نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، مهندسی مکانیک، دانشگاه صنعتی خواجه نصیر الدین طوسی ، تهران.

2 استادیار، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه نصیر الدین طوسی، تهران.

10.22068/jstc.2019.107476.1546

چکیده

در دهه های اخیر، نظر به اهمیت کاهش هزینه ها، افزایش بهره وری و بقا در رقابت فزاینده صنایع مختلف، و همچنین به دلیل وجود منابع عدم قطعیت متعدد، توجه به تحلیل قابلیت اطمینان در رشته های مختلف رشد چشمگیری داشته است. با توجه به مزیت های فراوان استفاده از سازه های کامپوزتی در صنایع مختلف و رشد روزافزون بکارگیری آن، توجه به مسئله قابلیت اطمینان در سازه های کامپوزیتی از اهمیت بسزایی برخوردار می باشد. برای تحلیل قابلیت اطمینان سازه ها، روش های مختلفی از جمله روش شبیه سازی مستقیم مونت کارلو ارائه شده است که به دلیل هزینه محاسباتی بسیار بالا، استفاده از آن فقط برای مسائل ساده امکان پذیر می باشد. در مقاله حاضر، برای تحلیل قابلیت اطمینان یک سازه ی کامپوزیتی با وجود منابع عدم قطعیت گسترده در پارامترهای ورودی مانند خواص مکانیکی، خواص هندسی، استحکام و بارگذاری، روش توسعه یافته ای مبنی بر روش بسط چند جمله ای های آشوب ارائه شده است که نسبت به روش های قدیمی از جمله مونت کارلو، از هزینه محاسباتی بسیار پایینی برخوردار بوده و دارای دقت بالایی می باشد. در نهایت با انجام یک مثال عددی در دو مرحله با افزایش پارامترهای ورودی غیر قطعی کارایی این روش در تحلیل مسائل با منابع عدم قطعیت گسترده از لحاظ تعداد پارامترها نشان داده شده و با بررسی نتایج حاصله با روش مونت کارلو، مزیت این روش از نظر دقت و سرعت محاسباتی مورد ارزیابی قرار گرفته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Reliability analysis based on polynomial chaos expansion method in composite structures

نویسندگان [English]

  • Mohammad Noorian 1
  • Mohammad Ravandi 2

1 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

2 Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.

چکیده [English]

Reliability analysis of composite structures has gained increased attention due to the growing use of composite materials in many industries such as aerospace, automotive and construction in recent decades. Uncertainty analysis approaches are effective tools in order to probabilistically assess the behavior and evaluate the reliability of composite structures with variabilities in material properties. In this study, a computationally efficient surrogate model based on the polynomial chaos expansion for reliability analysis of composite structures with a large number of uncertain parameters is presented. The uncertain input parameters including composite material properties, geometry and loads, are assumed as random variables with a normal distribution and are taken into account for reliability evaluation. A sparse grid collocation strategy is used to determine the sample points for constructing the surrogate model relating the uncertain variables to the structural response. In the end, a numerical example is performed to demonstrate the accuracy and efficiency of this methodology for a higher number of uncertain variables by comparing the results with the direct Monte Carlo simulation method.

کلیدواژه‌ها [English]

