Effect of hydrostatic pressure on flow and deformation in highly reinforced particulate composites

Random 3D-printed isotropic composites with high volume fraction of pore-like polydisperse inclusions and near-optimal elastic stiffness

“…In Figure 5a and Figure 5b increasing deviation from the average as the volume fraction of voided inclusions increases (see Figure 5b). The observed sensitivity for the measurements of the transverse strain at large volume fraction of inclusions is consistent with an earlier study on highly loaded particulate composites [54]. Moreover, data in Figure 5b display a small departure from the HS curve (here not a bound) for c ≥ 0.4.…”

Numerical estimation via remeshing and analytical modeling of nonlinear elastic composites comprising a large volume fraction of randomly distributed spherical particles or voids

“…These observations are thus in agreement with the present results that show that the monodisperse FE results are stiffer than the differential-scheme results. Note, however, that there exist so many different microstructures that are mono- (Travers et al, 1987;Willot and Jeulin, 2009;Anoukou et al, 2018;Tarantino et al, 2016;Tarantino and Mortensen, 2022), bi- (Pickering et al, 2016;Bele and Deshpande, 2015;Bele et al, 2017) and polydisperse (Zerhouni et al, 2021;Neumann et al, 2020;Hooshmand-Ahoor et al, 2022) with very different responses that none of the classical models may properly model. In this regard, the versatile expression shown in equation ( 8) might allow for a wide range of modeling flexibility by calibration with available numerical or experimental data.…”

“…The present study aims at extending the current available numerical and analytical homogenization results for particle reinforced and porous nonlinear elastic composites to larger volume fractions (as large as 55vol%). We note that large volume fractions of inclusions have actual material applications such as propellants (de Francqueville et al, 2021), cermets (Bele and Deshpande, 2015;Pickering et al, 2016;Tarantino et al, 2016), muscles (Spyrou et al, 2017(Spyrou et al, , 2019 and closed-cell foams (see for instance (Hooshmand-Ahoor et al, 2022)). For our numerical simulations, we generate cubic unit cells with randomly distributed monodis-Figure 1: Maximum effective nominal strain reached in finite element (FE) simulations for uniaxial tensile loading as a function of the particle volume fraction (Brassart et al, 2009;Lopez-Pamies et al, 2013;Jiménez and Pellegrino, 2012;Bouchart et al, 2008;Guo et al, 2007;DeBotton et al, 2006;Yang and Xu, 2009;Khisaeva and Ostoja-Starzewski, 2006;Chi et al, 2015;Meng and Wang, 2015;Leonard et al, 2020;Goudarzi et al, 2015;Guo et al, 2014;Jiménez, 2016;Moraleda et al, 2009).…”

Directing Surface Functions by Inducing Ordered and Irregular Morphologies at Single and Two‐Tiered Length Scales

“…Among the fluoro-functionalized samples, the electropolished base surfaces exhibited the highest WCA followed by the ground series and the shot peened ones, respectively; these results are aligned with previous findings confirming the role of random microscale features (SP series) in promoting surface wettability. [17,34,35] It should also be considered that the magnitude of the surface roughness on the SP series was much higher compared to the G series (11.58 vs 0.59 in terms of R a for SP and G series, respectively). This remarkable roughness difference could have contributed to the distinctive WCA results between the SP-A-F-L/SP-B-F-L pair and G-A-F-L/G-B-F-L. Higher surface roughness within the same length scale could stimulate higher wettability.…”