arXiv subjects

Ali Saeedi

Publications and source records attributed to Ali Saeedi.

5 recordsLinked to original sources

arXiv2026

The effect of interactions on elastic cavitation

Cavitation refers to the sudden, unstable expansion of a defect or cavity within a material in response to applied loads, when the loads reach a critical threshold. It is widely recognized as a common failure nucleation mechanism in soft and biological materials. For an isolated cavity in the bulk of an incompressible neo-Hookean solid loaded by remote hydrostatic tension, the classical cavitation pressure is well established as $2.5 \mu$, where $\mu$ is the shear modulus. However, in realistic settings the cavitation threshold is influenced by interaction of the cavity with nearby interfaces and other cavities. Interface interaction effects are particularly relevant in multi-material systems and additively manufactured structures, where defects frequently occur near material boundaries. Meanwhile, cavity-cavity interactions become important in materials exhibiting finite porosity, such as foams, porous solids, and phase-separating polymers. Here, we characterize the effect of interactions on cavitation pressure for (i) a nearby rigid interface and (ii) a neighboring identical cavity. For cavities near a rigid interface, our analysis shows that the cavitation pressure increases as the initial cavity-interface distance decreases, starting from the bulk value for a distant cavity and approaching the cavitation pressure value for a defect situated at an interface ($\approx3.5\mu$) as the cavity approaches the interface boundary. In contrast, interacting cavities exhibit a non-monotonic dependence of the cavitation pressure on the initial inter-cavity distance $d$: the threshold approaches the bulk value of $2.5\mu$ for distant cavities and reaches a maximum of $\sim2.67\mu$ at $d\sim3.8R$, where $R$ is the initial cavity radius.

cond-mat.soft↗

arXiv2025

A thermo-mechanically coupled finite deformation model for freezing-induced damage in soft materials

In the U.S., approximately 17 patients die each day awaiting an organ transplant, a crisis driven by the inability to store organs long-term via methods like cryopreservation. A primary failure mechanism is the severe thermo-mechanical damage tissues experience during freezing. A predictive understanding of this damage is hindered by the complex interplay between heat transfer, phase change, and large deformation mechanics. Motivated by this fundamental problem, we present a fully coupled, thermo-mechanical phase-field framework for modeling damage evolution in fluid-saturated soft materials under cryogenic conditions. The theoretical framework integrates heat transfer with solid-liquid phase transition, finite deformation nonlinear elasticity, and progressive mechanical damage. The governing equations are solved using \texttt{FEniCS} finite element package. The presentation will detail the theoretical framework and showcase representative simulations that capture the spatiotemporal evolution of temperature, freezing phase field, stress, and damage fields during representative freezing protocols. The developed framework serves as a powerful tool for understanding the fundamental mechanisms of freezing-induced injury and for designing improved cryopreservation strategies.

cond-mat.soft↗

arXiv2025

Push and Pull: Elastic Interaction Between Pressurized Spherical Cavities in Nonlinear Elastic Media

Elastic interaction of pressurized spherical cavities embedded in a three-dimensional hyperelastic medium is computationally analyzed. Using finite element analysis across several positive and negative pressure scenarios, we calculate the system's potential energy and configurational driving force for neo-Hookean, Mooney-Rivlin, and Arruda-Boyce material models. Our results show that while the interaction is always attractive for negative pressures, a non-monotonic energy landscape emerges for positive pressures above a critical value. In this regime, cavities attract at close range and repel when further apart. The critical separation distance for this transition is shown to be dependent on the material's strain-stiffening parameters. These findings are consolidated into phase diagrams, providing a clear map of interaction behaviors.

cond-mat.soft↗

arXiv2025

Elastic Interaction of Pressurized Cavities in Hyperelastic Media: Attraction and Repulsion

This study computationally investigates the elastic interaction of two pressurized cylindrical cavities in a 2D hyperelastic medium. Unlike linear elasticity, where interactions are exclusively attractive, nonlinear material models (neo-Hookean, Mooney-Rivlin, Arruda-Boyce) exhibit both attraction and repulsion between the cavities. A critical pressure-shear modulus ratio governs the transition, offering a pathway to manipulate cavity configurations through material and loading parameters. At low ratios, the interactions are always attractive; at higher ratios, both attractive and repulsive regimes exist depending on the separation between the cavities. Effect of strain stiffening on these interactions are also analyzed. These insights bridge theoretical and applied mechanics, with implications for soft material design and subsurface engineering.

cond-mat.soft↗

arXiv2017

Effects of Images with Different Levels of Familiarity on EEG

Evaluating human brain potentials during watching different images can be used for memory evaluation, information retrieving, guilty-innocent identification and examining the brain response. In this study, the effects of watching images, with different levels of familiarity, on subjects' Electroencephalogram (EEG) have been studied. Three different groups of images with three familiarity levels of "unfamiliar", "familiar" and "very familiar" have been considered for this study. EEG signals of 21 subjects (14 men) were recorded. After signal acquisition, pre-processing, including noise and artifact removal, were performed on epochs of data. Features, including spatial-statistical, wavelet, frequency and harmonic parameters, and also correlation between recording channels, were extracted from the data. Then, we evaluated the efficiency of the extracted features by using p-value and also an orthogonal feature selection method (combination of Gram-Schmitt method and Fisher discriminant ratio) for feature dimensional reduction. As the final step of feature selection, we used 'add-r take-away l' method for choosing the most discriminative features. For data classification, including all two-class and three-class cases, we applied Support Vector Machine (SVM) on the extracted features. The correct classification rates (CCR) for "unfamiliar-familiar", "unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%, 92.6%, and 70.6%, respectively. The best results of classifications were obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency and harmonic features were among the most discriminative features. Finally, in three-class case, the best CCR was 86.8%.

stat.ML↗