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A hollow block ventilation wall (HBVW) is an innovative construction where the block cavity of the wall is used as a ventilation duct. Low-grade energy can be used to remove heat stored in the structure in summer and warm the structure in winter. Therefore, the cooling and heating loads of buildings can be significantly reduced. Therefore, an experimental study was conducted to investigate the thermal performance of HBVW under different airflow temperatures and rates. The temperatures and heat flux of HBVW surfaces and the airflow temperatures in cavity were tested on a typical summer day. The results show that the air velocity in the cavity has a great influence on the thermal performance. Compared with a non-ventilation wall, the thermal resistance increases from 0.637 m 2 K/W to 1.61, 3.06, 3.86, and 4.12 m 2 K/W when the air velocity is 0.3, 0.9, 1.5, and 1.9 m/s, respectively, and the heat flux transferred through the inner surface can be decreased by 40.5%, 73.3%, 79.4%, and 82.6%. The average temperature of the inner surface can be respectively decreased by 1.8, 2.8, 3.0, and 3.1 °C. The airflow temperature has little effect on the equivalent heat resistance, but it does greatly affect the inner surface temperature. [ABSTRACT FROM AUTHOR]

Aiming at reducing the heating and cooling loads for built environments, the thermal resistance and capacitance (R C) model has widely been acknowledged as an effective method for predicting the heat flux through building walls by simplifying wall structures and their properties into R and C allocations. This paper demonstrates an inverse optimization method based on particle swarm optimization (PSO) for determining the optimal R C allocation that minimizes the heat flux through building walls. A thermal R C model with three resistances and two capacitances was used as an example to search for the optimal building R C allocation in the hot summer and warm winter zone of China. The inverse optimization could be efficiently accomplished in 2.5 h with an ordinary computer, approximately 0.12% of the time consumed by using the exhaustive search method. The optimized R C allocation was composed of three resistances of 0.43, 0.18 and 0.39 and two capacitances of 0.50 and 0.50. Compared to the three typical thermal insulation (e.g., internal, external, and internal/external), the optimal R C allocation could reduce the heat flux into/from buildings by 17.3%–44.3%. The proposed inverse PSO method shows an effective and efficient capacity in searching for the optimal R C allocation of thermal R C models. • An inverse PSO method for the optimal RC allocation is proposed. • Optimization shows fewer steps and less time cost. • An optimal RC allocation is obtained. • Heat flux is reduced by 17.3%, 34.9% and 44.3% than three traditional RC allocations. • The effect mechanism of RC allocation on heat flux is explored. [ABSTRACT FROM AUTHOR]