This document summarizes a study that aimed to optimize the blade profile of Francis turbines to minimize sediment erosion. a blade profile with higher loading
Blade profile and camber line are formed by NUPBS curve, and a blade surface is created by connecting several curves along the spanwise direction. To adopt the
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.
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.
An optimization algorithm is proposed and applied to the runner of a low specific speed Francis turbine, with an optimization strategy specifically constructed
# 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.
The establishment of the link between the optimized blade angle and specific speed can provide a turbine model with increased efficiency.
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). More about SimScale: https://hubs.la/Q01lJ_Np0 ==========Follow us on social========== LinkedIn: https://hubs.la/Q01cDsPX0 Facebook: https://hubs.la/Q01cDtKN0 Twitter: https://hubs.la/Q01cDv2y0 Instagram: https://hubs.la/Q01cDvb40 Instagram (Life at SimScale): https://hubs.la/Q01cDvrz0 13 comments