Experimental and numerical investigation of the cooling performance of a solar chimney integrated with a humidification system

被引:0
作者
Ali, Mahmud Hussain [1 ,3 ]
Mawlood, Mahmood Khalid [2 ]
Jalal, Rawand Ehsan [3 ]
机构
[1] Salahaddin Univ, Coll Engn, Mech & Mechatron Engn Dept, Erbil, Iraq
[2] Univ Kirkuk, Coll Engn, Kirkuk, Iraq
[3] Univ Kirkuk, Coll Engn, Mech Engn Dept, Kirkuk 36001, Iraq
关键词
Solar chimney; evaporative cooling; air change per hour; CFD; ANN; TROMBE WALL; VENTILATION; SIMULATION; BUILDINGS; HOT;
D O I
10.1177/17442591241276392
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Solar chimneys are efficient systems for natural ventilation based on thermal principles. However, in regions with high temperatures it may not serve to ensure a comfortable indoor environment. To overcome this challenge, integrating air humidification into the ventilation process becomes necessary. The present study investigates the feasibility of integrating a solar chimney with a humidification setup for evaporative cooling in the climate of Kirkuk, Iraq. This investigation utilizes both experimental and numerical techniques. Numerical simulations carried out using a finite volume approach and validated against the experimental data. The experimental outcomes highlight the effectiveness of evaporative cooling in lowering outdoor air temperatures entering the room from months May to August in the range 5 degrees C-10 degrees C. A parametric analysis of the system indicates that increasing water supply can enhance the rate of evaporative cooling and lower the room temperature while elevating relative humidity. Additionally, higher humidity diminishes air buoyancy, leading to a significant decrease in air changes per hour (ACH) rate. The findings also show that both air changes per hour and water consumption reach their peak at a chimney height of 0.5 m. Moreover, a direct correlation is observed between chimney width and both ACH and water consumption. Expanding the chimney width results in higher ACH and increased water consumption for evaporative cooling. Artificial neural network (ANN) model demonstrates robust predictive accuracy, evidenced by high regression coefficients of 0.9566 for ACH and 0.9505 for room temperature correlated from computational fluid dynamics (CFD) results. This underscores ANN model effectiveness in forecasting system performance under varying input conditions, providing valuable insights for different scenarios and configurations of the system.
引用
收藏
页码:767 / 793
页数:27
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