to have smaller chords at the root
02:28 The torque equation and why the tip's aerodynamics is more important than the root
02:52 What happens if you use a turbine at a different wind speed than it was designed for
03:46 How variable speed turbines can operate efficiently over a wind range of wind speeds
04:18 What is tip speed ratio (TSR) and why is it important to wind turbine designers?
04:48 Blade solidity
06:07 How to find a starting point in the wind turbine blade design process
07:22 Why are wind turbine blades getting so skinny?
07:54 Reducing wind turbine noise by limiting rotational speed
08:29The different requirements of aerofoils at the root versus tip of the blade
Check out part one and two of my “How Does a Wind Turbine Work?” series where I go through the mechanical engineering and aerodynamic theory needed to understand how a wind turbine works and design a wind turbine blade:
How Much Energy is in the Wind?
https://www.youtube.com/watch?v=7-awFXqisYA&t=7s
How to Calculate Wind Turbine Power Output: Blade Element Momentum Method
https://youtu.be/o6BCnhubbiQ
If you want to follow the derivations I mentioned in this video then check out section 3.7.2 of Burton's "Wind Energy Handbook."
Available to buy from Amazon (affiliate link), or your university library probably has it!
https://amzn.to/32Pb1fh
The optimum aerodynamic design equation at 6:10 has the following parameters:
sigma_r = chord solidity at the radial location (chord length divided by swept circumference at that radial location)
lambda = tip speed ratio (tip speed due to blade rotation (radial location times rotational speed) divided by wind speed)
C_l = local lift coefficient
mu = r/R (radial location divided by radius)
133 comments