An optimization on the stacking line of low-pressure axial-flow fan using the surrogate-assistant optimization method

2013 ◽

Vol 655-657 ◽

pp. 435-444

Author(s):

Dong Xia Niu ◽

Xian Yi Meng ◽

Ai Hua Zhu

Keyword(s):

Optimization Design ◽

Lift Coefficient ◽

Axial Flow ◽

Optimization Method ◽

Structure Parameters ◽

Continuous Variables ◽

State Variables ◽

Axial Flow Fan ◽

Installation Angle ◽

Design Variables

In the case of multiple loading conditions, a moving blade adjustable axial flow fan structure parameters are optimized by ANSYS. It is to achieve greater efficiency and less noise for the optimization goal. For different conditions, establish efficiency, noise comprehensive objective function using weighted coefficient method. Select impeller diameter, the wheel hub ratio, leaf number, lift coefficient, speed as design variables, Choose blade installation Angle, the wheel hub place dynamic load coefficient, cascade consistency, allowable safety coefficient as optimization of the state variables. Design variables contain continuous variables and discrete variable. Through the optimization method, we get the optimal structure parameters finally. And at the same time get the corresponding optimal blade installation Angle,under different working conditions.

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Flow characteristics of the sound pressure level and its prediction for a low pressure axial flow fan.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series B ◽

10.1299/kikaib.53.2514 ◽

1987 ◽

Vol 53 (492) ◽

pp. 2514-2520

Author(s):

Yoshio KODAMA ◽

Tohru FUKANO

Keyword(s):

Sound Pressure Level ◽

Sound Pressure ◽

Flow Characteristics ◽

Axial Flow ◽

Low Pressure ◽

Pressure Level ◽

Axial Flow Fan

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Experimental evaluation of energy efficiency and velocity fields on a low-pressure axial flow fan (desktop type)

Energy Efficiency ◽

10.1007/s12053-018-9685-x ◽

2018 ◽

Vol 12 (3) ◽

pp. 697-710

Author(s):

Robson L. Silva ◽

Silvio X. Brito

Keyword(s):

Energy Efficiency ◽

Experimental Evaluation ◽

Axial Flow ◽

Low Pressure ◽

Velocity Fields ◽

Axial Flow Fan

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An optimal design of axial-flow fan blades by the machining method and an artificial neural network

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/0954406021524954 ◽

2002 ◽

Vol 216 (3) ◽

pp. 367-376 ◽

Cited By ~ 5

Author(s):

B-J Lin ◽

C-I Hung ◽

E-J Tang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tool Path ◽

Three Dimensional ◽

Axial Flow ◽

Integrated Approach ◽

Optimization Method ◽

Surface Geometry ◽

Axial Flow Fan ◽

Artificial Neural

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 optimization method 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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