Vol. 3 No. 1 (2025): Advanced Technologies in Smart and Sustainable Energy for Carbon Neutrality Transformations

This issue entitled "Advanced Technologies in Smart and Sustainable Energy for Carbon Neutrality Transformations" will feature papers on carbon-neutral districts and climate change mitigation via cleaner power production, energy-efficient system design, and urban planning. Clean energy supply, energy system modeling, and multi-objective optimization are included. The deadline for submissions is 15 December 2024, and Volume 3, Issue 1 will be released in at the end of March 2025. 

Published: 2025-03-31

Article

  • Open Access

    Article ID: 249

    Investigation of hygrothermal behavior of a novel bio-based panel: Experiment and numerical simulation

    by Yaping Zhou, Abdelkrim Trabelsi, Li Xiang, Mohamed El Mankibi
    Clean Energy Science and Technology, Vol.3, No.1, 2025;
    50 Views
    DOI: https://doi.org/10.18686/cest249

    Straw composites, owing to their low carbon footprint and favorable hygrothermal properties, are becoming a promising alternative insulation material for buildings in order to promote energy saving and occupants’ comfort. However, the heat and moisture characteristics of straw composites at the material scale and under steady-state condition are insufficient for a thorough assessment of their performance as a building component in actual service conditions. This study focused on the hygrothermal performance of a novel bio-based wall made with a rice straw–alginate composite material. The temperature and relative humidity profiles within the wall were monitored under various boundary conditions. The inverse analysis method was proposed to determine liquid water permeability. In in a dynamic test, compared with the model of coupled heat-and-moisture transfer (CHM), the transient heat transfer model predicted temperature profiles with higher errors and underestimated total heat flux by up to 30.6%. Also, under the dynamic condition, the CHM model with liquid water transport showed decreased mean absolute errors by 61%, 57% and 8% at depths of 28 mm, 36 mm and 64 mm, respectively, compared with those predicted by the CHM model without liquid water transport. Both vapor transport and liquid transport seemed to be essential when modeling thermal transfer and moisture transfer through the wall.