RT info:eu-repo/semantics/article
T1 Application of Deep Neural Networks for Leakage Airflow Rate Estimation From Three‐Dimensional Thermal Patterns
A1 Tamayo Alonso, Diego
A1 Poza Casado, Irene
A1 Meiss Rodríguez, Alberto
K1 building airtightness
K1 deep convolutional neural network
K1 infiltration
K1 pressurisation test
K1 thermography
AB The employment of deep convolutional neural networks (CNNs) signifies a substantial progression in the domain of image analysis. The application of this method is particularly suitable when the image set represents a spatial structure and predictive analysis can only be performed using Gaussian processes, which are computationally complex. The uncontrolled airflow of air into buildings, known as infiltration, poses a significant challenge in terms of characterisation and quantification. The irregular contours of gaps and cracks through the enclosure create a virtually endless variety of cases, making a generalizable scientific interpretation that can be applied to existing buildings very difficult. This circumstance is always clearly manifested by an irregular, three-dimensional incoming airflow. This study presents an innovative methodology for estimating airflow rates based on three-dimensional thermal patterns captured through infrared thermography. The experimental setup employs a 3D-printed matrix of spheres, facilitating the characterisation of the spatial temperature distribution within the airflow. The resulting thermal images are processed using a CNNs, which integrates the spatial information contained in the thermograms with a scalar input representing the inlet air temperature. The model′s performance was assessed under a range of conditions, including reduced image resolutions, varying experimental configurations (involving different flow apertures) and a comparison between full thermographic inputs and thermal difference-based features. The results indicate that the model can accurately infer airflow rates within the same aperture (medium absolute error [MAE] < 2%). While generalisation to new apertures presents a greater challenge, the experiments demonstrate that a sufficiently diverse training dataset can enhance the model′s predictive capacity for configurations not included in the training phase. These findings underscore the potential of deep learning as a nonintrusive and efficient tool for estimating airflow in systems where conventional measurement techniques are either difficult to apply or impractical.
PB John Wiley & Sons A/S
SN 0905-6947
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/82402
UL https://uvadoc.uva.es/handle/10324/82402
LA eng
NO Tamayo-Alonso, D., Poza-Casado, I., & Meiss, A. (2025). Application of Deep Neural Networks for Leakage Airflow Rate Estimation From Three-Dimensional Thermal Patterns. Indoor Air, 2026(1), 5960599. https://doi.org/10.1155/ina/5960599
NO Producción Científica
DS UVaDOC
RD 21-feb-2026