During the SARS-CoV-2 pandemic, the air quality and infection risk in classrooms were the focus of many investigations. Despite general recommendations for sufficient ventilation, quantitative analyses were often lacking due to the large number of combinations of boundary conditions. Hence, in this paper, we describe a computational fluid dynamics model that predicts the time-resolved airflow for a typical 45 $\min$ classroom scenario. We model 28 students and a teacher, each emitting CO₂ and an individual aerosol. We investigated 13 ventilation setups with window or mechanical ventilation and different positions and operating conditions of an additional air purifier. The ventilation performance is assessed by evaluating the ventilation effectiveness, aerosol removal effectiveness, local air exchange efficiency and overall inhaled aerosol mass of the occupants, which is a measure of the infection risk. If the window is opened according to the “20-5-20” recommendation, the incoming airflow reduces both the CO₂ and aerosol concentration whilst decreasing the thermal comfort at low ambient temperatures. An active air purifier enhances aerosol removal, but, depending on the position, the local air exchange efficiency and individual aerosol inhalation vary. If mechanical ventilation with 700 m³/h is utilised, the CO₂ concentration is kept below 1250 ppm while also effectively removing aerosol from the classroom.

The acoustic emissions of ventilation systems and their subcomponents contribute to the perceived overall comfort in indoor environments and are, therefore, the subject of research. In contrast to fans, there is little research on the aeroacoustic properties of air diffusers (often referred to as outlets). This study investigates a commercially available ceiling swirl diffuser. Using a hybrid approach, a detailed three-dimensional large-eddy simulation is coupled with a perturbed wave equation to capture the aeroacoustic processes within the diffuser. The flow model is validated for the investigated operating point of 470 m³/h using laser-optical and acoustic measurements. To identify the noise sources, the acoustic pressure is sampled with various receivers and on cut sections to evaluate the cross-power spectral density, and the sound-pressure level distribution on cut sections is evaluated. It is found that the plenum attenuates the noise near its acoustic eigenmodes and thus dominates other noise sources by several orders of magnitude. By implementing the plenum walls as sound-absorbing, the overall sound-pressure level is predicted to decrease by nearly 10 dB/Hz. Other relevant geometric features are the mounting beam and the guide elements, which are responsible for flow-borne noise emissions near 698 Hz and 2699 Hz, respectively.

The increasing utilisation of demand-controlled ventilation strategies leads to the frequent operation of fans under part-load conditions. To accurately predict the energy demand of a ventilation system with a fan array in the early design stages, models that calculate reliable results across the whole operating range are required. We present the comparison of a polynomial and a machine learning approach through support vector regression (SVR) to predict the fan performance over a wide range of typical operating points. For fitting and validation, we use experimental data. We investigate the extrapolation performance of both approaches. The SVR model achieves a slightly better representation of the experimental data with a lower error, especially when only sparse data are available. Both approaches yield similar results when the evaluation is conducted within the experimentally captured domain but deviates outside the domain. At operating points that are far from the experimentally captured domain, the polynomial models yield fan efficiencies that are physically plausible, while the SVR models drastically overpredict the fan efficiency. To rate the influence of such deviations towards modelling the actual energy demand, both approaches are applied to an operation simulation of a simplified office building. Both approaches yield similar results despite differing extrapolation capabilities.

People in industrialised countries spend most of their days indoors and are often exposed to ventilation sounds. Since ventilation systems are not only used in industry but also increasingly in residential buildings, it is important to study its sound radiation and the consequences for our well-being. This research project aims to identify if geometric features of air diffusers affect the rated annoyance of ventilation systems. It is known that the A-weighted sound serves as a criterion to a limited extent. Therefore, psychoacoustic analyses and evaluations need to be included to find out if there are correlations between psychoacoustic features and flow phenomena caused by the geometric features of the air diffusers. Several air diffusers were acoustically measured in a hemi-anechoic room using a developed mobile ventilation unit that supplied the necessary air volume flow. The ventilation unit could be operated in both supply and extract air configuration of the diffusers. The measured data not only includes the acoustic directivity of the air diffusers but also recordings that are needed to conduct listening tests. To increase the ecological validity and to study the role of the environment, future listening experiments will be also extended to audio-visual experiments in virtual reality.