Open Access Open Access  Restricted Access Subscription Access

Multiscale Modeling of the Effective Thermal Conductivity of 2D Woven Composites by Mechanics of Structure Genome and Neural Networks

XIN LIU, BO PENG, WENBIN YU

Abstract


A data-driven multiscale modeling approach is developed to predict the effective thermal conductivity of two-dimensional (2D) woven composites. First, a two-step homogenization approach based on mechanics of structure genome (MSG) is developed to predict effective thermal conductivity. The accuracy and efficiency of the MSG model are compared with the representative volume element (RVE) model based on three-dimensional (3D) finite element analysis (FEA). Then, the simulation data is generated by the MSG model to train neural network models to predict the effective thermal conductivity of three 2D woven composites. The neural network models have mixed input features: continuous input (e.g., fiber volume fraction and yarn geometries) and discrete input (e.g., weave patterns). Moreover, the neural network models are trained with the normalized features to enable reusability. The results show that the developed data-driven models provide an ultra-efficient yet accurate approach for the thermal design and analysis of 2D woven composites.


DOI
10.12783/asc36/35936

Full Text:

PDF

References


Monnot, P., L´evesque, J. and Lebel, L. 2017. “Automated braiding of a complex aircraft

fuselage frame using a non-circular braiding model,†Compos Part A Appl Sci Manuf.,

:48-63.

Tao, F., Lyu, X., Liu, X., and Yu, W. 2021. “Multiscale analysis of multilayer printed

circuit board using mechanics of structure genome,†Mech Adv Mater Struct., 28(8):774-

Wang, Y., Liu, X., Zhu, C., Parsons, A., Liu, J., Huang, S., Ahmed, I., Rudd, C. and

Sharmin, N. 2019. “Production and characterisation of novel phosphate glass fibre yarns,

textiles, and textile composites for biomedical applications,†J Mech Behav Biomed Mater.,

:47-55.

Visser, S., King, R., Thornton, J., Brock, J., and Mansour, N. 2019. “Micro-scale Artificial

Weave Generation Capabilities for Thermal Protection System Material Modeling,†11th

Ablation Workshop.

Zhou, X., Ruan, X., Zhang, S., Xiong, W., and Ullah, Z. 2021. “Design optimization for

thermal conductivity of plain-woven textile composites,†Compos Struct., 255:112830.

Struzziero, G., Teuwen, J. and Skordos, AA. 2019. “Numerical optimisation of thermoset

composites manufacturing processes: A review,†Compos Part A Appl Sci Manuf.,

:105499.

O¨ zdemir, I., Brekelmans,WAM. and Geers,MGD. 2008. “Computational homogenization

for heat conduction in heterogeneous solids,†Int J Numer Methods Eng., 73(2):185-204.

Low, ZK., Blal, N., Naouar, N. and Baillis, D. 2020. “Influence of boundary conditions on

computation of the effective thermal conductivity of foams,†Int J Numer Methods Eng.,

:119781.

Yang, Z., Sun, Y., Cui, J., Yang, Z. and Guan, T. 2018. “A three-scale homogenization

algorithm for coupled conduction-radiation problems in porous materials with multiple

configurations,†Int J Heat Mass Transf., 125:1196-1211.

Yu, W. 2016. “A unified theory for constitutive modeling of composites,†J Mech Mater

Struct., 11(4):379-411.

Yu,W. 2019. “Simplified formulation of mechanics of structure genome,†AIAA Journal.,

(10):4201-4209.

Liu, X., Rouf, K., Peng, B. and Yu, W. 2017. “Two-step homogenization of textile composites

using mechanics of structure genome,†Compos Struct., 171:252-262.

Liu, X., Yu, W., Gasco, F., and Goodsell, J. 2019. “A unified approach for thermoelastic

constitutive modeling of composite structures,†Compos Part B Eng., 172:649-659.

Liu, X., Tang, T., Yu, W. and Pipes, RB. 2018. “Multiscale modeling of viscoelastic

behaviors of textile composites,†Int J Eng Sci., 130:175-186.

Berdichevsky, V. 2009. “Variational principles of continuum mechanics: I. Fundamentals,â€

Berlin: Springer Science & Business Media.

Pitchai, P. and Guruprasad, PJ. 2020. “Determination of the influence of interfacial thermal

resistance in a three phase composite using variational asymptotic based homogenization

method,†Int J Heat Mass Transf., 155:119889.

Tang, T. and Yu, W. 2007. “A variational asymptotic micromechanics model for predicting

conductivities of composite materials,†J Mech Mater Struct., 2(9):1813-1830.

Yan, S., Zou, X., Ilkhani, M., and Jones, A. 2020. “An efficient multiscale surrogate

modelling framework for composite materials considering progressive damage based on

artificial neural networks,†Compos Part B Eng., 194:108014.

Liu, X., Gasco, F., Goodsell, J. and Yu, W. 2019. “Initial failure strength prediction

of woven composites using a new yarn failure criterion constructed by deep learning,â€

Compos Struct., 230:111505.

Hashin, Z. 1968. “Assessment of the self consistent scheme approximation: conductivity

of particulate composites,†J Compos Mater., 2(3):284-300.

Peng, B. and Yu,W. 2018. “A micromechanics theory for homogenization and dehomogenization

of aperiodic heterogeneous materials,†Compos Struct., 199:53-62.

Geuzaine, C. and Remacle, J. 2009. “Gmsh: A 3-D finite element mesh generator with

built-in pre-and post-processing facilities,†Int J Numer Methods Eng., 79(11):1309-1331.

Lin, H., Brown, LP. and Long, A. 2011. “Modelling and simulating textile structures

using TexGen,†Compos Struct., 331:44-47.

Seo, Y., Luo, K., Ha, M. and Park, Y. 2020. “Direct numerical simulation and artificial

neural network modeling of heat transfer characteristics on natural convection with a

sinusoidal cylinder in a long rectangular enclosure,†Int J Heat Mass Transf., 152:119564.

Liu, X., Tao, F. and Yu, W. 2020. “A neural network enhanced system for learning nonlinear

constitutive law and failure initiation criterion of composites using indirectly measurable

data,†Compos Struct., 252:112658.

Liu, X., Tao, F., Du, H., Yu, W. and Xu, K. 2020. “Learning nonlinear constitutive

laws using neural network models based on indirectly measurable data,†J Appl Mech.,

(8):081003.

Nielsen, MA. 2015. “Neural networks and deep learning,†Determination press San Francisco,

CA, USA.

Hamillage, M., Kwok, K. and Fernandez, J. 2019. “Micromechanical Modeling of High-

Strain Thin-Ply Composites,†AIAA Scitech 2019 Forum, pp:1751.

Helton, J. and Davis, F. 2003. “Latin hypercube sampling and the propagation of uncertainty

in analyses of complex systems,†Reliab Eng & Syst Saf., 81(1):23-69.

Zhang, Z. 2018. “Improved adam optimizer for deep neural networks,†2018 IEEE/ACM

th International Symposium on Quality of Service (IWQoS), IEEE, pp:1-2.


Refbacks

  • There are currently no refbacks.