Research
The CFD & HSE Simulation Research Group utilizes advanced numerical techniques to address critical challenges in industrial process safety, occupational health and safety, indoor air quality, air pollution, multiphase flows, chemical reaction engineering, porous scale modelling, and public health. Through rigorous numerical simulations, we provide in-depth understanding and predictive capabilities for complex phenomena across these interconnected domains. Furthermore, we are increasingly incorporating AI/ML algorithms with our numerical data obtained to provide enhanced critical insights, achieve more accurate predictions, faster analysis, and optimized solutions for a wide spectrum of vital industrial and public health issues. This interdisciplinary approach fosters a deeper understanding of complex phenomena and facilitates the development of more effective strategies for enhancing safety, health, and environmental protection.
Indoor Air Quality
Our research on Indoor Air Quality (IAQ) focuses on understanding and predicting the transport and distribution of pollutants within enclosed spaces, such as offices, hospitals, and residential buildings. We investigate the impact of ventilation systems, meteorological conditions, filtration technologies, and indoor sources of contamination on air quality, aiming to develop strategies for creating healthier and more comfortable indoor environments
![Comparison of indoor measured and predicted PM2.5 levels for a) all the collected data and b) the dust storm period. Also, the scatter plots of measured versus predicted mass concentration c) in full graph scale and d) zoom graph scale including the additional linear fitting for FIT (red solid line) and MERV14 (blue solid line) scenarios. The black solid line indicates perfect agreement, while the black dashed lines represent the fraction of predictions within a factor of two of measurements (FAC2). The light grey shaded areas represent in (a) the period of the selected dust event and in (c) the zoom area [6].](/_next/image?url=%2Fresearch%2FFigure_4.webp&w=3840&q=75)
Figure4:Comparison of indoor measured and predicted PM2.5 levels for a) all the collected data and b) the dust storm period. Also, the scatter plots of measured versus predicted mass concentration c) in full graph scale and d) zoom graph scale including the additional linear fitting for FIT (red solid line) and MERV14 (blue solid line) scenarios. The black solid line indicates perfect agreement, while the black dashed lines represent the fraction of predictions within a factor of two of measurements (FAC2). The light grey shaded areas represent in (a) the period of the selected dust event and in (c) the zoom area [6].
Dust storms not only affect the health of those outdoors, but also the people living indoor as the PM infiltrates through the buildings. Despite the fact that Middle East Area (MEA) countries are suffering from frequent dust storm events, there is still a lack of knowledge regarding the health assessment in indoor environments (e.g. houses and commercial buildings) and the design of appropriate actions and mitigation measures in order to minimize the potential health effects. We are using advanced CFD techiques to predict the Particulate Matter (PM) building infiltration (Fig. 4). More specifically, a combination of CFD and multi-zone modes could be used along with available measurements from Low Cost Sensors (LCS). This combination is superior for more realistic predictions of airflow and pollutant transport in large buildings. Some initial work has already be done [6] and extension of the work for buildings in an University Campus will be arranged to improve the capabilities of the above mentioned models by providing better correlations for the particle filter efficiency and distribution size. These correlations will be produced by combining the obtained prediction with measurement PM data in buildings at MEA.
References
[6]C.D. Argyropoulos, H. Hassan, P. Kumar, K.E. Kakosimos, Measurements and modelling of particulate matter building ingress during a severe dust storm event, Build Environ., 167 (2020) 106441.