arXiv subjects

Hridyesh Tewani

Publications and source records attributed to Hridyesh Tewani.

5 recordsLinked to original sources

arXiv2026

Fabric Phononic Crystals for Passive Vibration Control

Weaving patterns in fabrics, traditionally used for aesthetic purposes, present a largely untapped opportunity to create metamaterials that serve as passive layers for sensing, filtering, and signal processing. However, the hierarchical architecture of fabrics makes structural design and wave prediction challenging. Here, we establish fully woven fabrics as phononic crystals that passively filter and route elastic vibrations. Using double weaving, we integrate a soft cotton weave with stiff woven copper inclusions to form periodic fabric lattices with engineered dispersion. A multiscale modeling framework that combines homogenization of weave blocks with an effective-property macroscale model enables computationally efficient design of phononic crystals. Simulations and experiments confirm a pronounced phononic bandgap for out-of-plane vibrations in a finite fabric crystal, while an equivalent pure cotton weave shows no band suppression in the corresponding frequency range. Building on the same platform, we realize a fully woven higher-order topological insulator. Modal analysis and transmission measurements reveal in-gap edge states and localized corner states. These results show that phononic bandgaps and topological states can be directly encoded through weaving patterns and material contrast, enabling passive vibration management layers and multifunctional waveguiding fabrics for sensing, haptic interfaces, robotics, and noise mitigation.

physics.app-ph↗

arXiv2025

Ar$χ$i-Textile Composites: Drapable Hybrid Woven Composites for Lightning Strike Protection

Carbon fiber-reinforced polymers (CFRPs) have been extensively used in the aerospace and wind energy industries due to their superior specific mechanical properties and corrosion resistance. However, their higher electrical resistivity makes them susceptible to lightning strike damage, which necessitates the addition of a surface lightning strike protection (LSP) layer. Traditional LSP systems, such as copper mesh or expanded foil, reduce lightning strike damage, but are not easily drapable around complex geometries and may introduce delamination-prone regions within the composite. Here, we propose a novel manufacturing strategy for architected hybrid composites as drapable LSP by weaving stainless steel yarns within the woven carbon fiber composites. We varied the metal-to-carbon yarn ratio and stacking configuration to assess damage evolution under quasi-static arc exposures and simulated lightning strikes. Our results elucidate that incorporating hybrid layers into composites significantly reduced surface temperatures, through-thickness damage, and mass loss under both electric arc impacts. The composites with the proposed LSP layers also exhibited higher retention of flexural modulus and strength compared to the reference CFRP. Advanced air mobility (AAM) vehicles, which operate at lower altitudes, face significant safety challenges due to their high susceptibility to lightning strikes. Therefore, the proposed hybridized composites can be used as an efficient and drapable LSP around complex shapes in AAM vehicles, offering enhanced safety and protection.

physics.app-ph↗

arXiv2024

Ar$χ$i-Textile Composites: Role of Weave Architecture on Mode-I Fracture Toughness in Woven Composites

This paper investigates the impact of weave architectures on the mechanics of crack propagation in fiber-reinforced woven polymer composites under quasi-static loading. Woven composites consist of fabrics/textiles containing fibers interwoven at 0 degrees (warp) and 90 degrees (weft) bound by a polymer matrix. The mechanical properties can be tuned by weaving fiber bundles with single or multiple materials in various patterns or architectures. Although the effects of uniform weave architectures, like plain, twill, satin, etc. on in-plane modulus and fracture energy have been studied, the influence of patterned weaves consisting of a combination of sub-patterns, that is, architected weaves, on these behaviors is not understood. We focus on identifying the mechanisms affecting crack path tortuosity and propagation rate in composites with architected woven textiles containing various sub-patterns, hence, \textit{Ar$χ$i} {\bf(ar.kee)} \textit{-Textile} Composites. Through compact tension tests, we determine how architected weave patterns compared to uniform weaves influence mode-I fracture energy of woven composites due to interactions of different failure modes. Results show that fracture energy increases at transition regions between sub-patterns in architected weave composites, with more tortuous crack propagation and higher resistance to crack growth than uniform weave composites. We also introduce three geometrical parameters - transition, area, and skewness factors - to characterize sub-patterns and their effects on in-plane fracture energy. This knowledge can be exploited to design and fabricate safer lightweight structures for marine and aerospace sectors with enhanced damage tolerance under extreme loads.

physics.app-ph↗

arXiv2024

Architected Flexible Syntactic Foams: Additive Manufacturing and Reinforcing Particle driven Matrix Segregation

Polymer syntactic foams are transforming materials that will shape the future of next-generation aerospace and marine structures. When manufactured using traditional processes, like compression molding, syntactic foams consist of a solid continuous polymer matrix reinforced with stiff hollow particles. However, polymer matrix segregation can be achieved during the selective laser sintering process with thermoplastic polyurethane (TPU). It is uncertain what role hollow particles play in forming this matrix segregation and its impact on the corresponding mechanical properties of syntactic foams. We show that the size of the hollow particles controls the internal microscale morphology of matrix segregation, leading to counter-intuitive macroscale mechanical responses. Particles with diameters greater than the gaps between the cell walls of the segregated matrix get lodged between and in the walls, bridging the gaps in the segregated matrix and increasing the stiffness of syntactic foams. In contrast, particles with smaller diameters with higher particle crushing strength get lodged only inside the cell walls of the segregated matrix, resulting in higher densification stresses (energy absorption). We show that stiffness and densification can be tuned while enabling lightweight syntactic foams. These novel discoveries will aid in facilitating functional and lightweight syntactic foams for cores in sandwich structures.

physics.app-ph↗

arXiv2023

Physics-Constrained Neural Network for Design and Feature-Based Optimization of Weave Architectures

Woven fabrics play an essential role in everyday textiles for clothing/sportswear, water filtration, and retaining walls, to reinforcements in stiff composites for lightweight structures like aerospace, sporting, automotive, and marine industries. Several possible combinations of weave patterns and material choices, which comprise weave architecture, present a challenging question about how they could influence the physical and mechanical properties of woven fabrics and reinforced structures. In this paper, we present a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties like the modulus of weave architectures and the inverse problem of predicting pattern/material sequence for a design/target modulus value. The inverse problem is particularly challenging as it usually requires many iterations to find the appropriate architecture using traditional optimization approaches. We show that the proposed PCNN can effectively predict weave architecture for the desired modulus with higher accuracy than several baseline models considered. We present a feature-based optimization strategy to improve the predictions using features in the Grey Level Co-occurrence Matrix (GLCM) space. We combine PCNN with this feature-based optimization to discover near-optimal weave architectures to facilitate the initial design of weave architecture. The proposed frameworks will primarily enable the woven composite analysis and optimization process, and be a starting point to introduce Knowledge-guided Neural Networks into the complex structural analysis.

physics.app-ph↗