This study presents a computational model for investigation of the air permeability coefficient and water-vapor resistance coefficient through 3D textile layer. The effective values of the coefficients are highly dependent on the volumetric internal structure of the layer. The computational study of air and water vapor flow has been performed a microscale finite element model of the representative volume of the textile layer by taking into account the real configuration the yarns and filaments. The effective values of the coefficients were obtained by using taking average values of air and water vapor fluxes through the thickness of the representative volume. The steady state computational models in micro scale are based on Navier-Stokes and Brinkman partial differential equations. The simulations were performed in Comsol Multiphysics 5.3a software environment by using laminar flow (.spf) application mode. Numerical results for the air permeability of the samples were obtained, analyzed and validated by comparing against experimental data. Good agreement with experimental data has been achieved.

In this study, computational models of heat and mass exchange through textile structures with additional ventilation at the micro- and macro-scale were investigated. The finite element analysis of advanced textile materials provides a better understanding of their heat and mass transfer properties, which influence thermal comfort. The developed computational models can predict air permeability (AP), thermal resistance (R_ct), and heat transfer (h) coefficients at the micro-scale. Moreover, the mesh size was taken into consideration and validated with experimental data presented in the literature. In addition, computational models were extended to micro- and macro-scale forced ventilation models. Macro-scale finite element models require input parameters such as an effective heat transfer coefficient that are usually obtained experimentally. In this research, the heat transfer coefficients (h_microlayer = 25.603 W/(K·m²), h_total = 8.9646 W/(K·m²)) were obtained numerically from the micro-scale model and were applied to a macro-scale model. The proposed methodology and developed models facilitate the determination of average temperature and temperature distributions through different through-thickness positions along the axis Oz. The simulations were carried out using Comsol Multiphysics and Matlab software.

This study develops pore-scale computational models to predict air permeability and thermal properties of one-layer, two-layer, and three-dimensional polyester textile structures. Accurate prediction of airflow and heat transfer in multilayer textile structures is essential for evaluating the thermal insulation and ventilation characteristics of protective clothing while ensuring thermal comfort. Compared with previous studies, the proposed pore-scale models examine the combined effects of multilayer textile structure and additional ventilation on airflow and heat transfer. The models were created in COMSOL Multiphysics 6.2 using Navier-Stokes and Brinkman equations for airflow and the energy equation for heat transfer simulations in the laminar flow regime. The results showed that when the complexity of textile structures increased from one-layer to three-dimensional structures, the air permeability decreased from 1257.1 mm/s to approximately 447.9 mm/s, and the thermal resistance increased from 0.0144 to 0.123 m ² K/W. Additional ventilation improved heat extraction, achieving an effective heat transfer coefficient of 53.64 W/m ² K at a flow rate of 0.5 dm ³ /min and an inlet air temperature of 10°C. The numerical predictions showed good agreement with experimental data reported in the literature, confirming the applicability of the developed models for the analysis of airflow and thermal characteristics of multilayer textile structures.

This study introduces an automated computational tool to evaluate the phase velocity of the highly dispersive A₀ mode using only two signals measured along the wave propagation path. The algorithm combines the zero-crossing technique with automated spectral decomposition, utilizing a bank of bandpass filters with adaptive bandwidths. Validated through theoretical and experimental analysis of an aluminium plate near 300 kHz, the results demonstrate that using a two-signal set and variable filter widths significantly improves accuracy and extends the measurable frequency range of the dispersion curve. Experimental results demonstrate that by applying various filter widths, the phase velocity dispersion curve segment can be reconstructed over a frequency range exceeding 65% of the signal’s spectral width at the −40 dB level. The reconstruction yielded an average relative error of 0.8% ± 1.2%, while the best-case scenario showed an error of just 0.3% ± 0.4%. Implementing automated filter parameter selection on a signal pair offers a time-efficient alternative to traditional spatial scanning, significantly simplifying data collection while reducing labour and time requirements.