A novel design approach that combines manufacturing process and numerical simulation is proposed for centrifugal pump impellers in this study. We aim to provide a fast design methodology for centrifugal pump impellers...
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A novel design approach that combines manufacturing process and numerical simulation is proposed for centrifugal pump impellers in this study. We aim to provide a fast design methodology for centrifugal pump impellers. The geometric design of impeller is obtained by means of cubic spline and the manufacturing process is based on the generated machining method for five-axis control machine tools. The feature of this study is that the centrifugal impeller design with a well-behaved flow field could practically be manufactured. For the numerical simulation, a commercial CFD software (CFX-TASCflow) is used to solve the three-dimensional Reynolds-averaged Navier-Stokes equations in a rotating cylindrical coordinate system. Besides, an artificial neural network combined with the complex optimization method is also proposed to solve the inverse problem of a centrifugal impeller when the basic geometric parameters and the hub-shroud contours are known, and the expected velocity distribution of blade surface is given. Results of this study show that efficiency and performance of the designed impeller are higher than those of the initial impeller in the same operation condition.
The geometry design and machining of blades for axial-flow fans are important issues because the twisted profile and flowfield of blades are complicated. The rapid design of a blade that performs well and satisfies ma...
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The geometry design and machining of blades for axial-flow fans are important issues because the twisted profile and flowfield of blades are complicated. The rapid design of a blade that performs well and satisfies machining requirements is one of the goals in designing fluid machinery blades. In this study, an integrated approach combining computational fluid dynamics (CFD), an artificial neural network, an optimizationmethod and a machining method is proposed to design a three-dimensional blade for an axial-flow fan. From the machining point of view, the three-dimensional surface geometry of a fan blade can be defined as the swept surface of the tool path created by using the generated machining method. By taking advantage of its powerful learning capability, a back-propagation artificial neural network is used to set up the flowfield models and to forecast the flow performance of the axial-flow fan. The desired optimal blade geometry is obtained by using a complex optimization method.
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