arXiv subjects

Sahar Choukir

Publications and source records attributed to Sahar Choukir.

4 recordsLinked to original sources

arXiv2026

Precipitate-Induced Dynamic Strain Aging and Its Effect on the Strain Rate Sensitivity of Precipitation Hardened Aluminum Alloys

We examine precipitate-induced dynamic strain aging in precipitation-hardened Al-Cu alloys by combining atomistic simulations, kinetic Monte Carlo, and analytical rate theory. Atomistic simulations were used to characterize (1) the energetics of nearest neighbour Cu<->Al exchanges at dislocation - precipitate junctions and (2) the subsequent change in obstacle strength. For robustness, the simulations were performed with two distinct interatomic potentials. The resulting catalog of local Cu-Al exchange events was used as input for a kinetic Monte Carlo model of the time-dependent evolution of obstacle strength during dislocation pinning at the precipitate. The predicted strengthening kinetics were then embedded in an analytical dynamic strain aging model to predict the strain-rate sensitivity parameter. On the whole, the modeling predicts a low strain-rate sensitivity across a broad range of intermediate quasi-static strain rates, consistent with experimental observations for precipitate-strengthened alloys. The results therefore identify a mechanistic origin of the low strain-rate sensitivity in precipitation hardened aluminum alloys, emerging directly from the kinetics of dislocation-precipitate interactions when nearest neighbour Cu<->Al exchanges are considered.

cond-mat.mtrl-sci↗

arXiv2026

An Atlas of Extreme Properties in Cubic Symmetric Metamaterials

Current research on three-dimensional metamaterial has largely focused on conventional strut, plate, and shell-based lattice designs. Although these designs offer several advantages, they possess inherent limitations that can restrict their performance in certain applications, motivating the exploration of alternative structural topologies. Here, we present a large-scale, symmetry guided framework for the generation and analysis of architected metamaterials based on all 36 cubic space groups. Using a voxel-based representation, we construct a database of approximately 1.95 million periodic unit cells spanning a broad range of relative densities and topological complexity. This dataset reveals a rich elastic property landscape shaped by crystallographic symmetry, including rare pentamode designs with high bulk to shear ratios such as $K/G \approx 166$ , isotropic-auxetic architectures with Poisson's ratio as low as $\nu=-0.76$, and structures achieving up to 93% of the Hashin-Shtrikman upper bound on stiffness. Complementing the dataset, we develop a three-dimensional convolutional neural network surrogate model trained and evaluated on the full database to predict strain-energy density values under uniaxial, shear, and hydrostatic loading. Together, this work establishes a comprehensive atlas of cubic symmetric metamaterials and provides a pre-trained model for the accelerated discovery and design of 3D architected materials with extreme mechanical properties.

cs.CE↗

arXiv2025

Assessment of ChatGPT for Engineering Statics Analysis

Large language models (LLMs) such as OpenAI's ChatGPT hold potential for automating engineering analysis, yet their reliability in solving multi-step statics problems remains uncertain. This study evaluates the performance of ChatGPT-4o and ChatGPT-o1-preview on foundational statics tasks, from simple calculations of Newton's second law of motion to beam and truss analyses and compares their results to first-year engineering students on a typical statics exam. To enhance accuracy, we developed a Custom GPT, embedding refined prompts directly into its instructions. This optimized model achieved an 82% score, surpassing the 75% student average, demonstrating the impact of tailored guidance. Despite these improvements, LLMs continued to exhibit errors in nuanced or open-ended problems, such as misidentifying tension and compression in truss members. These findings highlight both the promise and current limitations of AI in structural analysis, emphasizing the need for improved reasoning, multimodal capabilities, and targeted training data for future AI-driven automation in civil and mechanical engineering.

cs.CE↗

arXiv2021

The benefits of structural disorder in natural cellular solids

Structural cellular materials in nature, such as wood, trabecular bone, corals, and dentin combine complex biological functions with structural roles, such as skeletal support and impact protection1,2. They feature complex structural hierarchies from nano- to macroscale that enable optimization of both strength and toughness (flaw tolerance) simultaneously3-9. These hierarchies typically exhibit structural disorder in the arrangement of pores. The degree of disorder, however, has not been systematically quantified before, and its role in the mechanical performance of cellular biomaterials is generally unknown. Here we have applied Voronoi tessellations to quantify the cell size variation in 2D cross-sections of biological and engineered cellular materials, using a disorder parameter (d) ranging between 0 (highly disordered) to 1.0 (regular hexagonal honeycomb). We demonstrate that various plant, fungi, and animal cellular materials show characteristic ranges of disorder. Using 3D printed analogues and numerical methods, we demonstrate experimentally a range of pseudo-order (d=0.6 to 0.8) that exhibits a > 30% increase in fracture toughness (and equivalent strength) compared to hexagonal honeycombs (d=1.0) of equal density. Our results show this range of disorder is similar to that identified in the biological examples, which suggests convergent evolution. This optimal degree of structural disorder limits catastrophic failure, providing an evolutionary advantage for organism survival. Distributed structural damage limits cracks below a maximum threshold size and also enables tissue repair mechanisms after trauma. Our work shows that tailored disorder should be considered as a new design paradigm for digitally fabricated, lightweight architected materials to improve damage tolerance.

physics.app-ph↗