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Session Chair: Dr. Marc Abadie, La Rochelle Université
Location:Room 2 - Room 011, Building: 116
11:40am - 11:43am
Correlating covid19 and indoor health: how the lockdown and the work-from-home trends are changing the hygrothermal profiles of our homes
Arianna Brambilla1, Alberto Sangiorgio2
1The University of Sydney, Australia; 2Grimshaw Architects, Sydney, Australia
In 2020 the residential sector witnessed a complete transformation of the way people live and occupy the spaces. Indeed, different Countries introduced total lockdowns as a measure to contain and prevent the spread of covid19, forcing people to stay at home more and performing activities and tasks that were usually done elsewhere. These measures clearly impact the indoor hygrothermal environment. Nonetheless, higher internal thermal loads and moisture generation rate may create the perfect situation to support mould growth. Considering that mould is associated with adverse health symptoms and that one on three houses already suffers from biological infestation; the possibility of an increased occurrence may pose a significant health hazard.
This project aims to understand the impacts of cyclical lockdowns or increased work-from-home practices on the hygrothermal performance of residential buildings with a strong focus on mould growth occurrence.
The assessment uses a two-step methodology: firstly, whole building transient simulations (software trnsys) are used to generate the indoor temperature and humidity profiles, secondly hygrothermal transient simulations (software WUFI) are used to quantify the risk of mould growth. A newly built energy efficient building in Melbourne is used as model, as it started to show mould issues during the lockdown. Different occupations profiles are created to represent a broad variety of combinations of number of occupants, time spent indoors, and activities performed.
This research provides a snapshot of the increased risk of mould growth in the indoor environment supported by new occupation trends. It opens the discussions about the current design practices and policy frameworks, which fail to provide a prevention agenda to designers and rather focus on the remediation aspects.
11:43am - 11:46am
A simplified model to estimate COVID19 transport in enclosed spaces
Parham A Mirzaei1, Mohammad Moshfeghi2, Hamid Motamedi Zoka3, Yahya Sheikhnejad4, Hadi Bordbar5
1The University of nottingham, United Kingdom; 2Sogang University , South Korea; 3Tarbiat Modares University, Iran; 4University of Aveiro, Portugal; 5Aalto University, Finland
Virus-laden respiratory droplets are the primary route of COVID19 transmission, which are released from infected people. The strength and amplitude of a release mechanism strongly depends on the source mode, including respiration, speech, sneeze, and cough.
This study aims to develop a simplified conical model for each of the identified release modes using a Eulerian-Lagrangian CFD model. The model is first validated with an experimental study, and then a high-fidelity Lagrangian CFD model is employed to monitor various scale particles’ trajectory, evaporation, and lingering persistency. A series of computationally intensive Eulerian-Lagrangian CFD simulations are conducted to generate a database of bioaerosol release spectrum for the release modes. Eventually, artificial intelligence is applied over the data to offer a simplified conical bioaerosol release model.
The simplified model can be applied as a robust source term for design and decision-making about ventilation systems, occupancy thresholds, and disease transmission risks in enclosed spaces.
11:46am - 11:49am
A monitoring system for evaluation of COVID-19 infection risk
Jevgenijs Telicko, Dagis Daniels Vidulejs, Andris Jakovičs
University of Latvia, Latvia
Monitoring systems allow to achieve the greatest comfort inside buildings, however, as a rule, their parameters are not enough to analyse the epidemiological threat in buildings. Due to the pandemic and increasing incidence, there was a need for monitoring systems that could provide the necessary information to analyse the risk of infection . With timely notification of people about the risks such system could not only increase safety in buildings, but also save resources on work of medical personnel. This paper shows an example of implementation in real conditions of a system to indicate risk and inform people inside. One of the goals was to create a cheap, scalable system, for this, an appropriate set of sensors, communication protocols were selected as well as processing of undirect measurements with neural networks was carried out on an embedded computer Jetson Nano. Based on experiments and literature analysis, the necessary parameters for measurements were selected. In addition to the standard monitoring parameters, was used dust sensors as well as sensors that process audio and video information using special data pre-processing and algorithms based on recurrent and convolutional neural networks. System was assembled and tested in University of Latvia. The article shows the potential difficulties in using ready-made solutions for undirect measurements such as people counting and voice recognition and show methods to improve result.
 1.J. Virbulis, M. Sjomkane, M. Surovovs, A. Jakovics. Model for prediction of COVID-19 infection risk in indoor environment based on sensor data. Paper for 8th International Building Physics Conference in Copenhagen IBPC 2021.