# Development of a Three-Dimensional Geometric Model of Multi-Structured Woven Fabrics Using Spun Yarns for Theoretical Air Permeability Prediction

**Authors:** Theeradech Songart, Wasit Chaikumming, Keartisak Sriprateep

PMC · DOI: 10.3390/ma19051045 · 2026-03-09

## TL;DR

Researchers developed a 3D model to predict air permeability in woven fabrics, improving accuracy compared to traditional methods.

## Contribution

A novel filament-level 3D model using NURBS and CFD simulations for accurate air permeability prediction in woven fabrics.

## Key findings

- The filament assembly model showed lower percentage prediction errors than single-line yarn models.
- CFD simulations validated with ISO 9237:1995 standards confirmed the model's accuracy.
- The model effectively captures airflow through inter-yarn and intra-yarn pores.

## Abstract

This study presents the development of a three-dimensional (3D) filament assembly model for predicting the air permeability of woven fabrics composed of spun yarns. To address the limitations of conventional single-line yarn models, the proposed framework incorporates fiber-level geometric representations using non-uniform rational B-splines (NURBS) and simulates multiple weave patterns—including plain, basket, twill, and rib—under various set density configurations. Each yarn was modeled with accurate filament distribution and cross-sectional layering, enabling the construction of realistic unit-cell-based CAD geometries. Computational fluid dynamics (CFD) simulations were performed using the k-ε turbulence model in SolidWorks Flow Simulation and validated against experimental measurements conducted under ISO 9237:1995 conditions. The filament assembly model achieved high predictive accuracy, exhibiting a lower of percentage prediction errors than the single-line yarn path model, thereby more effectively capturing airflow behavior through inter-yarn and intra-yarn pores. These findings highlight the capability of integrated CAD/CFD methodologies for virtual prototyping of breathable textiles and provide a robust foundation for high-precision performance prediction in functional and technical fabric design.

## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12986539/full.md

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Source: https://tomesphere.com/paper/PMC12986539