Indoor Airflow Imaging Using Physics-Informed Background-Oriented Schlieren Tomography
This work addresses the challenge of indoor airflow imaging for applications like ventilation monitoring, but it is incremental as it builds on existing background-oriented schlieren and physics-informed neural network methods.
The paper tackles the problem of non-invasive volumetric indoor airflow estimation from a single viewpoint by developing a framework that uses background-oriented schlieren measurements and physics-informed reconstruction, achieving improved accuracy through enhanced ray tracing, physics-based rendering, and regularization with a physics-informed neural network.
We develop a framework for non-invasive volumetric indoor airflow estimation from a single viewpoint using background-oriented schlieren (BOS) measurements and physics-informed reconstruction. Our framework utilizes a light projector that projects a pattern onto a target back-wall and a camera that observes small distortions in the light pattern. While the single-view BOS tomography problem is severely ill-posed, our proposed framework addresses this using: (1) improved ray tracing, (2) a physics-based light rendering approach and loss formulation, and (3) a physics-based regularization using a physics-informed neural network (PINN) to ensure that the reconstructed airflow is consistent with the governing equations for buoyancy-driven flows.