ЧИСЛЕННОЕ И ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ ОПТИМИЗАЦИИ СТРУКТУРЫ УГЛЕПЛАСТИКА ПО КОЭФФИЦИЕНТУ ТЕПЛОВОГО РАСШИРЕНИЯ
Сведения об авторах
- Сибирский университет науки и технологий, Красноярск, Российская Федерация
- Сибирский университет науки и технологий, Красноярск, Российская Федерация
- Сибирский университет науки и технологий, Красноярск, Российская Федерация
Аннотация
Исследуется оптимизация макроструктуры для достижения стабильно низкого коэффициента теплового расширения αx композита с углеродными волокнами. Для ограничения области поиска предложено необходимое условие существования локальных минимумов αx, выраженное через радиусы гиперсфер в проектном пространстве угловой ориентации слоев, преобразованном алгоритмом РСА. Анализ вариантов структуры, характеризующихся низким αx, показывает различную устойчивость к вариативности свойств единичного слоя композита. Выполнена многокритериальная оптимизация. Целевыми функциями являются математическое ожидание E(αx) и дисперсия Var(αx). Анализ Парето-фронта и функций плотности распределения вероятности позволяет оценить достижимость расчетного αx при заданных условиях вариативности свойств единичного слоя композита. Исследована возможность уменьшения дисперсии распределения αx путем модификации полимерной матрицы многостенными углеродными нанотрубками (МУНТ) в условиях дезориентации армирующих волокон и вариативности свойств единичного слоя. Модификация микроструктуры полимерного композиционного материала позволяет снизить Var(αx) на 91,61 % при объемном соотношении МУНТ до 1 %. Требуемые термомеханические свойства достигаются путем определения ориентации анизотропных слоев. На основе полученных оптимальных структур были изготовлены образцы углепластика с объемным содержанием МУНТ 0, 1 и 2 %. Проведена сканирующая электронная микроскопия с использованием FE–SEM Hitachi S-5500 для проверки однородности распределения и совместимости эпоксидной матрицы и МУНТ. Измерение αx выполнено с помощью термомеханического анализатора TAInstrumentsQ400. Измеренные значения αx находятся в диапазоне от 6,2·10-8 до 1,98·10-7 1/ K. Подход к оптимизации структуры, предложенный в этой статье, позволяет получить набор решений со стабильно низким αx в диапазоне до 1·10-7 1/K. Преобразование проектного пространства угловой ориентации слоев и ограничение области поиска позволило сократить диапазон рассматриваемых решений на 83,9 %.
Ключевые слова
Numerical and experimental study on CFRP structure optimization for coefficient of thermal expansion
This paper explores the optimization macrostructure to reach a stable low coefficient of thermal expansion αx of a composite with carbon fibers. To limit the search area, a necessary condition for the existence of αx local minima is proposed, expressed in terms of the radii of hyperspheres in the design space of the angular orientation of the layers transformed by the PCA algorithm. The analysis of the structure variants characterized by low αx shows different sustainability to lamina properties variability. Multi- criteria optimization was carried out. The objective functions are expectation E(αx) and variance Var(αx). The analysis of Pareto fronts and probability density functions make it possible to estimate the reachability of the calculated αx under given conditions of lamina properties variability. The reduction variance opportunity of αx distribution by modifying the polymer matrix with MWCNTs under conditions of reinforcing fibers disorientation and lamina properties variability is investigated. The microstructure modification of the polymer composite material allows to reduce the Var(αx) by 91.61% with a volume ratio of MWCNTs up to 1%. Requirement thermomechanical properties are reached by determining the orientation of anisotropic layers. Based on the obtained optimal structures, specimens of CFRP with 0, 1 and 2 vol. % MWCNTs were made. Scanning electron microscopy using FE- SEM Hitachi S- 5500 was performed to check the uniformity of distribution and compatibility of the epoxy matrix and MWCNTs. The measurement of αx is determined using a TAlnstrumentsQ400 thermomechanical analyzer. Measured αx of specimens is in the range from 6.2·10- 8 to 1.98·10- 7 1/K. The structure optimization approach proposed in this paper makes it possible to obtain a set of solutions with a consistently low αx in the range up to 1·10- 7 1/K. The transformation of the design space of the layers' orientation angles and the limitation of the search area allowed to reduce the range of solutions under consideration by 83.9%.
