Araştırma Makalesi
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Yıl 2022, Cilt 38, Sayı 3, 676 - 684, 30.12.2022

Öz

Kaynakça

  • [1] M. Seyednourani, M. Yildiz, and H. S. Sas, “A two-stage optimization methodology for gate and vent locations and distribution media layout for liquid composite molding process,” Compos. Part A Appl. Sci. Manuf., vol. 149, no. June, p. 106522, 2021, doi: 10.1016/j.compositesa.2021.106522.
  • [2] S. Jiang, C. Zhang, and B. Wang, “Optimum arrangement of gate and vent locations for RTM process design using a mesh distance-based approach,” Compos. Part A Appl. Sci. Manuf., vol. 33, no. 4, pp. 471–481, Apr. 2002, doi: 10.1016/S1359-835X(01)00146-4.
  • [3] H. S. Sas and M. Erdal, “Modeling of particle–resin suspension impregnation in compression resin transfer molding of particle-filled, continuous fiber reinforced composites,” Heat Mass Transf., vol. 50, no. 3, pp. 397–414, Mar. 2014, doi: 10.1007/s00231-013-1275-z.
  • [4] T. S. Lundström, B. R. Gebart, and E. Sandlund, “In-plane permeability measurements on fiber reinforcements by the multi-cavity parallel flow technique,” Polym. Compos., vol. 20, no. 1, pp. 146–154, Feb. 1999, doi: 10.1002/PC.10342.
  • [5] C. Di Fratta, G. Koutsoukis, F. Klunker, F. Trochu, and P. Ermanni, “Characterization of anisotropic permeability from flow front angle measurements,” Polym. Compos., vol. 37, no. 7, pp. 2037–2052, Jul. 2016, doi: 10.1002/PC.23382.
  • [6] K. K. Han, C. W. Lee, and B. P. Rice, “Measurements of the permeability of fiber preforms and applications,” Composites Science and Technology, vol. 60, no. 12–13. pp. 2435–2441, 2000, doi: 10.1016/S0266-3538(00)00037-3.
  • [7] P. Ferland, D. Guittard, and F. Trochu, “Concurrent methods for permeability measurement in resin transfer molding,” Polym. Compos., vol. 17, no. 1, pp. 149–158, Feb. 1996, doi: 10.1002/PC.10600.
  • [8] J. R. Weitzenböck, R. A. Shenoi, and P. A. Wilson, “A unified approach to determine principal permeability of fibrous porous media,” Polym. Compos., vol. 23, no. 6, pp. 1132–1150, Dec. 2002, doi: 10.1002/PC.10507.
  • [9] E. Fauster et al., “Image processing and data evaluation algorithms for reproducible optical in-plane permeability characterization by radial flow experiments:,” https://doi.org/10.1177/0021998318780209, vol. 53, no. 1, pp. 45–63, Jun. 2018, doi: 10.1177/0021998318780209.
  • [10] B. R. Gebart and P. Lidström, “Measurement of in-plane permeability of anisotropic fiber reinforcements,” Polym. Compos., vol. 17, no. 1, pp. 43–51, 1996, doi: 10.1002/PC.10589.
  • [11] T. S. Lundström, R. Stenberg, R. Bergström, H. Partanen, and P. A. Birkeland, “In-plane permeability measurements: a nordic round-robin study,” Compos. Part A Appl. Sci. Manuf., vol. 31, no. 1, pp. 29–43, Jan. 2000, doi: 10.1016/S1359-835X(99)00058-5.
  • [12] R. S. Parnas and A. J. Salem, “A comparison of the unidirectional and radial in-plane flow of fluids through woven composite reinforcements,” Polym. Compos., vol. 14, no. 5, pp. 383–394, Oct. 1993, doi: 10.1002/PC.750140504.
  • [13] N. Vernet et al., “Experimental determination of the permeability of engineering textiles: Benchmark II,” Compos. Part A Appl. Sci. Manuf., vol. 61, pp. 172–184, 2014, doi: 10.1016/j.compositesa.2014.02.010.
  • [14] W. R. Hwang and S. G. Advani, “Numerical simulations of Stokes–Brinkman equations for permeability prediction of dual scale fibrous porous media,” Phys. Fluids, vol. 22, no. 11, p. 113101, Nov. 2010, doi: 10.1063/1.3484273.
  • [15] K. Okonkwo, P. Simacek, S. G. Advani, and R. S. Parnas, “Characterization of 3D fiber preform permeability tensor in radial flow using an inverse algorithm based on sensors and simulation,” Compos. Part A Appl. Sci. Manuf., vol. 42, no. 10, pp. 1283–1292, 2011, doi: 10.1016/j.compositesa.2011.05.010.
  • [16] M. Yun, H. Sas, P. Simacek, and S. G. Advani, “Characterization of 3D fabric permeability with skew terms,” Compos. Part A Appl. Sci. Manuf., vol. 97, pp. 51–59, 2017, doi: 10.1016/j.compositesa.2016.12.030.
  • [17] A. Gokce, M. Chohra, S. G. Advani, and S. M. Walsh, “Permeability estimation algorithm to simultaneously characterize the distribution media and the fabric preform in vacuum assisted resin transfer molding process,” Compos. Sci. Technol., vol. 65, no. 14, pp. 2129–2139, Nov. 2005, doi: 10.1016/j.compscitech.2005.05.012.
  • [18] J. Lugo, P. Simacek, and S. G. Advani, “Analytic method to estimate multiple equivalent permeability components from a single rectilinear experiment in liquid composite molding processes,” Composites Part A: Applied Science and Manufacturing, vol. 67. pp. 157–170, 2014, doi: 10.1016/j.compositesa.2014.08.031.
  • [19] P. Šimáček and S. G. Advani, “Desirable features in mold filling simulations for liquid composite molding processes,” Polym. Compos., vol. 25, no. 4, pp. 355–367, 2004, doi: 10.1002/pc.20029.
  • [20] S. Bickerton, H. C. Stadtfeld, K. V Steiner, and S. G. Advani, “Design and application of actively controlled injection schemes for resin-transfer molding,” Compos. Sci. Technol., vol. 61, no. 11, pp. 1625–1637, Aug. 2001, doi: 10.1016/S0266-3538(01)00064-1.
  • [21] E. Sozer, S. Bickerton, and S. . Advani, “On-line strategic control of liquid composite mould filling process,” Compos. Part A Appl. Sci. Manuf., vol. 31, no. 12, pp. 1383–1394, Dec. 2000, doi: 10.1016/S1359-835X(00)00060-9.
  • [22] R. Eberhart and James Kennedy, “A New Optimizer Using Particle Swarm Theory,” Int. Symp. Micro Mach. Hum. Sci., pp. 39–43, 1999, [Online]. Available: https://bytebucket.org/
  • [23] W.-B. Young and C.-L. Lai, “Analysis of the edge effect in resin transfer molding,” Compos. Part A Appl. Sci. Manuf., vol. 28, no. 9–10, pp. 817–822, Jan. 1997, doi: 10.1016/S1359-835X(97)00034-1.
  • [24] S. Bickerton, S. G. Advani, R. V. Mohan, and D. R. Shires, “Experimental analysis and numerical modeling of flow channel effects in resin transfer molding,” Polym. Compos., vol. 21, no. 1, pp. 134–153, Feb. 2000, doi: 10.1002/pc.10172.

Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization

Yıl 2022, Cilt 38, Sayı 3, 676 - 684, 30.12.2022

Öz

Flow simulations are performed to analyze the resin flow behavior through fibrous reinforcements to promote void-free composite manufacturing with excellent mechanical properties. These flow simulations require an essential parameter known as permeability tensor which is defined as the resistance to the flow of resin due to fibrous reinforcement. This study proposes a key strategy to determine all in-plane permeability components from a single rectilinear flow experiment. The method is based on introducing intentional disturbance to the mold domain, which transforms the one-dimensional flow into a two-dimensional flow. The process is divided into two steps, the experimental determination of flow arrival times at designated locations within the mold domain and comparing it to the numerical flow arrival times (obtained using LIMS software) via the residual sum of squares (RSS). An optimization algorithm based on particle swarm optimization (PSO) is established to reduce the RSS to get accurate permeability predictions. The validation study of the proposed strategy has been practiced for three cases. The results show that this method can effectively characterize the in-plane permeability components from a single rectilinear injection experiment.

Kaynakça

  • [1] M. Seyednourani, M. Yildiz, and H. S. Sas, “A two-stage optimization methodology for gate and vent locations and distribution media layout for liquid composite molding process,” Compos. Part A Appl. Sci. Manuf., vol. 149, no. June, p. 106522, 2021, doi: 10.1016/j.compositesa.2021.106522.
  • [2] S. Jiang, C. Zhang, and B. Wang, “Optimum arrangement of gate and vent locations for RTM process design using a mesh distance-based approach,” Compos. Part A Appl. Sci. Manuf., vol. 33, no. 4, pp. 471–481, Apr. 2002, doi: 10.1016/S1359-835X(01)00146-4.
  • [3] H. S. Sas and M. Erdal, “Modeling of particle–resin suspension impregnation in compression resin transfer molding of particle-filled, continuous fiber reinforced composites,” Heat Mass Transf., vol. 50, no. 3, pp. 397–414, Mar. 2014, doi: 10.1007/s00231-013-1275-z.
  • [4] T. S. Lundström, B. R. Gebart, and E. Sandlund, “In-plane permeability measurements on fiber reinforcements by the multi-cavity parallel flow technique,” Polym. Compos., vol. 20, no. 1, pp. 146–154, Feb. 1999, doi: 10.1002/PC.10342.
  • [5] C. Di Fratta, G. Koutsoukis, F. Klunker, F. Trochu, and P. Ermanni, “Characterization of anisotropic permeability from flow front angle measurements,” Polym. Compos., vol. 37, no. 7, pp. 2037–2052, Jul. 2016, doi: 10.1002/PC.23382.
  • [6] K. K. Han, C. W. Lee, and B. P. Rice, “Measurements of the permeability of fiber preforms and applications,” Composites Science and Technology, vol. 60, no. 12–13. pp. 2435–2441, 2000, doi: 10.1016/S0266-3538(00)00037-3.
  • [7] P. Ferland, D. Guittard, and F. Trochu, “Concurrent methods for permeability measurement in resin transfer molding,” Polym. Compos., vol. 17, no. 1, pp. 149–158, Feb. 1996, doi: 10.1002/PC.10600.
  • [8] J. R. Weitzenböck, R. A. Shenoi, and P. A. Wilson, “A unified approach to determine principal permeability of fibrous porous media,” Polym. Compos., vol. 23, no. 6, pp. 1132–1150, Dec. 2002, doi: 10.1002/PC.10507.
  • [9] E. Fauster et al., “Image processing and data evaluation algorithms for reproducible optical in-plane permeability characterization by radial flow experiments:,” https://doi.org/10.1177/0021998318780209, vol. 53, no. 1, pp. 45–63, Jun. 2018, doi: 10.1177/0021998318780209.
