arXiv subjects

Sabarinathan P Subramaniyan

Publications and source records attributed to Sabarinathan P Subramaniyan.

5 recordsLinked to original sources

arXiv2024

Microscale Morphology Driven Thermal Transport in Fiber Reinforced Polymer Composites

Fiber-reinforced polymer composite (FRPC) materials are used extensively in various industries, such as aerospace, automobiles, and electronics packaging, due to their remarkable specific strength and desirable properties, such as enhanced durability and corrosion resistance. The evolution of thermal properties in FRPCs is crucial for advancing thermal management systems, optimizing material performance, and enhancing energy efficiency across these diverse sectors. Despite significant research efforts to develop new materials with improved thermal properties and reduced thermal degradation, there is a lack of understanding of the thermal transport phenomena considering the influence of microscale reinforcement morphology in these composites. In the current study, we performed experimental investigations complemented by computations to determine the thermal transport properties and associated phenomena in epoxy and carbon fiber-reinforced epoxy composites. The experimental findings were utilized as input data for numerical analysis to examine the impact of fiber morphology and volume fraction in thermal transport phenomena. Our results revealed that composites incorporating non-circular fibers manifested higher thermal conductivity than traditional circular fibers in the transverse direction. This can be attributed to increased interconnected heat flow pathways facilitated by the increased surface area of non-circular fibers with the same cross-sectional areas, resulting in efficient heat transfer.

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↗

arXiv2023

Moisture-Driven Morphology Changes in the Thermal and Dielectric Properties of TPU-Based Syntactic Foams

Syntactic foams are a promising candidate for applications in marine and oil and gas industries in underwater cables and pipelines due to their excellent insulation properties. The effective transmission of electrical energy through cables requires insulation materials with a low loss factor and low dielectric constant. Similarly, in transporting fluid through pipelines, thermal insulation is crucial. However, both applications are susceptible to potential environmental degradation from moisture exposure, which can significantly impact the material's properties. This study addresses the knowledge gap by examining the implications of prolonged moisture exposure on TPU and TPU-derived syntactic foam via various multi-scale materials characterization methods. The research focuses on a flexible syntactic foam created using selective laser sintering and thermoplastic polyurethane elastomer (TPU) reinforced with glass microballoons (GMB). The study specifically explores the impact of moisture exposure duration and GMB volume fraction on microphase morphological changes, their associated mechanisms, and their influence on thermal transport and dielectric properties.

physics.app-ph↗

arXiv2023

Moisture-Driven Degradation Mechanisms in the Viscoelastic Properties of TPU-Based Syntactic Foams

Syntactic foams have found widespread usage in various applications including, marine, aerospace, automotive, pipe insulation, electrical cable sheathing, and shoe insoles. However, syntactic foams are often exposed to moisture when used in these applications that potentially alter their viscoelastic properties, which influences their long-term durability. Despite their significance, previous research has mainly focused on experimental studies concerning mechanical property changes resulting from filler loading and different matrix materials, overlooking the fundamental mechanisms resulting from moisture exposure. The current paper aims to bridge this gap in knowledge by elucidating the impact of long-term moisture exposure on TPU and TPU-based syntactic foam through multi-scale materials characterization approaches. Here, we choose a flexible syntactic foam manufactured using thermoplastic polyurethane elastomer (TPU) reinforced with glass microballoons (GMB) through selective laser sintering. Specifically, the research investigates the influence of moisture exposure time and the volume fraction of GMB on chemical and microphase morphological changes, along with their associated mechanisms. The study further examines how these microphase morphological changes manifest in viscoelastic properties.

physics.app-ph↗

arXiv2021

Densification Mechanics of Polymeric Syntactic Foams

In this paper, a fundamental understanding of the densification mechanics of polymeric syntactic foams under compressive loading is established. These syntactic foams are closed cell composite foams with thin-walled microballoons dispersed in a matrix (resin) whose closed cell structure provides excellent mechanical properties, like high strength and low density. There are several parameters that can contribute towards their mechanical properties, including, microballoon volume fraction, microballoon wall thickness, bonding between the microballoons and the matrix, and the crushing strength of microballoons. Conducting purely experimental testing by varying these parameters can be very time sensitive and expensive. Also, identification of densification mechanics is challenging using experiments only. Higher densification stress and energy are favorable properties under foam compression or crushing. Hence, the influence of key structural and material parameters associated with syntactic foams that dictate the mechanics of densification is studied here by implementing micromechanics based computational models and multiple linear regression analysis. Specifically, specific densification stresses and energy, which are densification stresses and energy normalized by weight, are evaluated which are more relevant for a wide variety of weight saving applications. Microballoon crushing strength and volume fraction are identified as the parameters that have the higher influence on densification stress and energy, and their specific counterparts, whereas the interfacial bonding has the least impact. In addition, designing aspects of syntactic foams with specified overall density are discussed by mapping microballoon volume fraction and wall thickness.

physics.app-ph↗