Optimization of the co-closing law of guide vanes and blades for bulb turbines based on CFD

The load rejection transient process of bulb turbine units is critical to safety of hydropower stations,and determining appropriate closing laws of guide vanes(GVs)and runner blades(RBs)for this process is of significance.In this study,we proposed a procedure to optimize the co-closing law of GVs and RBs by using computational fluid dynamics(CFD),combined with the design of experiment(DOE)method,approximation model,and genetic optimization algorithm.The sensitivity of closing law parameters on the histories of head,speed,and thrust was analyzed,and a two-stage GVs’closing law associating with a linear RBs’closing law was proposed.The results show that GVs dominate the transient characteristics by controlling the change of discharge.Speeding GVs’first-stage closing speed while shortening first-stage closing time can not only significantly reduce the maximum rotational speed but also suppress the water hammer pressure;slowing GVs’second-stage closing speed is conducive to controlling the maximum reverse axial force.RBs directly affect the runner force.Slowing RBs’closing speed can further reduce the rotational speed and the maximum reverse axial force.The safety margin of each control parameter,flow patterns,and pressure pulsations of a practical hydropower station were all improved after optimization,demonstrating the effectiveness of this method.

Optimization of closing law of turbine guide vanes based on improved artificial ecosystem algorithm

Optimization of the closing law of the guide vane is the most economical and efficient way to reduce the risk incurred by pressure and speed excursions,thus guaranteeing the security of the hydro-turbine and the whole hydraulic network.In order to optimize the closing law of the guide vane of hydraulic turbine,an improved artificial ecosystem optimization algorithm was proposed(IAEO).The reverse learning was used to initialize the population,multi-strategy bound handing schemes was used to improve the algorithm convergence speed.Twenty-three mathematical benchmark functions were used to test the IAEO.Results showed an improvement in the IAEO algorithm convergence speed and a stronger exploration than other algorithms.IAEO algorithm was used to optimize the closing law of the guide vane of hydraulic turbine based on the hydraulic transient calculation.The results showed that the maximum pressure in the spiral casing inlet,the minimum pressure in the draft tube inlet and the maximum speed all meet the design requirements by use of the closing law of the guide vane optimized by IAEO.Compared with other algorithms such as particle swarm optimization(PSO),artificial ecosystem-based optimization(AEO)and grey wolf optimizer(GWO),the closing law of the guide vane optimized by IAEO algorithm was proved to be of great advantages in distribution of safety margin of each optimization goal.