  • [10] B. R. Gebart and P. Lidström, “Measurement of in-plane permeability of anisotropic fiber reinforcements,” Polym. Compos., vol. 17, no. 1, pp. 43–51, 1996, doi: 10.1002/PC.10589.
  • [11] T. S. Lundström, R. Stenberg, R. Bergström, H. Partanen, and P. A. Birkeland, “In-plane permeability measurements: a nordic round-robin study,” Compos. Part A Appl. Sci. Manuf., vol. 31, no. 1, pp. 29–43, Jan. 2000, doi: 10.1016/S1359-835X(99)00058-5.
  • [12] R. S. Parnas and A. J. Salem, “A comparison of the unidirectional and radial in-plane flow of fluids through woven composite reinforcements,” Polym. Compos., vol. 14, no. 5, pp. 383–394, Oct. 1993, doi: 10.1002/PC.750140504.
  • [13] N. Vernet et al., “Experimental determination of the permeability of engineering textiles: Benchmark II,” Compos. Part A Appl. Sci. Manuf., vol. 61, pp. 172–184, 2014, doi: 10.1016/j.compositesa.2014.02.010.
  • [14] W. R. Hwang and S. G. Advani, “Numerical simulations of Stokes–Brinkman equations for permeability prediction of dual scale fibrous porous media,” Phys. Fluids, vol. 22, no. 11, p. 113101, Nov. 2010, doi: 10.1063/1.3484273.
  • [15] K. Okonkwo, P. Simacek, S. G. Advani, and R. S. Parnas, “Characterization of 3D fiber preform permeability tensor in radial flow using an inverse algorithm based on sensors and simulation,” Compos. Part A Appl. Sci. Manuf., vol. 42, no. 10, pp. 1283–1292, 2011, doi: 10.1016/j.compositesa.2011.05.010.
  • [16] M. Yun, H. Sas, P. Simacek, and S. G. Advani, “Characterization of 3D fabric permeability with skew terms,” Compos. Part A Appl. Sci. Manuf., vol. 97, pp. 51–59, 2017, doi: 10.1016/j.compositesa.2016.12.030.
  • [17] A. Gokce, M. Chohra, S. G. Advani, and S. M. Walsh, “Permeability estimation algorithm to simultaneously characterize the distribution media and the fabric preform in vacuum assisted resin transfer molding process,” Compos. Sci. Technol., vol. 65, no. 14, pp. 2129–2139, Nov. 2005, doi: 10.1016/j.compscitech.2005.05.012.
  • [18] J. Lugo, P. Simacek, and S. G. Advani, “Analytic method to estimate multiple equivalent permeability components from a single rectilinear experiment in liquid composite molding processes,” Composites Part A: Applied Science and Manufacturing, vol. 67. pp. 157–170, 2014, doi: 10.1016/j.compositesa.2014.08.031.
  • [19] P. Šimáček and S. G. Advani, “Desirable features in mold filling simulations for liquid composite molding processes,” Polym. Compos., vol. 25, no. 4, pp. 355–367, 2004, doi: 10.1002/pc.20029.
  • [20] S. Bickerton, H. C. Stadtfeld, K. V Steiner, and S. G. Advani, “Design and application of actively controlled injection schemes for resin-transfer molding,” Compos. Sci. Technol., vol. 61, no. 11, pp. 1625–1637, Aug. 2001, doi: 10.1016/S0266-3538(01)00064-1.
  • [21] E. Sozer, S. Bickerton, and S. . Advani, “On-line strategic control of liquid composite mould filling process,” Compos. Part A Appl. Sci. Manuf., vol. 31, no. 12, pp. 1383–1394, Dec. 2000, doi: 10.1016/S1359-835X(00)00060-9.
  • [22] R. Eberhart and James Kennedy, “A New Optimizer Using Particle Swarm Theory,” Int. Symp. Micro Mach. Hum. Sci., pp. 39–43, 1999, [Online]. Available: https://bytebucket.org/
  • [23] W.-B. Young and C.-L. Lai, “Analysis of the edge effect in resin transfer molding,” Compos. Part A Appl. Sci. Manuf., vol. 28, no. 9–10, pp. 817–822, Jan. 1997, doi: 10.1016/S1359-835X(97)00034-1.
  • [24] S. Bickerton, S. G. Advani, R. V. Mohan, and D. R. Shires, “Experimental analysis and numerical modeling of flow channel effects in resin transfer molding,” Polym. Compos., vol. 21, no. 1, pp. 134–153, Feb. 2000, doi: 10.1002/pc.10172.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Salman ZAFAR>
Sabanci University
0000-0002-8355-1099
Türkiye


