Abstract
Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. A fast approach to predicting the Net Positive Suction Head required was applied to perform a multi-objective optimization on the double-suction centrifugal pump. An L32 (84) orthogonal array was designed to evaluate 8 geometrical parameters at 4 levels each. A two-layer feedforward neural network and genetic algorithm was applied to solve the multi-objective problem into pareto solutions. The results were validated by numerical simulation and compared to the original design. The suction performance was improved by 7.26%, 3.9%, 4.5% and 3.8% at flow conditions 0.6Qd, 0.8Qd, 1.0Qd and 1.2Qd respectively. The efficiency increased by 1.53% 1.0Qd and 1.1% at 0.8Qd. The streamline on the blade surface was improved and the vapor volume fraction of the optimized impeller was much smaller than that of the original impeller. This study established a fast approach to cavitation optimization and a parametric database for both hub and shroud blade angles for double suction centrifugal pump optimization design.
Highlights
Over recent years, pump manufacturers have intensified their quest to rapidly develop cost-competitive and high-performance pumps with compact and robust structures to meet consumer’s limitless demands for high end centrifugal pumps, since they offer wide-ranging stable operation both in industrial and agricultural applications
Investigations were done at the design flow rate as per the objectives of the optimization
Artificial neural networks were adopted as the metamodel to build the relationship between the two objective functions and the design variables for optimization
Summary
Pump manufacturers have intensified their quest to rapidly develop cost-competitive and high-performance pumps with compact and robust structures to meet consumer’s limitless demands for high end centrifugal pumps, since they offer wide-ranging stable operation both in industrial and agricultural applications. Recent advances in the design of centrifugal pump systems does require energy efficient and quieter systems, and reductions in design time and lower costs. Engineers have devised optimization methods and algorithms with numerical simulations so that optimization does not rely solely on the designer’s experience [3]. Aside from the optimization algorithms there are other approaches to optimization, such as the experimental design known as the design of experiment (DOE)
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