  • Composite structures
  • Reliability analysis
  • Polynomial chaos expansion
  • Sparse grid collocation
  • Uncertainty analysis
[1] Saeedifar, M. Najafabadi, MA. Toudeshky, HH. Mohammadi, R., "Investigation of Initiation and Evolution of Delamination in Glass/ Epoxy Laminated Composites Using Acoustic Emission Method," In Persian, AmirKabir Jounrnal of Science & Research Mechanical Engineering, Vol. 48, No. 4, pp. 153-156, 2017.
[2]Dey, S. Mukhopadhyay, T. Adhikari, S., "Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment,"  Composite Structures, Vol. 171, pp. 227–250, 2017.
[3]Baran, I. Cinar, K. Ersoy, N. Akkerman, R. Hattel, JH., "A Review on the Mechanical Modeling of Composite Manufacturing Processes," Archives of Computational Methods in Engineering, Vol. 24, pp. 365–395,2017.
[4]   Fisher, J. Bhattacharya, R., "Linear quadratic regulation of systems with stochastic parameter uncertainties," Automatica Vol. 45, pp. 2831–2841, 2017.
[5]Chen, X. Qiu, Z., "A novel uncertainty analysis method for composite structures with mixed uncertainties including random and interval variables," Composite Structures Vol. 184, pp. 400–410, 2018.
[6]Thapa, M. Mulani, SB. Walters, RW., "Stochastic multi-scale modeling of carbon fiber reinforced composites with polynomial chaos," Composite Structures,
[7]Peng, X. Li, D. Wu, H. Liu, Z. Li, J. Jiang, S. et al., "Uncertainty analysis of composite laminated plate with data-driven polynomial chaos expansion method under insufficient input data of uncertain parameters," Composite Structures, Vol. 209, pp. 625–633, 2019.
[8]Sasikumar, P. Venketeswaran, A. Suresh, R. Gupta, S., "A data driven polynomial chaos based approach for stochastic analysis of CFRP laminated composite plates," Composite Structures, Vol. 125, pp. 212–227, 2015.
[9]Balokas, G. Czichon, S. Rolfes, R., "Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty," Composite Structures, Vol. 183, pp. 550–562, 2018.
[10]Naskar, S. Mukhopadhyay, T. Sriramula, S., "Probabilistic micromechanical spatial variability quantification in laminated composites," Composites Part B: Engineering, Vol. 151, pp. 291–325, 2018.
[11]Ghasemi, H. Rafiee, R. Zhuang, X. Muthu, J. Rabczuk, T., "Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling," Computational Materials Science, Vol. 85, pp. 295–305, 2014.
[12]Whiteside MB., "Stochastic Analysis of Composite Materials. Doctoral dissertation," Imperial College of Science,Technology and Medicine, 2012.
[13]Marburg, S. Hardtke, H-J., "Uncertainty quantification in stochastic systems using polynomial chaos expansion," International Journal of Applied Mechanics, Vol. 02, pp. 305–353, 2010.
[14]Choi, S-K. Grandhi, RV. Canfield, RA., "Structural reliability under non-Gaussian stochastic behavior," Computers & Structures, Vol. 82, pp. 1113–1121, 2004.
[15]Choi, S-K. Grandhi, RV. Canfield, RA. Pettit, CL., "Polynomial Chaos Expansion with Latin Hypercube Sampling for Estimating Response Variability," AIAA Journal, Vol. 42, pp. 1191–1198, 2004.
[16] Xiu, D. Hesthaven, JS., "High-Order Collocation Methods for Differential Equations with Random Inputs," SIAM Journal on Scientific Computing, Vol. 27, pp. 1118–1139, 2005.
[17]Xiu, D. Karniadakis, GE., "Modeling uncertainty in flow simulations via generalized polynomial chaos," Journal of Computational Physics, Vol. 187, pp. 137–167, 2003.
[18]Nobile, F. Tempone, R. Webster, CG., "A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data," SIAM Journal on Numerical Analysis, Vol. 46, pp. 2309–2345, 2008.
[19]Burkardt, J., "Slow Exponential Growth for Clenshaw Curtis Sparse Grids," 2014.
[20]Bhaduri, A. Graham-Brady, L., "An efficient adaptive sparse grid collocation method through derivative estimation. Probabilistic Engineering Mechanics," Vol. 51, pp. 11–22, 2018.
[21]Dey, S. Mukhopadhyay, T. Spickenheuer, A. Gohs, U. Adhikari, S., "Uncertainty Quantification in Natural Frequency of Composite Plates - An Artificial Neural Network Based Approach," Advanced Composites Letters, Vol. 25, pp. 43-48, 2016.
[22]Reddy, JN., "Mechanics of laminated composite plates and shells: theory and analysis," 2nd ed. USA, CRC Press, 2004.
[23]Ghannadpour, AM. Kurkaani Barvaj, A., "Ultimate strength analysis of imperfect composite plates under both in-plane compressive load and lateral pressure using Ritz method," In Persian, Journal of Science and Technology of Composites, Vol. 5, pp. 1-10, 2018.
[24]Ghannadpour, A. Barekati, M., "Post-buckling analysis of anti-symmetric cross-ply composite plates under end-shortening," In Persian,  Journal of Science and Technology of Composites, Vol. 2, pp. 35-42, 2018.
[25]Jeong, HK. Shenoi, RA., "Reliability analysis of mid-plane symmetric laminated plates using direct simulation method," Composite Structures, Vol. 43, pp. 1-13, 1998.;43:1–13.
.