Multi-objective optimization of an axial flow hydraulic turbine with a collection device to be installed in an open channel

Sediment erosion poses a significant challenge for hydraulic turbines in sediment-laden rivers. This paper investigates the erosion patterns in high-head Francis turbines using numerical simulations. It analyzes how sediment concentration, particle size, and operating conditions affect wear on turbine components. The results identify the trailing edges of the guide vanes, the intersection of runner blade trailing edge, and runner band as primary erosion zones. Higher sediment concentrations increase erosion intensity and extend the erosion range upstream. Small particles uniformly affect both pressure and suction sides of the blades, while larger particles concentrate erosion near the runner band trailing edges. The flow rate has a greater impact on runner blade erosion than water head, with lower flow rates reducing pressure side erosion but initially increasing, then decreasing suction side erosion. Additionally, the study proposes a multi-objective, multi-condition optimization design method that balances erosion resistance and energy efficiency. The optimized runner reduces the maximum erosion rate by 23.91% while limiting the weighted efficiency loss to under 0.1%. The improved runner design reduces high erosion areas on both blade sides, particularly decreasing particle impact speeds near the trailing edges. Sensitivity analysis reveals a trade-off between minimizing erosion and maintaining hydraulic efficiency, highlighting the importance of span height at 75% for controlling erosion. Changes in blade circumference angle generally reduce erosion but may also lower efficiency. Overall, this research demonstrates a successful reduction in Francis turbine erosion while preserving hydraulic efficiency, offering valuable guidance for anti-erosion turbine design in sediment-heavy environments.

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Grey Wolf Optimization Algorithm Based on Follow-Controlled Learning Strategy

Zhang,

Chen,

Zhang

et al. 2023

IEEE Access

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This paper analyzes the OBL strategy's impact on optimizing the GWO algorithm and identifies three shortcomings. the specific limitations of the OBL optimization approach. To address these three shortcomings and enhance both global optimization and local exploration capabilities of GWO, this paper introduces a follow-controlled opposition learning strategy. then, the paper analyzes the control parameter C of the grey wolf algorithm to investigate its impact on global optimization and local exploration. Based on these properties, a new control parameter C is proposed. The proposed learning strategy and control parameter C are introduced into the traditional grey wolf algorithm to obtain the FCGWO algorithm. Finally, this paper conducts a comparative analysis of the FCGWO algorithm in comparison to other meta-heuristic algorithms, as well as the enhanced grey wolf algorithm, utilizing 23 benchmark test functions and 2 engineering problems. The results indicate that FCGWO effectively avoids the shortcomings of the traditional OBL, while also outperforming other algorithms significantly in terms of solution quality.

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Multi-objective optimization of an axial flow hydraulic turbine with a collection device to be installed in an open channel

Cited by 3 publications

References 36 publications

Numerical analysis on hydrodynamic performance and wake recovery of helical-bladed hydrokinetic turbine: Impact of section inclination angle

Numerical analysis on hydrodynamic performance and wake recovery of helical-bladed hydrokinetic turbine: Impact of section inclination angle

Erosion assessment and anti-erosion optimization design for Francis turbine

Grey Wolf Optimization Algorithm Based on Follow-Controlled Learning Strategy

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