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Content available remote Conductive Heat Transfer Prediction of Plain Socks in Wet State
EN
In this study, an algebraic model and its experimental verification was carried out to investigate the effect of moisture content on the heat loss that takes place due to conduction of sock fabrics. The results show that increasing moisture content in the studied socks caused a significant increase in their conductive heat loss. Plain knitted socks with different fiber composition were wetted to a saturated level, and then their moisture content was reduced stepwise. When achieving the required moisture content, the socks samples were characterized by the Alambeta testing instrument for heat transfer. Three different existing modified mathematical models for the thermal conductivity of wet fabrics were used for predicting thermal resistance of socks under wet conditions. The results from both ways are in very good agreement for all the socks at a 95% confidence level. In the above-mentioned models, the prediction of thermal resistance presents newly a combined effect of the real filling coefficient and thermal conductivity of the so-called “wet” polymers instead of dry polymers. With these modifications, the used models predicted the thermal resistance at different moisture levels. Predicted thermal resistance is converted into heat transfer (due to conduction) with a significantly high coefficient of correlation.
EN
Socks’ comfort has vast implications in our everyday living. This importance increased when we have undergone an effort of low or high activity. It causes the perspiration of our bodies at different rates. In this study, plain socks with different fiber composition were wetted to a saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in wet state at different moisture levels. Theoretical thermal resistance is predicted using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fiber) in different models. By this modification, these mathematical models can predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance has reason able correlation with experimental results in both dry (laboratory conditions moisture) and wet states.
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