Direct numerical simulation of noninvasive channel healing in electrical field
KAUST DepartmentComputational Transport Phenomena Lab
Physical Sciences and Engineering (PSE) Division
KAUST Grant NumberBAS/1/1351-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/626260
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AbstractNoninvasive channel healing is a new idea to repair the broken pipe wall, using external electric fields to drive iron particles to the destination. The repair can be done in the normal operation of the pipe flow without any shutdown of the pipeline so that this method can be a potentially efficient and safe technology of pipe healing. However, the real application needs full knowledge of healing details. Numerical simulation is an effective method. Thus, in this research, we first established a numerical model for noninvasive channel healing technology to represent fluid–particle interaction. The iron particles can be attached to a cracking area by external electrostatic forces or can also be detached by mechanical forces from the fluid. When enough particles are permanently attached on the cracking area, the pipe wall can be healed. The numerical criterion of the permanent attachment is discussed. A fully three-dimensional finite difference framework of direct numerical simulation is established and applied to different cases to simulate the full process of channel healing. The impact of Reynolds number and particle concentration on the healing process is discussed. This numerical investigation provides valuable reference and tools for further simulation of real pipe healing in engineering.
CitationWang Y, Sun S (2017) Direct numerical simulation of noninvasive channel healing in electrical field. Advances in Mechanical Engineering 9: 168781401772328. Available: http://dx.doi.org/10.1177/1687814017723282.
SponsorsThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by National Natural Science Foundation of China (NSFC) (No.51576210), funding from King Abdullah University of Science and Technology (KAUST) through the grant BAS/1/1351-01-01 and Science Foundation of China University of Petroleum-Beijing (No. 2462015BJB03, No. 2462015YQ0409, No. C201602).
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