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Application of Artificial Neural Networks (ANN) for modeling Personalized Ventilation Systems (PVS) using CFD data
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DOI：10.4025/actascitechnol.v48i1.74146.png)
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Abstract
En 中文
This study investigates the application of Artificial Neural Networks (ANNs) for modeling Personalized Ventilation Systems (PVS) using data from Computational Fluid Dynamics (CFD) simulations. In recent years, machine-learning techniques like ANNs have been increasingly used to accelerate CFD analysis and improve the accuracy of temperature and airflow velocity predictions in indoor environments. The methodology involved conducting twelve CFD simulations in a three-dimensional space, followed by data filtering and normalization to train and test the neural network. The room was composed of two individuals, positioned side by side, both seated and receiving air from a ceiling supply system. Both individuals were modeled to maintain a constant surface temperature while also transferring heat to the environment. The quality of the results were analyzed by comparing the neural network outputs with data that had been omitted from the network. The results demonstrated the effectiveness of the model, with average errors ranging from 1% to 3% and maximum errors between 6% and 15%. This approach significantly reduces the computational time required for traditional CFD simulations while maintaining high accuracy, offeringpromisingprospects for optimizingPVS performance in various indoor settings. The use of machine learning makes the analysis and design of personalized ventilation systems faster and more efficient, with practical applications in offices, classrooms, and residential spaces.
Keywords:
Machine Learning
Computational Fluid Dynamics
Air Conditioning
Predictive model
Indoor airflow
Journal
A
IF:
0.6
Papers:
31
Citations:
563
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Cited Papers