Capturing the influence of geometric variations on permeability using a numerical permeability prediction tool

被引:10
|
作者
Swery, Elinor E. [1 ]
Allen, Tom [1 ]
Kelly, Piaras [2 ]
机构
[1] Univ Auckland, Dept Mech Engn, Ctr Adv Composite Mat, Private Bag 92019, Auckland 1142, New Zealand
[2] Univ Auckland, Dept Engn Sci, Auckland, New Zealand
关键词
Permeability; textile modelling; liquid composite moulding; TEXTILE REINFORCEMENTS; INPLANE PERMEABILITY; FABRIC PERMEABILITY; SIMULATION; COMPOSITES; COMPACTION; FLOW; MICROSTRUCTURES; HOMOGENIZATION; ARCHITECTURE;
D O I
10.1177/0731684416669249
中图分类号
TB33 [复合材料];
学科分类号
摘要
An automated tool has been developed for generation of permeability predictions for multi-layered unit cells utilising textile modelling techniques. This tool has been used to predict the permeability tensor of a woven textile. Single-layer predictions were carried out and the predicted permeabilities obtained were in close agreement to the permeability behaviour captured experimentally. The tool was used to capture the effects of textile variability on its permeability, isolating the influence of individual parameters. A complete textile sample was also analysed, predicting its permeability map. The concept of estimating the permeability of a textile with variability using an average single unit cell was explored. The prediction tool was also used to study the effect of preform structure on its permeability, including consideration of the number of layers, ply shift and applied compaction.
引用
收藏
页码:1802 / 1813
页数:12
相关论文
共 50 条
  • [21] Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study
    Niu, Qifei
    Zhang, Chi
    WATER RESOURCES RESEARCH, 2019, 55 (04) : 3107 - 3121
  • [22] Permeability Prediction Using Different Methods in Carbonate Reservoir
    Ramadhan, Ahmad A.
    Kadhim, Fadhil S.
    Mohammed, Noor Al-Huda A.
    Salman, Adyanh K.
    Jabbar, Mariam A.
    PETROLEUM CHEMISTRY, 2024, 64 (07) : 891 - 899
  • [23] Reactive flow and permeability prediction - numerical simulation of complex hydrogeothermal problems
    Bartels, J.
    Clauser, C.
    Kuehn, M.
    Pape, H.
    Schneider, W.
    PETROPHYSICAL PROPERTIES OF CRYSTALLINE ROCKS, 2005, 240 : 133 - 151
  • [24] Prediction of skin permeability using an artificial neural network
    Fu, XC
    Ma, XW
    Liang, WQ
    PHARMAZIE, 2002, 57 (09): : 655 - 656
  • [25] Prediction of corneal permeability using artificial neural networks
    Agatonovic-Kustrin, S
    Evans, A
    Alany, RG
    PHARMAZIE, 2003, 58 (10): : 725 - 729
  • [26] The Prediction of Permeability Using an Artificial Neural Network System
    Pazuki, G. R.
    Nikookar, M.
    Dehnavi, M.
    Al-Anazi, B.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (20) : 2108 - 2113
  • [27] Permeability prediction using stress sensitive petrophysical properties
    Jones, C
    Somerville, JM
    Smart, BGD
    Kirstetter, O
    Hamilton, SA
    Edlmann, KP
    PETROLEUM GEOSCIENCE, 2001, 7 (02) : 211 - 219
  • [28] Study on the Influence of Geometric Characteristics of Grain Membranes on Permeability Properties in Porous Sandstone
    Shi, Run
    Xiao, Huaiguang
    Shao, Chengmeng
    Huang, Mingzheng
    He, Lei
    MEMBRANES, 2021, 11 (08)
  • [29] A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical Proxy SCAL Data
    Zhao, Bochao
    Ratnakar, Ram
    Dindoruk, Birol
    Mohanty, Kishore
    SPE JOURNAL, 2020, 25 (05): : 2749 - 2764
  • [30] A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical Proxy SCAL Data
    Zhao B.
    Ratnakar R.
    Dindoruk B.
    Mohanty K.
    Dindoruk, Birol, 1600, Society of Petroleum Engineers (SPE) (25): : 2749 - 2764