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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.
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simscale.com
article
https://www.simscale.com/blog/francis-turbine-optimization
# How to Optimize a Francis Turbine Design with CFD. BlogMachinery & Industrial EquipmentHow to Optimize a Francis Turbine Design with CFD. A water turbine, with the Kaplan, Pelton, and Francis turbines being the most common ones, is a large rotary machine that works to convert kinetic and potential energy into hydroelectricity. These modern equivalents of the water wheel have been used for over 135 years for industrial power generation, and more recently hydropower energy generation. ## Francis Turbine **What Are Water Turbines Used for Today?**. Low-head hydropower systems are larger, as the water turbine has to be large to achieve a high flow rate while low water pressure is applied across the blades. These turbines are known as axial flow reaction turbines, as they change the pressure of the water as it flows through it. ## Francis Turbine **How Can You Optimize Your Water Turbine Design with CFD?**.
E
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.
A
asmedigitalcollection.asme.org
article
https://asmedigitalcollection.asme.org/fluidsengineering/article/142/10/10121…
by I Iliev · 2020 · Cited by 32 — An optimization algorithm is proposed and applied to the runner of a low specific speed Francis turbine, with an optimization strategy specifically constructed
Y
youtube.com
video
https://www.youtube.com/watch?v=fcE6HV1g2kk
Water Turbine Design Optimization with CFD
SimScale
31200 subscribers
278 likes
29387 views
21 Dec 2018
Francis turbines (which are water turbines) are the modern equivalent of water wheels that have been used over centuries for power generation. These devices are becoming essential for an environmentally-friendly and clean source of power and thus have evolved into complex designs that need to meet certain requirements in terms of performance and power output. This requires an ongoing optimization of the design of different components. Fluid flow simulation (CFD) is an alternative to complex, conventional development processes consisting of design development, prototype construction, and experimental validation.
In this webinar, you will learn how the SimScale cloud-based simulation platform enables every engineer in the world to leverage the potential of CFD for their own projects in the field of power generation via water turbines by using a standard web browser (no installation or special hardware required).
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13 comments
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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
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.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0960148124019906
by MS Roh · 2025 · Cited by 12 — 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.