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In this study, a passive ventilation and cooling technique which is widely used in sustainable building designs was investigated by a numerical simulation based on computational fluid dynamics (CFD). A system of a solar chimney (SC) and an Earth-to-Air Heat Exchanger (EAHE) is able to ensure thermal comfort in a house by generating an air flow which is induced by the thermal effect. The solar chimney drew ambient air (at 35°C) through the inlet of EAHE; the air flow was cooled by a lower temperature of the underground pipes (at 20°C) before circulating inside the house and leaving through the outlet of the solar chimney. Both the openings of the system were exposed to the atmosphere, and the atmospheric pressure was set at the inlet of the EAHE pipe and the outlet of the solar chimney. The performance of this ventilation and cooling system was analyzed under effects of design parameters, including the gap of the chimney, the diameter and length of the EAHE pipe as well as the solar heat flux applied on the wall of the air channel of the chimney. The results of the assessed parameters, such as temperature inside the house, show that the indoor temperature was reduced to 25°C and 26°C, respectively for the forced ventilation case of 0.2 m/s and natural ventilation case in the configuration with the EAHE pipe length of 11m. The coupled solar chimney and EAHE system can achieve the thermal comfort conditions for both cases of natural and forced ventilation.

Solar chimneys have been intensively studied as an effective method for natural ventilation of buildings. Though numerical methods, such as Computational Fluid Dynamics (CFD), have been widely utilized in such studies, they usually require extensive computational resources. Moreover, experimental study is quite complicated and costly. In recent years, machine learning has started to be used as a tool in the thermal-fluid field. In this study, in order to save time and cost, Adaptive Neuro-Fuzzy Inference System (ANFIS) technique, a class of adaptive networks that incorporate both neural networks and fuzzy logic principles, is combined with CFD. A simulation model was first validated by experiment from another study in the field. The result was documented as a dataset using CFD code ANSYS Fluent (Academic version 2020 R2). Then, they are used to train and validate the ANFIS model. In particular, the study is to predict the fluid flow field in a 2-dimensional typical solar chimney when heat flux changes in the range of 400 to 1000 W/m2. Inputs of the ANFIS model are position and heat flux, while outputs are temperature and velocity at that location. As a result, the 2 ANFIS models could achieve R2 values of 0.997, 0.97 (training set) and 0.994, 0.9715 (testing set); RMSE are 1.009, 0.00224 (training set) and 1.074, 0.0204 (testing set) for outputs of temperature and velocity, respectively. Those results are acceptable. By using the ANFIS model, large amounts of flow fields with different scenarios can be estimated simultaneously. Therefore, it is expected that engineers and architects can have a quick tool in the process of design.

Solar chimneys absorb solar radiation for natural ventilation or cooling of buildings. In typical solar chimneys, the air flow is induced by the thermal effect which causes pressure gradient inside the chimney to deviate from that of the ambient air. The induced flow is employed to ventilate the connected building naturally. In this study, we aimed to boost the induced flow rate through a typical solar chimney by a forced flow at the inlet of the air channel which is divided into two ports for the forced and the induced flows. The air flow and heat transfer were modeled with a Computational Fluid Dynamics (CFD) model with the ANSYS Fluent CFD code. Changing factors included the forced velocity, the dimensions of the inlet port for the forced air flow and the length of the partition between the ports of the naturally induced and forced air flows. It is seen that the forced flow significantly reduces the pressure in the air channel compared to that of a typical chimney; hence increasing the induced flow rate. The flow rate was significantly enhanced as the forced velocity was above 0.2 m/s; the area of the inlet port for the forced flow was 12.4% of the total inlet area; and the partition height was less than 5.0% of the total height of the air channel. In this configuration, the forced flow can be the waste exhaust air flow from other mechanical ventilation systems of the building. Therefore, no additional energy is required for this solution.

Numerical simulations, particularly those based on the Computational Fluid Dynamics (CFD) Method, have been widely employed in engineering and design, such as design of green buildings. In this study, we investigated effects of the computational domain, a factor influencing the accuracy of a CFD simulation, of a solar chimney, a device for natural ventilation of buildings, on the predicted performance of a solar chimney attached to a building. Four different domains which, in turn, included both the chimney and the house, the chimney and the inlet length, the chimney with the horizontal inlet, and only the air channel of the chimney, were examined. The CFD model was based on the RANS equations and RNG k – ∊ turbulence model. The chimney had different heights, gaps, heat fluxes, and the location of the heat source in the air channel. The results show that the differences in the predicted flow rate and temperature rises through the chimney among the domains changed with the height, gap, and the location of the heat source. The simplest domain, which was a simple vertical rectangular channel, over – predicted the flow rate and under – predicted the temperature rise. The main cause of its worse performance is because of its inability to model the separation zones near the inlet of the air channel. Therefore, it is not recommended for high accuracy – simulations. Two other simpler domains, which included the inlet length and only the inlet, can be used when the required accuracy is within 10.0%. The full domain consisting of both the chimney and the building is preferred for the highest accuracy.

In this study, effects of the contractions of the inlet and outlet areas of a vertical solar chimney were examined with a Computational Fluid Dynamics. The results show that the flow rate decreased with both the inlet and outlet areas. The outlet area influenced more than the inlet area did. The flow rate decreased linearly with the outlet area, however, was affected significantly only when the inlet area was below half of the air gap.