Publication:
Prediction of flow field in a solar chimney using ANFIS technique
Prediction of flow field in a solar chimney using ANFIS technique
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Date
2021
Authors
Minh-Thu T Huynh
Tri Q Truong
Thinh N Doan
Trieu N Huynh
Tung V Nguyen
Viet T Nguyen
Y Q Nguyen
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Research Projects
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Abstract
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
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Keywords
solar chimney,
CFD,
machine learning,
ANFIS