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.

With rising insulation standards, the use of mechanical ventilation, especially in non-residential buildings, is becoming increasingly relevant. To ensure thermal comfort and avoid health problems for people in the room, cost- and energy-intensive humidification of the supply air is necessary. The use of moisture recovery systems can thus significantly reduce the energy consumption of ventilation systems. Despite this energy-saving potential, moisture recovery systems are rarely used in ventilation systems. To forecast the efficiency of moisture recovery systems in partial load operation and under different climatic conditions, a dynamic model of a membrane-based enthalpy exchanger was developed in the object-oriented modelling language Modelica. The model is based on the solution diffusion model, a quite common approach. In contrast to the models found in the literature, the sorption process is not assumed to be in equilibrium state. Rather, as a first approach the membrane’s permeance, consisting of the solubility and diffusion coefficient, is modelled with a linear dependency on the moisture difference between the two incoming airflows. A parameter fitting has been carried out with experimental data to determine the unknown material parameters. The model containing the fitted parameter set was validated using different experimental data.

Healthy and comfort indoor air becomes more relevant for the persons working or living in buildings. Keeping the indoor air temperature in a comfortable range is common for room air conditioning. Nevertheless, keeping the moisture in a comfortable range is often neglected, as the influence of too dry or too humid air on health is underestimated. To predict the moisture content in indoor air environments over yearly periods, we enhanced a dynamic building model with a moisture balance. Using this model, we analyzed the influence of moisture recovery systems in residential ventilation on the indoor air humidity compared to heat recovery systems. The comparison shows that moisture recovery systems significantly decrease the number of hours of dry air during a yearly period.

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.