Keywords
Литература
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- 27.S.S. Wagh, A.P. More, G.B. Kolekar, M.V. Kulkarni. Study of physical properties of ZnO nanoparticles dispersed in PC/MWCNTs nanocomposites. Polym. Compos., 2019, 40 (S2), pp. E1671-E1678. https://doi.org/10.1002/pc.25099 Scholar
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- 31.S. Ganguli, A. K. Roy, D. P. Anderson. Improved thermal conductivity for chemically functionalized exfoliated graphite/epoxy composites. Carbon N Y, 2008, 46 (5), pp. 806-817. https://doi.org/10.1016/j.carbon.2008.02.008 Scholar
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- 33.R.P. Zhu, C.T. Sun. Effects of Fiber Orientation and Elastic Constants on Coefficients of Thermal Expansion in Laminates. Mech. Adv. Mater. Struct., 10 (2) (2003), pp. 99-107. https://doi.org/10.1080/15376490306733 Scholar
- 34.Yoon KJ, Kim J-S. Prediction of Thermal Expansion Properties of Carbon/Epoxy Laminates for Temperature Variation. Journal of Composite Materials. 2000;34(2):90-100. https://doi.org/10.1177/002199830003400201. Scholar
- 35.Polymer nanocomposites. MRS Bull, 2007, № 32, pp. 314-319. https://doi.org/10.1557/mrs2007.229. Scholar
- 36.Chang T., Gao H. Size-dependent elastic properties of a single-walled carbon nanotube via a molecular mechanics model. J MechPhys Solids, 2003, №. 51, pp. 1059-74. https://doi.org/10.1016/S0022-5096. Scholar
- 37.J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python. IEEE Access, Vol. 8, pp. 89497-89509, 2020. Polym. Adv. Technol., 2017, 28 (11), pp. 1375-1381. https://doi.org/10.1109/ACCESS.2020.2990567 Scholar
- 38.Blank, Julian and Kalyanmoy Deb. A. Running Performance Metric and Termination Criterion for Evaluating Evolutionary Multi- and Many-objective Optimization Algorithms. 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-8. https://doi.org/10.1109/CEC48606.2020.9185546. Scholar
- 1.Anaya L., Vicente W., Pavanello R. Minimization of the Effective Thermal Expansion Coefficient of Composite Material Using a Multi-scale Topology Optimization Method. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization / ed. Rodrigues H.C. et al. Cham: Springer International Publishing, 2019, pp. 1055-1060. https://doi.org/10.1007/978-3-319-97773-7_91 Scholar
- 2.Zhengchun D. et al. Design and application of composite platform with extreme low thermal deformation for satellite. Compos. Struct. Elsevier Ltd, 2016, Vol. 152, pp. 693-703. https://doi.org/10.1016/j.compstruct.2016.05.073 Scholar
- 3.Catapano A., Desmorat B., Vannucci P. Stiffness and Strength Optimization of the Anisotropy Distribution for Laminated Structures. J. Optim. Theory Appl. 2015, Vol. 167, No 1, pp. 118- 146. https://doi.org/10.1007/s10957- 014- 0693- 5 Scholar
- 4.Kim D. et al. Topology optimization of functionally graded anisotropic composite structures using homogenization design method. Comput. Methods Appl. Mech. Eng. Elsevier B.V., 2020, Vol. 369, pp. 113220. https://doi.org/10.1016/j.cma.2020.113220 Scholar
- 5.Schaedler de Almeida F. Optimization of laminated composite structures using harmony search algorithm. Compos. Struct. Elsevier Ltd, 2019, Vol. 221, pp. 110852. https://doi.org/10.1016/j.compstruct.2019.04.024 Scholar
- 6.Peng X. et al. Multiple- scale uncertainty optimization design of hybrid composite structures based on neural network and genetic algorithm. Compos. Struct. Elsevier Ltd, 2020, Vol. 262, pp. 113371. https://doi.org/10.1016/j.compstruct.2020.113371 Scholar
- 7.Hao P. et al. Efficient reliability- based design optimization of composite structures via isogeometric analysis // Reliab. Eng. Syst. Saf. Elsevier Ltd, 2021, Vol. 209, pp. 107465. https://doi.org/10.1016/j.ress.2021.107465 Scholar
