2026-05-05T11:25:28Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/824022026-04-08T11:05:04Zcom_10324_1167com_10324_931com_10324_894col_10324_1238

2026-01-30T11:28:02Z urn:hdl:10324/82402 Application of deep neural networks for leakage airflow rate estimation from three‐dimensional thermal patterns Tamayo Alonso, Diego Poza Casado, Irene Meiss Rodríguez, Alberto Producción Científica 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. 2026-01-30T11:28:02Z 2026-01-30T11:28:02Z 2026 info:eu-repo/semantics/article Indoor Air, 2026, vol. 1,.5960599, 15 pp.. 0905-6947 https://uvadoc.uva.es/handle/10324/82402 10.1155/ina/5960599 1 1 15 Indoor Air 2026 1600-0668 eng https://onlinelibrary.wiley.com/doi/10.1155/ina/5960599 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional John Wiley & Sons A/S