An experimental methodology for the calibration of indoor building environment models using thermal point clouds and CFD simulation

This paper presents an experimental methodology employing thermal point clouds (TPCs) of indoor spaces to develop and calibrate Computational Fluid Dynamics (CFD) models for simulating indoor building environments. TPCs, captured at various time intervals, provide geometry and surface temperatures o...

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Bibliographic Details
Authors: Ramón Constantí, Amanda, Adán Oliver, Antonio, Castilla Pascual, Francisco Javier, Pérez Andréu, Víctor José
Format: article
Publication Date:2024
Country:España
Institution:Consejo General de la Arquitectura Técnica de España (CGATE)
Repository:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/3814
Online Access:http://hdl.handle.net/20.500.12251/3814
https://doi.org/10.1080/17512549.2024.2358923
Access Level:Open access
Keyword:Nube de puntos
Comportamiento térmico
Ámbitos habitacionales
Edificación residencial
Sensorización
3311.02 Ingeniería de Control
3311.16 Instrumentos de Medida de la Temperatura
3305.90 Transmisión de Calor en la Edificación
3305.14 Viviendas
Description
Summary:This paper presents an experimental methodology employing thermal point clouds (TPCs) of indoor spaces to develop and calibrate Computational Fluid Dynamics (CFD) models for simulating indoor building environments. TPCs, captured at various time intervals, provide geometry and surface temperatures of the space envelope are crucial for initial and final contrast parameters in model calibration. Segments from TPCs of interior surfaces serve as ground truth for calibrating theoretical CFD models. This involves adjusting boundary conditions to approximate real surface temperature distributions on selected walls. The innovative calibration approach incorporates automatic wall segmentation processes, enabling comparison between in-situ measured and simulated values. A case study assesses the similarity between real thermal orthoimages and CFD simulations, employing qualitative and quantitative analysis with a convergence criterion. Results validate the methodology, highlighting the need for further automation of manual processes.

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