- 8.das Neves Carneiro G., Conceição António C. Dimensional reduction applied to the reliability- based robust design optimization of composite structures. Compos. Struct. Elsevier Ltd, 2021, Vol. 255, pp. 112937. https://doi.org/10.1016/j.compstruct.2020.112937 Scholar
- 9.Sigmund O., Torquato S. Design of materials with extreme thermal expansion using a three- phase topology optimization method. J. Mech. Phys. Solids. Elsevier Ltd, 1997, Vol. 45, No 6, pp. 1037- 1067. https://doi.org/10.1016/S0022- 5096(96)00114- 7 Scholar
- 10.Q.S. Sun, Y.D. Feng, J. Guo, et al. High performance epoxy resin with ultralow coefficient of thermal expansion cured by conformation- switchable multi- functional agent. Chemical Engineering Journal, 2022, Vol. 450, pp. 138295. https://doi.org/10.1016/j.cej.2022.138295 Scholar
- 11.S.R. Wang, Z.Y. Liang, P. Gonnet, Y.H. Liao, B. Wang, C. Zhang. Effect of nanotube functionalization on the coefficient of thermal expansion of nanocomposites. Adv. Funct. Mater., 2007, 17(1), pp. 87- 92. https://doi.org/10.1002/adfm.200600760 Scholar
- 12.J.K. Ma, T.Y. Shang, L.L. Ren, Y.M. Yao, T. Zhang, J.Q. X ie, B.T. Zhang, X.L. Zeng, R. Sun, J.B. Xu, C.P. Wong. Throughplane assembly of carbon fibers into 3D skeleton achieving enhanced thermal conductivity of a thermal interface material. Chem. Eng. J., 2020, 380, p. 8. https://doi.org/10.1016/j.cej.2019.122550 Scholar
- 13.Obvertkin, I., K. Pasechnik, u A. Vlasov. The potential of using SWCNTs, MWCNTs and CNFs capable of increasing the composite material dimensional and technological stability as modifiers of a polymer matrix. PNRPU Mechanics Bulletin, 2021, No. 4, pp. 98- 110. https://doi.org/10.15593/pemr.mech/2021.4.10 Scholar
- 14.K.C. Yung, B.L. Zhu, T.M. Yue, C.S. Xie. Effect of the Filler Size and Content on the Thermomechanical Properties of Particulate Aluminum Nitride Filled Epoxy Composites. J. Appl. Polym. Sci., 2010, 116 (1), pp. 225- 236. https://doi.org/10.1002/app.31431 Scholar
- 15.C.J. Huang, S.Y. Fu, Y.H. Zhang, B. Lauke, L.F. Li, L. Ye. Cryogenic properties of SiO2/epoxy nanocomposites. Cryogenics, 2005, 45 (6), pp. 450- 454. https://doi.org/10.1016/j.cryogenics.2005.03.003 Scholar
- 16.Ghasemi A.R., Mohammadi M.M., Mohandes M. The role of carbon nanofibers on thermo- mechanical properties of polymer matrix composites and their effect on reduction of residual stresses. Compos Part B Eng, 2015, no. 77, pp. 519- 27. https://doi.org/10.1016/j.compositesb.2015.03.065 Scholar
- 17.Shokrieh M.M., Akbari S., Daneshvar A. Reduction of residual stresses in polymer composites using nano- additives. Residual Stress Composites Mater, 2014, pp. 350- 73, https://doi.org/10.1016/B978- 0- 12- 818817- 0.00013- 5 Scholar
- 18.M.M. Shokrieh, A. Daneshvar, S. Akbari. Reduction of thermal residual stresses of laminated polymer composites by addition of carbon nanotubes. Mater. Des., 2013, 53, pp. 209- 216. https://doi.org/10.1016/j.matdes.2013.07.008 Scholar
- 19.M. Sanchez, P. Campo, A. Jimenez-Suarez, A. Urena. Effect of the carbon nanotube functionalization on flexural and electrical properties of multiscale CFRPs. Polym. Compos., 2020, 41 (5), pp. 1845-1856. https://doi.org/10.1002/pc.25499 Scholar
- 20.B. Jiang, A. Wang, G. Huang, Z. Guo, J. Liu, J. Chen. Carbon nanofibers reinforced 3D printed elastomers: A study of mechanical properties. Polym. Test., 2020, 90, p. 106762. https://doi.org/10.1016/j.polymertesting.2020.106762 Scholar
- 21.S. Mallakpour, E. Khadem. Carbon nanotube–metal oxide nanocomposites: Fabrication, properties, and applications. Chem. Eng. J., 2016, 302, pp. 344-367. https://doi.org/10.1016/j.cej.2016.05.038 Scholar
