Iwona Jasiuk - Profile on Academia.edu (original) (raw)

Papers by Iwona Jasiuk

Additional Cover

International Journal for Numerical Methods in Engineering, Jan 16, 2023

Journal of Materials Research and Technology, 2020

Biological systems must have the capability to withstand impacts generated during collisions due ... more Biological systems must have the capability to withstand impacts generated during collisions due to combat and defense. Thus, evolution has created complex materials' architectures at various length scales that are capable of withstanding repeated, low-tomedium-velocity impacts (up to 50 m/s). In this paper, we review impact resistant biological systems with a focus on their recurrent structural design elements, material properties, and energy absorbing mechanisms. We classify these impact resistant structures at the micro-and meso-scales into layered, gradient, tubular, sandwich, and sutured and show how they construct global hierarchical, composite, porous, and interfacial architectures. Additionally, we review how these individual structures and their design parameters can provide a tailored response. We conclude with a future outlook and discussion of their potential for impact resistant bioinspired designs.

arXiv (Cornell University), Jun 13, 2023

Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approxima... more Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution functions in contrast to classical neural networks that need re-training for every new set of parametric inputs. In this work, we have extended the classical formulation of DeepONets by introducing sequential learning models like the gated recurrent unit (GRU) and long short-term memory (LSTM) in the branch network to allow for accurate predictions of the solution contour plots under parametric and time-dependent loading histories. Two example problems, one on transient heat transfer and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the new architectures compared to the benchmark DeepONet model with a feed-forward neural network (FNN) in the branch. Despite being more computationally expensive, the GRU-and LSTM-DeepONets lowered the prediction error by half (0.06% vs. 0.12%) compared to FNN-DeepONet in the heat transfer problem, and by 2.5 times (0.85% vs. 3%) in the plasticity problem. In all cases, the proposed DeepONets achieved a prediction R 2 value of above 0.995, indicating superior accuracy. Results show that once trained, the proposed DeepONets can accurately predict the final full-field solution over the entire domain and are at least two orders of magnitude faster than direct finite element simulations, rendering it an accurate and robust surrogate model for rapid preliminary evaluations.

On Stiffness, Strength, Anisotropy, and Buckling of 30 Strut‐Based Lattices with Cubic Crystal Structures

Advanced Engineering Materials, 2021

Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads

arXiv (Cornell University), Jun 6, 2023

Cracking of Plates With Randomly Distributed Holes by a Maximum Entropy Method

It has recently been shown [1] that fracture response of nominally identical elastic-brittle (epo... more It has recently been shown [1] that fracture response of nominally identical elastic-brittle (epoxy) as well as ductile (aluminum) sheets, each containing randomly distributed circular holes, is non-unique. This non-uniqueness pertains, in particular, to the resulting fracture patterns and effective stress-strain curves, whereby both of these characteristics display considerable scatter. This result points to the significant influence which microscale random noise in material parameters may have on the global, macroscopic behavior. In this paper we formulate, on the basis of a maximum entropy method [2], a stochastic fracture mechanics model for this class of problems. The method is based on the statistics of experimental data, obtained for a number of specimens, involving the inter-hole crack lengths and their angles. It allows prediction of probability distributions of damage responses and patterns of Gibbs ensembles of random hole systems such as, for example, porous materials with millions of voids.

Journal of Applied Physics, Jul 27, 2016

In this study, we examine the effect of filler alignment on percolation behavior of polymer nanoc... more In this study, we examine the effect of filler alignment on percolation behavior of polymer nanocomposites using Monte Carlo simulations of monodisperse prolate and oblate hard-core soft-shell ellipsoids representing carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs), respectively. The percolation threshold is observed to increase with increasing extent of alignment as expected. For a highly aligned system of rod-like fillers, the simulation results are shown to be in good agreement with the second virial approximation based predictions. However, for a highly aligned system of disk-like fillers, the second virial approximation based results are observed to significantly deviate from the simulations, even for higher aspect ratios. The effect of filler alignment on anisotropy in percolation behavior is also studied by predicting the percolation threshold along different directions. The anisotropy in percolation threshold is found to vanish even for highly aligned systems of fillers with increasing system size.