Hatice Sinem SAS> (Sorumlu Yazar)
Sabancı University
0000-0002-5179-2509
Türkiye

Yayımlanma Tarihi 30 Aralık 2022
Yayınlandığı Sayı Yıl 2022, Cilt 38, Sayı 3

Kaynak Göster

Bibtex @araştırma makalesi { erciyesfen1172211, journal = {Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi}, issn = {1012-2354}, address = {ERCİYES ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ 38039 Kayseri, TÜRKİYE}, publisher = {Erciyes Üniversitesi}, year = {2022}, volume = {38}, number = {3}, pages = {676 - 684}, title = {Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization}, key = {cite}, author = {Zafar, Salman and Sas, Hatice Sinem} }
APA Zafar, S. & Sas, H. S. (2022). Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization . Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi , 38 (3) , 676-684 . Retrieved from https://dergipark.org.tr/tr/pub/erciyesfen/issue/74713/1172211
MLA Zafar, S. , Sas, H. S. "Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization" . Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38 (2022 ): 676-684 <https://dergipark.org.tr/tr/pub/erciyesfen/issue/74713/1172211>
Chicago Zafar, S. , Sas, H. S. "Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization". Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38 (2022 ): 676-684
RIS TY - JOUR T1 - Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization AU - SalmanZafar, Hatice SinemSas Y1 - 2022 PY - 2022 N1 - DO - T2 - Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 676 EP - 684 VL - 38 IS - 3 SN - 1012-2354- M3 - UR - Y2 - 2022 ER -
EndNote %0 Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization %A Salman Zafar , Hatice Sinem Sas %T Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization %D 2022 %J Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi %P 1012-2354- %V 38 %N 3 %R %U
ISNAD Zafar, Salman , Sas, Hatice Sinem . "Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization". Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 38 / 3 (Aralık 2022): 676-684 .
AMA Zafar S. , Sas H. S. Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2022; 38(3): 676-684.
Vancouver Zafar S. , Sas H. S. Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi. 2022; 38(3): 676-684.
IEEE S. Zafar ve H. S. Sas , "Numerical permeability tensor characterization of fibrous reinforcement through 1D flow analysis using particle swarm optimization", Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, c. 38, sayı. 3, ss. 676-684, Ara. 2022

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