High-Pressure Water Spray Research Based on Neural Network

Rui Li; Zi Ming Kou

doi:10.4028/www.scientific.net/AMM.29-32.138

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Application of Digital Image Monitoring System in Strain Measurement of Cantilever Steel Beam
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Study on Theoretical Analysis and Numerical Simulation of Smoke Flow during Gas Explosion
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Experimental Study on Integrated Performance for Flower Baffle Heat Exchanger’s Distance between Two Flower Baffles
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High-Pressure Water Spray Research Based on Neural Network
p.138

On the Numerical Study of Bubbly Flow Created by Ventilated Cavity
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Numerical Analysis of Joint Rock Shear Properties Considering Joint Geometrical Characters
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 29-32High-Pressure Water Spray Research Based on Neural...

High-Pressure Water Spray Research Based on Neural Network

Abstract:

The spray cleaning method is important and universal in many industrial processes and other occasion. Because the size of the waterdrop is one of key factors for cleaning, this paper not only studied the relationship between the size of waterdrop and other influencing factors, but also researched the forecasted method for the size of waterdrop. In lab, by measuring the size of the waterdrop, jetted by one kind of nozzle, data were acquired and were used to train the Back Propagation Neural Network ( BPNN ). Through comparing those diameters, between measured in lab and calculated by BPNN after trained. It was acquired that the maximum errors was smaller than 1.62%, between the computed results and the factual measured ones. The experimental results showed that BPNN is an effective tool to predict the variation of the non-linear waterdrop diameter.

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Applied Mechanics And Mechanical Engineering

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Periodical:

Applied Mechanics and Materials (Volumes 29-32)

Pages:

138-142

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.29-32.138

Citation:

Cite this paper

Online since:

August 2010

Authors:

Rui Li, Zi Ming Kou

Keywords:

BP Neural Network (BPNN), Coal Chemical Industry Region, Fen Chew, Spraying Cleaning, Waterdrop Diameter

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References

[1] ZHANG Jing, Research on the designing of multi-spots bending press and the simulation research of its'key Essential element, " Master, s thesis, Taiyuan University of Technology, Taiyuan, China, (2009).

Google Scholar

[2] QIN Tingmo, Atomization Purging Method, Electroplating & PollutionControl, vol. 8, (1988), pp.26-26.

Google Scholar

[3] Sun Zhenhai and Han Ruijin, Numerical Study on Hydrodynamic Forces for Micro ParticleDetachment by Droplet Impac, JOURNAL OF SEMICONDUCTORS, Vol. 29, (2008), pp.1081-1087.

Google Scholar

[4] CHEN Xiang-guang, PEI Xu-dong, Artificial neural network technology and application, China Electric Power Press, Beijing, (2003).

Google Scholar

[5] NIU Quan-zhen, STUDY ON DESIGN OF MINE WIND-WATER ATOMIZING ASPIRATING DEVICE AND FLOW FIELD EMULATION, Taiyuan University of Technology, Taiyuan, (2007).

Google Scholar

[6] MA Su-ping, KOU Zi-ming, Study and Analysis of the Efficiency on Spray for Suppressing Airborne Dust, JOURNAL OF TAIYUAN UNIVERSITY OF TECHNOLOG, Taiyuan University of Technology, Taiyuan, vol 5, (2006), pp.327-330.

DOI: 10.7498/aps.63.104701

Google Scholar

[7] LI Rui and KOU Ziming, Research on Fogdrop Diameter based on Neural Network, International Conference Measuring Technology and Mechatronic Automation (ICMTMA 09) , IEEE CS Press, Apr. (2009), pp.285-288.

DOI: 10.1109/icmtma.2009.154

Google Scholar

[8] LI Rui and KOU Ziming, Atomization Cleaning Rate Research for Mold Box Based on Adaptive Neural Fuzzy Inference System, IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 09) , IEEE Press, November. (2009).

DOI: 10.1109/icicisys.2009.5357853

Google Scholar