On the construction and use of linear low-dimensional ventilation models

Area of Science:

• Fluid dynamics
• Computational modeling
• Environmental engineering

Background:

• Accurate indoor air quality monitoring and control are crucial for ventilation applications.
• Traditional computational fluid dynamics (CFD) simulations can be computationally expensive for real-time control.

Purpose of the Study:

• To derive and construct fast, reliable, linear low-dimensional models for indoor ventilation.
• To enable the reconstruction of pollutant concentration fields from any source distribution.
• To provide efficient tools for monitoring and control of ventilation systems.

Main Methods:

• Utilized a discrete Green's function approach to derive models from Navier-Stokes and pollutant transport equations.
• Investigated decoupling of flow and concentration equations under specific conditions (high air-to-pollutant mass-flow ratio, α).
• Employed Reynolds-averaged Navier-Stokes (RANS) simulations for model derivation and validation.

Main Results:

• Successfully derived linear low-dimensional ventilation models capable of reconstructing concentration fields.
• Demonstrated model efficiency compared to direct CFD simulations for estimating contaminant distributions.
• Quantified model errors related to low-dimensional resolution and low α values.

Conclusions:

• Linear low-dimensional ventilation models offer an efficient alternative to CFD for estimating indoor contaminant concentrations.
• These models facilitate enhanced monitoring and control of ventilation systems for contaminant removal.
• The methodology allows for reconstruction of concentration fields from arbitrary pollutant source distributions.