The thermal indoor climate is a complicated combination of a number of physical variables, all of which strongly affect people’s well-being. The indoor climate not only heavily affects people’s health and life quality, but also their productivity and ability to work efficiently.

One of the reasons why so many problems are associated with indoor climate is that it is more or less invisible; it is hard to understand something that cannot be seen. In particular, the near-zone of supply air diffusers in displacement ventilation is very critical. Complaints about drafts are often associated with this type of ventilation system.

The main aim of this research is to improve the knowledge of the whole-field techniques used to measure and visualize air temperatures and pollutant concentrations. These methods are explored with respect to applicability and reliability. Computational Fluid Dynamics (CFD) has been used to predict the velocity and temperature distributions and to improve the current limitations.

Infrared thermography is an excellent technique for visualization of air temperature and airflow pattern, particular in areas with high temperature gradient, such as close to diffusers. It is applicable to both laboratory and field test environments, such as in industries and workplaces. For quantitative measurements the recorded temperatures must be corrected for radiation heat exchange with the environment, a complicated task since knowledge about the local heat transfer coefficients, view factors and surrounding surfaces are needed to be known with good accuracy.

Computed tomography together with optical sensing is a promising tool in order to study the dispersion of airborne pollutants in buildings. However, the design of the optical sensing configuration and the reconstruction algorithm has a major influence on the performance of this whole-field measuring technique. A Bayesian approach seems to be a rational choice for reconstruction of pollutant concentration indoors, since it avoids the high noise sensitivity frequently encountered with many other reconstruction methods. A modified Low Third Derivative (LTD) method has been proposed in this work that performs well particular for concentration distributions containing steep gradients and regions with very low concentrations.

CFD simulation is a powerful tool for visualization of velocities, airflow pattern and temperature distribution in rooms. However, for predictions of the absolute value of the physical variables the CFD model have to be validated against some reference case with high quality experimental data. CFD predictions of air temperatures and velocities close to a complex supply diffuser are very troublesome. The performance of CFD prediction of the airflow close to a complex supply diffuser depends mainly on the accuracy of the diffuser, turbulence and wall treatment modeling