Please wait a minute...
Submit  |   Chinese  | 
 
Advanced Search
   Home  |  Online Now  |  Current Issue  |  Focus  |  Archive  |  For Authors  |  Journal Information   Open Access  
Submit  |   Chinese  | 
Engineering    2017, Vol. 3 Issue (6) : 888 -891     https://doi.org/10.1016/j.eng.2017.12.004
Research |
Universal Method for the Prediction of Abrasive Waterjet Performance in Mining
Eugene Averin
OOO Skuratovsky Experimental Plant, Tula 300911, Russia
Abstract
Abstract  

Abrasive waterjets (AWJs) can be used in extreme mining conditions for hard rock destruction, due to their ability to effectively cut difficult-to-machine materials with an absence of dust formation. They can also be used for explosion, intrinsic, and fire safety. Every destructible material can be considered as either ductile or brittle in terms of its fracture mechanics. Thus, there is a need for a method to predict the efficiency of cutting with AWJs that is highly accurate irrespective of material. This problem can be solved using the energy conservation approach, which states the proportionality between the material removal volume and the kinetic energy of AWJs. This paper describes a method based on this approach, along with recommendations on reaching the most effective level of destruction. Recommendations are provided regarding rational ranges of values for the relation of abrasive flow rate to water flow rate, standoff distance, and size of abrasive particles. I also provide a parameter to establish the threshold conditions for a material’s destruction initiation based on the temporary-structural approach of fracture mechanics.

Keywords Abrasive waterjet      Energy conservation approach      Depth of cut      Fracture mechanics      Threshold velocity      Mining     
Fund: 
Just Accepted Date: 13 December 2017   Issue Date: 02 March 2018
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Eugene Averin
Cite this article:   
Eugene Averin. Universal Method for the Prediction of Abrasive Waterjet Performance in Mining[J]. Engineering, 2017, 3(6): 888 -891 .
URL:  
http://engineering.org.cn/EN/10.1016/j.eng.2017.12.004     OR     http://engineering.org.cn/EN/Y2017/V3/I6/888
References
Related
[1] Jinhua Wang,Zenghua Huang. The Recent Technological Development of Intelligent Mining in China[J]. Engineering, 2017, 3(4): 439 -444 .
[2] Luis Ribeiro e Sousa, Tiago Miranda, Rita Leal e Sousa, Joaquim Tinoco. The Use of Data Mining Techniques in Rockburst Risk Assessment[J]. Engineering, 2017, 3(4): 552 -558 .
[3] Pathegama G. Ranjith,Jian Zhao,Minghe Ju,Radhika V. S. De Silva,Tharaka D. Rathnaweera,Adheesha K. M. S. Bandara. Opportunities and Challenges in Deep Mining: A Brief Review[J]. Engineering, 2017, 3(4): 546 -551 .
[4] Zhiqiang Yang. Key Technology Research on the Efficient Exploitation and Comprehensive Utilization of Resources in the Deep Jinchuan Nickel Deposit[J]. Engineering, 2017, 3(4): 559 -566 .
[5] Charles Fairhurst. Some Challenges of Deep Mining[J]. Engineering, 2017, 3(4): 527 -537 .
[6] Xia-Ting Feng,Jianpo Liu,Bingrui Chen,Yaxun Xiao,Guangliang Feng,Fengpeng Zhang. Monitoring, Warning, and Control of Rockburst in Deep Metal Mines[J]. Engineering, 2017, 3(4): 538 -545 .
Copyright © 2015 Higher Education Press & Engineering Sciences Press, All Rights Reserved.
京ICP备11030251号-2

 Engineering