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Optimisation of Induced Steam Residual Moisture Content in a Clothing Conditioner Based on a Genetic Algorithm

Author

Listed:
  • Arslan Saleem

    (School of Mechanical Engineering and IEDT, Kyungpook National University, Daegu 41566, Korea
    School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, Wales, UK)

  • Muhammad Saeed

    (Abu Dhabi Maritime Academy, Abu Dhabi P.O. Box 54477, United Arab Emirates)

  • Man-Hoe Kim

    (School of Mechanical Engineering and IEDT, Kyungpook National University, Daegu 41566, Korea)

Abstract

This paper presents the modelling of heat and moisture transfer in a clothes-conditioning unit with the aim of improving the moisture content distribution to the clothes. A multicomponent, non-reacting, two-phase Eulerian–Eulerian model was utilised to solve the computational model. The clothes inside the conditioning unit were modeled as retangular towels (porous medium) of uniform thickness. Mass flow distribution of air and steam through the clothes was studied by systematically varying the steam nozzle angle (30° to 75°) and air inflow grill angle (45° to 105°). The simulation results were studied to identify the impact of design parameters on the mass flow distribution inside the clothes-conditioning unit. The mass flow of steam and the air–steam mixture were calculated through each towel in the forward and reverse direction. Response surface analysis was conducted to correlate the total mass flow rate and steam mass flow rate through each towel with the design variables. Moreover, a multiobjective genetic algorithm was employed to optimise the mass flow through the clothes and ascertain the optimal design configuration. The geometric configuration with a steam nozzle angle of 45° and air grill angle of 105° resulted in optimal steam and mixture distribution.

Suggested Citation

  • Arslan Saleem & Muhammad Saeed & Man-Hoe Kim, 2022. "Optimisation of Induced Steam Residual Moisture Content in a Clothing Conditioner Based on a Genetic Algorithm," Energies, MDPI, vol. 15(15), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5696-:d:881315
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    References listed on IDEAS

    as
    1. Saleem, Arslan & Kim, Man-Hoe, 2020. "Aerodynamic performance optimization of an airfoil-based airborne wind turbine using genetic algorithm," Energy, Elsevier, vol. 203(C).
    2. Saleem, Arslan & Kim, Man-Hoe, 2019. "Performance of buoyant shell horizontal axis wind turbine under fluctuating yaw angles," Energy, Elsevier, vol. 169(C), pages 79-91.
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