Journal of Applied Physics, Feb 28, 2018

Using a mixture of different types of fillers has been experimentally shown to improve the electr... more Using a mixture of different types of fillers has been experimentally shown to improve the electrical conductivity of polymer nanocomposites beyond the weighted average due to synergistic effects. In this study, we develop a critical path analysis-based tunneling-percolation model for multicomponent systems of nanocomposites with ellipsoidal fillers. The nature of the interaction between different filler components is controlled by a key modeling parameter capturing the tunneling interactions between fillers. This generalization allows us to examine scenarios where the nature of a given type of filler can be varied continuously from an insulating-type to a conductive-type. The percolation behavior of two-component systems with a combination of prolate, oblate, and spherical fillers is investigated using Monte Carlo simulations for different relative volume fractions and nature of interactions while keeping the total volume fraction fixed. The simulation results are shown to be in semi-quantitative agreement with predictions made by the second-virial-approximation-based theories. Our results suggest that for multicomponent systems with well-dispersed fillers, the synergistic effects are linked directly with the nature of interactions between different filler types. Moreover, addition of prolate fillers to oblate or spherical fillers should generally improve the electrical conductivity of multicomponent nanocomposites.

Scale and boundary conditions effects in elasticity and damage mechanics of random composites

Studies in Applied Mechanics, 1998

Spatial randomness, as opposed to periodic geometries, may have a significant effect on damage fo... more Spatial randomness, as opposed to periodic geometries, may have a significant effect on damage formation in composite materials. This issue was studied extensively over the last few years [1, 2, 3, 4], and in this paper we report new results on effects of scale and boundary conditions in the determination of meso-scale continuum-type models for elasticity and fracture. These models are formulated on scales larger than the single inclusion, yet smaller than the conventional continuum limit. The latter corresponds to the classical concept of aRepresentative Volume Element (RVE) which presupposes the presence representation of the microstructure with all the typical microheterogeneities, and thus calls for relatively large volumes. Indeed, according to Hill [5], an RVE should be such that the relations between volume average stress and strain should be the same regardless of whether kinematic or stress boundary conditions have been used.

Materials & Design, May 1, 2018

In this paper, the acoustic band structure, sound attenuation, and uniaxial elastic modulus of th... more In this paper, the acoustic band structure, sound attenuation, and uniaxial elastic modulus of three cellular solids are studied computationally. The cellular solids are generated based on mathematical surfaces, called triply periodic minimal surfaces (TPMS), which include Schwarz Primitive, Schoen IWP, and Neovius surfaces. Finite element method is used to find the acoustic

Nanofiller Geometry Effects on Electrical Properties of Composites

Nanofillers with highly anisotropic shapes, such as carbon nanotubes, graphene nanoplatelets, car... more Nanofillers with highly anisotropic shapes, such as carbon nanotubes, graphene nanoplatelets, carbon black, and metallic nanowires are used as inclusions in polymer matrix materials to generate nanocomposites with superior electrical, mechanical, and thermal properties. In this paper, we report on our recent and ongoing studies focusing on the enhanced effective electrical conductivity of such composites. First, we report on Monte Carlo simulations of systems of polydisperse prolate and oblate ellipsoids using the critical path-based tunneling-percolation mode. For polydisperse prolate ellipsoids, the critical percolation volume fraction, c  , is shown to have a quasi-universal dependence on the weight-averaged aspect ratio. For polydisperse oblate ellipsoids, c  is shown to have a quasi-universal dependence on the apparent aspect ratio, which is a function of up to fourth moment of the size distribution, as given by percolation theory. In both cases, the function approaches the theoretical predictions for higher volume fractions and higher aspect ratios. The model predictions are then compared with experimental data to estimate the tunneling length scale which is found to be within the expected range. Next, we examine the effect of filler alignment on percolation behavior of nanocomposites using Monte Carlo simulations of monodisperse prolate and oblate hard-core soft-shell ellipsoids representing carbon nanotubes and graphene nanoplatelets, respectively. As expected, the percolation threshold is observed to increase with increasing extent of alignment. For a highly aligned system of rod-like fillers, the simulation results are shown to be in good agreement with the second virial approximation-based predictions. However, for a highly aligned system of disk-like fillers, the second virial approximation-based results are observed to significantly deviate from the simulations, even for higher aspect ratios. The anisotropy in percolation threshold is found to vanish with increasing system size even for highly aligned systems of fillers.

