Year: 2025
Type: dissertation
Abstract: Maintaining healthy indoor environments is crucial for occupant well-being and productivity, necessitating optimal indoor air quality (IAQ) and thermal comfort. Airflow significantly impacts these factors, underscoring the need for accurate assessments and visualizations. Schlieren imaging, a technique traditionally used in fluid dynamics, provides high-resolution airflow visualizations without the need for seeding particles. However, it remains underutilized in indoor climate research, particularly for quantitative assessments, with existing literature revealing gaps in validation and application guidelines. This research explores and validates schlieren imaging for both qualitative and quantitative analysis of indoor airflow. The study involved designing and constructing a large-scale schlieren setup and developing novel methodologies, including Optical Flow Schlieren Image Velocimetry (OFSIV) for estimating airflow velocities from schlieren image sequences and Computational Schlieren Imaging (CSI). These methods were validated against conventional sensor data and Computational Fluid Dynamics (CFD) simulations, focusing on three common indoor airflow scenarios: human thermal plumes, human exhalation, and ventilation fan airflows. The findings demonstrate that the schlieren setup effectively visualizes temperature variations as small as 0.25 K and captures complex indoor airflow patterns. The methodologies, particularly OFSIV, showed strong correlations with conventional sensor data and CFD results, confirming schlieren imaging as a reliable tool for indoor climate studies. Overall, this research establishes schlieren imaging as a valuable tool for indoor climate studies, offering a novel approach for qualitative and quantitative airflow analysis and providing practical guidelines for future research.
DOI: 10.25643/dbt.68219
Author: Gena, Amayu
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7104823427
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