Blade shape optimization and internal-flow characteristics of the backward non-volute centrifugal fan

2021 ◽  
pp. 095765092110527
Author(s):  
Hongjie Zhang ◽  
Zhengdao Wang ◽  
Hui Yang ◽  
Zuchao Zhu ◽  
Yikun Wei
Keyword(s):  
Total Pressure ◽  
Gradient Descent ◽  
Flow Characteristics ◽  
Internal Flow ◽  
Descent Method ◽  
High Flow ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Centrifugal Fan ◽  
Blade Profile

The work proposed the double parameter optimization method of the non-volute centrifugal fan’s blade profile based on the steepest descent method. Total-pressure efficiency improvement at the high-flow area was taken as an optimization objective. A method of applying the steepest descent method to modify the blade profile of backward centrifugal fan is proposed in this paper. The gradient descent direction was analyzed to design the blade profile and obtain the optimal blade profile at a high-flow rate. Besides, numerical simulations were carried out to analyze the aerodynamic performance and the internal flow characteristics of the centrifugal fan by the computational fluid dynamics method. Numerical results showed that the blade profile along the gradient descent was optimized to effectively increase the total pressure and the total pressure efficiency of the original model at the high-flow rate. The steepest descent method for local optimization could improve the fan blade design.

Analysis of Internal Flow Field for Centrifugal Fan in Series

2012 ◽  
Vol 233 ◽  
pp. 96-99
Author(s):  
Ya Jun Fan ◽  
Zhang Xu ◽  
Ding Wensi
Keyword(s):  
Velocity Distribution ◽  
Total Pressure ◽  
Flow Characteristics ◽  
Internal Flow ◽  
Wind Pressure ◽  
Meridian Plane ◽  
Centrifugal Fan ◽  
Fan Performance ◽  
In Series ◽  
The Impact

Centrifugal fan in series with high wind pressure is the key facility of pneumatic transport equipment. To consider the impact of changed conditions on performance of centrifugal fan, internal flow of three-stage centrifugal fan at rated speed in different total pressure conditions is analyzed by CFD software FLUENT6.3 in this paper. Flow characteristics are obtained and the differences of total pressure and velocity distribution in each impeller are analyzed under different conditions, velocity distribution on the meridian plane and section of wind guide plates are compared. Finally, curves of P-Q and P-η at 4600 r/min are forecasted through the analysis of the data, which provide references for reducing impact that condition alteration on fan performance and improving the efficiency of the fan.

scholarly journals A Learning Method for Neural Networks Based on a Pseudoinverse Technique

1996 ◽  
Vol 3 (3) ◽  
pp. 201-209 ◽  
Author(s):  
Chinmoy Pal ◽  
Ichiro Hagiwara ◽  
Naoki Kayaba ◽  
Shin Morishita
Keyword(s):  
Descent Method ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Learning Method ◽  
Theoretical Formulation ◽  
Learning Capability ◽  
Fast Learning ◽  
Backpropagation Method ◽  
Mass Spring ◽  
Exclusive Or

A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

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