Research Papers

Parallelized Finite Element Analysis of Knitted Textile Mechanical Behavior

[+] Author and Article Information
D. Liu

Theoretical and Applied Mechanics Group,
Department of Mechanical Engineering
and Mechanics,
Drexel University,
2991 W. School House. Ln., Apt. PW21,
Philadelphia, PA 19144

S. Koric

National Center for Supercomputing
Department of Mechanical Science
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801

A. Kontsos

Theoretical and Applied Mechanics Group,
Department of Mechanical Engineering
and Mechanics,
Drexel University,
3141 Chestnut St.,
Philadelphia, PA 19104
e-mail: antonios.kontsos@drexel.edu

1Corresponding author.

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received May 10, 2018; final manuscript received September 28, 2018; published online December 20, 2018. Assoc. Editor: Anna Pandolfi.

J. Eng. Mater. Technol 141(2), 021008 (Dec 20, 2018) (10 pages) Paper No: MATS-18-1132; doi: 10.1115/1.4041869 History: Received May 10, 2018; Revised September 28, 2018

Direct numerical simulations (DNS) of knitted textile mechanical behavior are for the first time conducted on high performance computing (HPC) using both the explicit and implicit finite element analysis (FEA) to directly assess effective ways to model the behavior of such complex material systems. Yarn-level models including interyarn interactions are used as a benchmark computational problem to enable direct comparison in terms of computational efficiency between explicit and implicit methods. The need for such comparison stems from both a significant increase in the degrees-of-freedom (DOFs) with increasing size of the computational models considered as well as from memory and numerical stability issues due to the highly complex three-dimensional (3D) mechanical behavior of such 3D architectured materials. Mesh and size dependency, as well as parallelization in an HPC environment are investigated. The results demonstrate a satisfying accuracy combined with higher computational efficiency and much less memory requirements for the explicit method, which could be leveraged in modeling and design of such novel materials.

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Fig. 1

Parametric geometry based on material characterization (a), spline curves built by control points (b) and a 3 × 3 finite element model (c) for knitted textile

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Fig. 2

Flowchart of SDI and EQI in implicit scheme with contact and friction behavior

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Fig. 3

(a) Out-of-plane motion (U3) resulting from the contact and friction behavior between yarns; (b) motions of contact zones observed for each yarn including combined rotation, sliding and spin about the yarn's centerline; and (c) reaction force of a 3 × 3 model under course tensile loading

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Fig. 4

von Mises stress contour for explicit analysis within 100% CT (a) and 10% CT (b); kinetic energy and strain energy comparisons between implicit static analysis and explicit analysis within 100% CT (c) and 10% CT (d)

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Fig. 5

Reaction force comparisons for implicit with/without stabilization (a) and out-of-plane contour for implicit without stabilization (b) and with stabilization (c)

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Fig. 6

(a) Number of SDI and EQI and (b) reaction force versus course strain of implicit simulation for different mesh size

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Fig. 7

(a) Different domain size defined by number of unit cell and (b) strain energy per unit cell for domain size from 3 × 3 to 12 × 12

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Fig. 8

Reaction force comparison between implicit and explicit simulations for domain: (a) 3 × 3, (b) 6 × 6, (c) 9 × 9, and (d) 12 × 12

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Fig. 9

Wall clock time ratio (implicit/explicit) and energy ratio (kinetic energy/strain energy) evolution for different domain size from 3 × 3 to 12 × 12

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Fig. 10

Comparisons of (a) reaction force and (b) out-of-plane motion contour between implicit (top) and explicit (bottom) analysis for c15w15

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Fig. 11

Comparison between implicit and explicit solver for wall clock time versus number of parallel process

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Fig. 12

Comparison between implicit and explicit solver for parallel speed-up

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Fig. 13

Comparison between implicit and explicit solver for peak nodal memory consumption



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