润湿
水分
材料科学
环境科学
地表径流
涂层
吸水率
吸收(声学)
复合材料
毛细管作用
岩土工程
地质学
生态学
生物
作者
Ali Karim,Angela Sasic Kalagasidis,Pär Johansson
标识
DOI:10.1016/j.buildenv.2023.110905
摘要
Aerogel-based coating mortars (ACM-systems) provide energy-efficient retrofitting solutions for masonry buildings, with thermal conductivities (30–50 mW/(m·K)) comparable to traditional insulation materials. However, limited knowledge on their moisture absorption under rainwater wetting hinders the moisture-safe design of building envelopes incorporating these mortars. Thus, this study investigated the moisture absorption of an ACM-system under three laboratory-created wetting scenarios. A small-scale setup was developed to simulate runoff wetting based on typical wind-driven rain intensities in Sweden, enabling continuous monitoring of moisture conditions during wetting and drying. Two complementary capillary suction experiments under zero (free suction) and elevated hydrostatic pressure explored additional wetting scenarios. The impact of water-repellent paint and surface cracks was also assessed, as previous testing focused on undamaged ACM-systems. Among the three wetting scenarios, runoff wetting resulted in the lowest moisture absorption by the undamaged ACM-system. Water-repellent paint (sd = 0.01 m) reduced moisture uptake by up to 15% during runoff and 50% during free capillary suction for the same system. Horizontal or vertical surface cracks of 1 ± 0.5 mm width increased water absorption by 3–5 times during prolonged runoff wetting, comparable to suction at elevated hydrostatic pressure. Furthermore, a trial was done to verify a simplified numerical moisture transport model using the runoff experiment. The results highlighted the necessity for future model refinement and advanced moisture transport modeling in the ACM-system. The developed small-scale setup facilitated easy use and real-time monitoring of moisture conditions during wetting and drying. Future development should include wind-driven rain simulation alongside the existing runoff wetting scenario.
科研通智能强力驱动
Strongly Powered by AbleSci AI