A high-speed video camera was used to track a tracer textile as it is tumbled among other textiles in a domestic tumble dryer under different operating conditions, with the aim of investigating the mechanisms by which the mechanical action is imposed on textiles and affects drying performance during drying. These mechanisms were first recognized by comparing the clothes drying process to other well-researched chemical engineering processes. From the observation of the recorded motion processes, cotton textile transverse motion can be divided into three categories and a motion index system was derived to characterize the type of motion undergone. The impact of textile transverse motion on drying performance was numerically discussed based on the results of an analysis of variance and regression analysis. Results indicated that textile dynamics with more complexity and flexibility tended to have more mixing, shortened distance of moisture migration inside the fabric layer and fewer wrinkles formed, resulting in higher potential to have a better drying performance.

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The purpose of this study is to investigate the effect of drying parameters on drying performance in an air-vented tumble dryer, and to optimize its drying performance by adjusting parameters. The critical drying parameters that influenced specific moisture extraction rate (SMER), final moisture content, evenness of drying, and smoothness appearance were determined by the analysis of variance in JMP software, which were rotational speed of the motor and load size, with clearly significant individual effects and binary interactions. In order to improve the applicability of the outcomes obtained in this study and to take into account the interactions between drying parameters and drum structure parameters on drying efficiency, non-dimensional analysis was used and the correlation between drying efficiency and dimensionless variables was studied. The Buckingham Pi theorem was applied to the problem to derive dimensionless Pi terms upon which the drying efficiency depends. A step regression analysis was then conducted to test the assumption that SMER was influenced by the dimensionless parameters based on the standard least squares fitting. Results indicated that the regression model showed an explanatory power of 73.8%. By adjusting the dimensionless parameters in the model, an optimized energy-saving drying program was obtained with the desirability goal of reducing the value of SMER. Compared with the original program, energy efficiency was improved by 32.4%.