Abstract
Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions. To solve that problem, an optimization strategy has been designed to widen the high-efficiency range of the double-suction centrifugal pump at design (Qd) and nondesign flow conditions. An orthogonal experimental scheme is therefore designed with the impeller hub and shroud angles as the decision variables. Then, the “efficiency-house” theory is introduced to convert the multiple objectives into a single optimization target. A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. The pump performance is predicted using three-dimensional Reynolds-averaged Navier–Stokes equations which is validated by the experimental test. With ANN, Kriging, and a hybrid approximate model, an optimization strategy is built to widen the high-efficiency range of the double-suction centrifugal pump at overload conditions by 1.63%, 1.95%, and 4.94% for flow conditions 0.8Qd, 1.0Qd, and 1.2Qd, respectively. A higher fitting accuracy is achieved for the hybrid approximation model compared with the single approximation model. A complete optimization platform based on efficiency-house and the hybrid approximation model is built to optimize the model double-suction centrifugal pump, and the results are satisfactory.
Highlights
Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions
A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. e pump performance is predicted using three-dimensional Reynolds-averaged Navier–Stokes equations which is validated by the experimental test
Centrifugal pumps are a group of turbomachinery with several applications ranging from domestic use to power plants and chemical and agricultural industries [3, 4]. e double-suction centrifugal pump is a highly efficient pump as compared with the end suction pumps; like all centrifugal pumps, it has a common problem of efficiency reduction at nondesign flow conditions which translates into energy costs [5, 6]. is requires an effective design optimization strategy to improve pump performance and save energy costs at the same time for the benefits of the manufacturer and the consumer
Summary
Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions. An optimization strategy has been designed to widen the high-efficiency range of the double-suction centrifugal pump at design (Qd) and nondesign flow conditions. With ANN, Kriging, and a hybrid approximate model, an optimization strategy is built to widen the high-efficiency range of the double-suction centrifugal pump at overload conditions by 1.63%, 1.95%, and 4.94% for flow conditions 0.8Qd, 1.0Qd, and 1.2Qd, respectively. E theory of pump design has over the years advanced from the one-dimensional Euler equation and empirical correction theory to a three-dimensional design theory such as direct design with numerical simulation During this period, several optimization strategies have been applied by various researchers. Theoretical models of net positive suction head (NPSH) and efficiency have been used for single-objective optimization in centrifugal and mixed-flow pumps [7, 8]. Complexity approach has been widely applied to optimize pump impellers for performance improvement within a shorter design period [9, 10]
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