- 22.M. R. Zakaria, H. M. Akil, M. H. A. Kudus, F. Saleha. Enhancement of tensile and thermal properties of epoxy nanocomposites through the hybridization of functionalized carbon nanotubes and alumina nanoparticles. Polym. Compos., 2019, 40 (S1), pp. E845-E854. https://doi.org/10.1002/pc.25016 Scholar
- 23.M. Alhaji, I. Kubade, H. Kulkarni. Effect of surface modified multi-walled carbon nanotubes on mechanical and thermal properties of epoxy nanocomposites. Mater. Res. Express, 2019, 6 (3), p. 035050. https://doi.org/10.1088/2053-1591/aae8e7 Scholar
- 24.R. Patel, K. Shah. Improvement in mechanical and thermal properties of epoxy/glass fiber with addition of multiwalled carbon nanotubes. Polym. Compos., 2020, 41 (12), pp. 5213-5224. https://doi.org/10.1002/pc.25784 Scholar
- 25.I. Obvertkin, A. Vlasov. Modeling of carbon fibers orientation in a composite under the action of an external magnetic field. PNRPU Mechanics Bulletin, 2021, No. 3, pp. 104-114. https://doi.org/10.15593/perm.mech/2021.3.09 Scholar
- 26.D.K. Singh, D.S. Pradhan, P. Giri, M. Srivastava. Role of electron irradiation and vacuum annealing on the structural and transport properties of multiwall carbon nanotubes. Adv. Mater. Lett., 2010, 1 (3), pp. 246-250. https://doi.org/10.5185/amlett.2010.8143 Scholar
- 27.S.S. Wagh, A.P. More, G.B. Kolekar, M.V. Kulkarni. Study of physical properties of ZnO nanoparticles dispersed in PC/MWCNTs nanocomposites. Polym. Compos., 2019, 40 (S2), pp. E1671-E1678. https://doi.org/10.1002/pc.25099 Scholar
- 28.J. B. Nair, G. Kumar, M. A. R. C. P. Sujith. Mechanical and thermal properties of multiwalled carbon nanotubes filled epoxy composites. Mater. Today Proc., 2021. https://doi.org/10.1016/j.matpr.2021.01.960 Scholar
- 29.S. K. Tiwari, V. Kumar, A. Huczko, R. Oraon, A. De Adhikari, G. C. Nayak. Magical allotropes of carbon: prospects and applications. Crit. Rev. Solid State Mater. Sci., 2016, 41 (4), pp. 257-317. https://doi.org/10.1080/10408436.2015.1127206 Scholar
- 30.A. Dasari, Z.-Z. Yu, Y.-W. Mai. Fundamental aspects and recent progress on wear/scratch damage in polymer nanocomposites. Mater. Sci. Eng. R Rep., 2009, 63 (2), pp. 31-80. https://doi.org/10.1016/j.mser.2008.10.001 Scholar
- 31.S. Ganguli, A. K. Roy, D. P. Anderson. Improved thermal conductivity for chemically functionalized exfoliated graphite/epoxy composites. Carbon N Y, 2008, 46 (5), pp. 806-817. https://doi.org/10.1016/j.carbon.2008.02.008 Scholar
- 32.G. Xie, Y. Bai, Z. Zhang, J. Yang, C. Yang. Thermal expansion behavior of carbon nanotubes. A combined experimental and theoretical study. Phys. Chem. Chem. Phys., 2017, 19 (31), pp. 20419-20426. https://doi.org/10.1039/c7cp02926e Scholar
- 33.R.P. Zhu, C.T. Sun. Effects of Fiber Orientation and Elastic Constants on Coefficients of Thermal Expansion in Laminates. Mech. Adv. Mater. Struct., 10 (2) (2003), pp. 99-107. https://doi.org/10.1080/15376490306733 Scholar
- 34.Yoon KJ, Kim J-S. Prediction of Thermal Expansion Properties of Carbon/Epoxy Laminates for Temperature Variation. Journal of Composite Materials. 2000;34(2):90-100. https://doi.org/10.1177/002199830003400201. Scholar
- 35.Polymer nanocomposites. MRS Bull, 2007, № 32, pp. 314-319. https://doi.org/10.1557/mrs2007.229. Scholar
- 36.Chang T., Gao H. Size-dependent elastic properties of a single-walled carbon nanotube via a molecular mechanics model. J MechPhys Solids, 2003, №. 51, pp. 1059-74. https://doi.org/10.1016/S0022-5096. Scholar
- 37.J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python. IEEE Access, Vol. 8, pp. 89497-89509, 2020. Polym. Adv. Technol., 2017, 28 (11), pp. 1375-1381. https://doi.org/10.1109/ACCESS.2020.2990567 Scholar
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