4.5 Article

The effects of fabric's mechanical properties on its motion and drying performance in a domestic tumble dryer

期刊

DRYING TECHNOLOGY
卷 39, 期 4, 页码 528-547

出版社

TAYLOR & FRANCIS INC
DOI: 10.1080/07373937.2020.1711523

关键词

Domestic tumble dryer; fabric mechanical properties; textile motion; drying performance; drying rate

资金

  1. National Key R&D Program of China [2018YFF0215703]
  2. Shanghai Science and Technology Committee [17DZ2202900]
  3. Shanghai Summit Discipline in Design [DD18005]
  4. Fundamental Research Funds for the Central Universities [2232019G-08]

向作者/读者索取更多资源

The study measured and compared the mechanical properties of 10 different fabrics to investigate the impact of fabric properties on textile motion and drying performance in a domestic tumble dryer. Fabric motions were found to be complicated during the drying process and influenced mainly by fabric weight. Regression models were developed to predict drying indexes based on motion indexes with an explanatory power of around 90%.
In order to understand the impact of fabric properties on textile motion and drying performance in a domestic tumble dryer, mechanical properties of 10 different fabrics were measured and compared. A video capturing and processing system was utilized to track and record the centroid of the tracer fabric for characterizing fabric dynamics during the drying process. Based on the observation of motion trajectory, velocity profile and residence time distribution of the tracer fabric for each fabric, 10 motion indexes were established, and the correlation between fabric motion and drying performance was investigated. The results showed that fabric motions in a whole drying process were complicated and could be divided into four categories. The major property which influenced textile motion was weight of the fabric determined by ANOVA and the post hoc test using the LSD method. Various fabric motions during the drying process due to fabric mechanical properties had a direct impact on drying performance. Motion indexes were examined with their relation to the drying performance based on the regression analysis. The regression models to predict the two drying indexes by the motion indexes were developed with an explanatory power of around 90%.

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