Structural and Multidisciplinary Optimization, Oct 20, 2022

This paper introduces a heuristic topology optimization framework for thin-walled, 2D extruded la... more This paper introduces a heuristic topology optimization framework for thin-walled, 2D extruded lattice structures subject to complex high-speed loading. The proposed framework optimizes the wall thickness distribution in the lattice cross section through different thickness update schemes, inspired by the idea of equalization of absorbed energy density across all lattice walls. The proposed framework is ubiquitous and can be used in explicit dynamic simulations, which is the primary numerical method used in crashworthiness studies. No information on the material tangent stiffness matrix is required, and complex material behaviors and complex loading conditions can be handled. Three numerical examples are presented to demonstrate framework capabilities: (1) Optimization of a long, slender column under axial compression to maximize specific energy absorption, (2) Optimization of a lattice-filled sandwich panel under off-center blast loading to minimize material damage, (3) Generation of a periodic lattice core design under blast loading. The results show that the framework can effectively increase specific energy absorption or minimize material damage with as few as 25 finite element simulations and optimization iterations.

The deep energy method (DEM) employs the principle of minimum potential energy to train neural ne... more The deep energy method (DEM) employs the principle of minimum potential energy to train neural network models to predict displacement at a state of equilibrium under given boundary conditions. The accuracy of the model is contingent upon choosing appropriate hyperparameters. The hyperparameters have traditionally been chosen based on literature or through manual iterations. The displacements predicted using hyperparameters suggested in the literature do not ensure the minimum potential energy of the system. Additionally, they do not necessarily generalize to different load cases. Selecting hyperparameters through manual trial and error and grid search algorithms can be highly time-consuming. We propose a systematic approach using the Bayesian optimization algorithms and random search to identify optimal values for these parameters. Seven hyperparameters are optimized to obtain the minimum potential energy of the system under compression, tension, and bending loads cases. In addition to Bayesian optimization, Fourier feature mapping is also introduced to improve accuracy. The models trained using optimal hyperparameters and Fourier feature mapping could accurately predict deflections compared to finite element analysis for linear elastic materials. The deflections obtained for tension and compression load cases are found to be more sensitive to values of hyperparameters compared to bending. The approach can be easily extended to 3D and other material models.

Extreme Mechanics Letters, May 1, 2020

Couple-stress moduli and characteristics length of a two-phase composite

Mechanics Research Communications, Jul 1, 1999

JOM, Jan 25, 2018

We present an overview on additive manufacturing (AM), also called threedimensional printing, wit... more We present an overview on additive manufacturing (AM), also called threedimensional printing, with a focus on polymers. First, we introduce the AM concept. Next, we outline several AM processes, including their advantages and limitations, and list common polymers that are used in commercial printers. Then, we state various AM applications and present two examples. We conclude with a global view of the AM field, its challenges, and future directions.

Improving the Accuracy of the Deep Energy Method

arXiv (Cornell University), Jul 14, 2022

A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momen... more A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic materials due to its ability to handle irregular domains over the traditional DEM method based on a multilayer perceptron (MLP) network. Its accuracy and solution time are compared to the DEM model based on a MLP network. We demonstrate that the GCN-based model delivers similar accuracy while having a shorter run time through numerical examples. Two different spatial gradient computation techniques, one based on automatic differentiation (AD) and the other based on shape function (SF) gradients, are also accessed. We provide a simple example to demonstrate the strain localization instability associated with the AD-based gradient computation and show that the instability exists in more general cases by four numerical examples. The SF-based gradient computation is shown to be more robust and delivers an accurate solution even at severe deformations. Therefore, the combination of the GCN-based DEM model and SF-based gradient computation is potentially a promising candidate for solving problems involving severe material and geometric nonlinearities.

Direct Numerical Simulation of Bone Plasticity and Strength