Research

To ensure process safety in the Oil & Gas and Petrochemical industries, we employ advanced CFD tools in conjunction with comprehensive Process Hazard Analysis (PHA) methods, including checklists, QRA, HAZOP, STAMP, and STPA, among others. This powerful combination enables us to simulate and thoroughly analyze potential hazards, such as the dispersion of flammable or toxic materials, the progression of explosions, and the performance of safety barriers and ventilation systems. Moreover, we harness the power of AI and ML algorithms to extract deeper insights from our numerical data, enabling advanced hazard identification, predictive risk modeling, and sophisticated scenario analysis. By meticulously modeling the fluid dynamics of these critical events, we contribute directly to the design of inherently safer processes and the development of effective risk mitigation strategies. The strategic integration of AI and ML significantly elevates our process safety capabilities, resulting in demonstrably safer operations, substantial risk reduction, and enhanced environmental stewardship. This data-driven approach paves the way for a more proactive and effective safety management system.

A) Potential tank fire scenarios (Argyropoulos et al. 2012) and B) Enthalpy contours around the tank on fire obtained using the commercial CFD software PHOENICS (Argyropoulos et al. 2013).

A critical consideration, often posing a higher risk, is the potential for toxi releases occuring outdoors to impact occupants withing nearby indoor environments. The ingress and subsequent entrapment of hazardous gases indoors can lead to significantly accelerated accumulation, potentially reaching lethal dosages more readily than in open-air conditions. Despite this pronounced risk, there remains a notable lack of data in empirical data and comparative studies regarding appropriate modelling techniques and effective mitigation strategies for indoor exposure. Consequently, we utilize advanced CFD techniqeus to specifically investigate phenomena such as toxic building ingress and to perform rigorous risk assessments based on dose-response methodologies. The proposed methodology will serve as a guide for the improvement of relevant risk assessment tools and future studies.

A) Distribution of the AEGL-3 values, for all building rooms, at different wind directions and for the CD-HVAC off-scenario, Radius corresponds to the AEGL value and the colour represents the number of rooms with this value for: (a)ASHRAE model, (b) QUIC-CFD model and (c) QUIC-URB model and B) Surface streamlines and Cp for “D5”and 8 different wind directions calculated by QUIC-CFD (Argyropoulos et al. 2017).

Furthermore, we harness the power of AI and ML algorithms to extract deeper insights from our numerical data, enabling advanced hazard identification, predictive risk modeling, and sophisticated scenario analysis. By modelling the fluid dynamics of critical events, we contribute directly to the design of inherently safer processes and developing effective risk mitigation strategies. The strategic integration of AI and ML significantly elevates our process safety capabilities, resulting in safer operations, substantial risk reduction, and enhanced environmental stewardship. This data-driven approach paves the way for a more proactive and effective safety management system.

In the realm of occupational health and safety, we utilize CFD to assess worker exposure to airborne contaminants, heat stress, and other physical hazards within workplaces. Our simulations can optimize ventilation designs, evaluate the effectiveness of personal protective equipment, and inform strategies to minimize occupational risks, ultimately fostering healthier and safer working conditions. In addition, via online surveys and questionnaires, we are trying to evaluate the common occupational health hazards among workers for different industrial sectors (e.g. oil & gas, drilling operations and exploration, petrochemicals, manufacturing, etc.) and study their health effects and risks. Moreover, we study the prediction of safety factors on safety compliance to substantial accident risks and provide meaningful and practical recommendations. This will allow us to enhance the safety compliance level and identify essential risks, improve safety culture, comply with safety rules and procedures.

Pulling out of drill hole, Turner Valley oil field, Alberta Provincial Archives of Alberta, P1983.

Our research on indoor air quality 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, filtration technologies, and indoor sources of contamination on air quality, aiming to develop strategies for creating healthier and more comfortable indoor environments. 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. More specifically, a combination of CFD and multi-zone modes will 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 () and extension of the work for buildings in an University Campus will be arranged in order to improve the 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.

Addressing air pollution, we employ CFD to model the dispersion of pollutants from various sources, including industrial emissions and vehicular traffic, in the atmospheric environment. Our simulations help to understand the impact of these emissions on air quality at different scales, inform the development of effective pollution control measures, and contribute to improved urban planning and environmental management.

Our investigations into multiphase flows involve the numerical simulation of systems where multiple phases (e.g., gas-liquid, liquid-solid) interact. This is crucial for understanding and optimizing a wide range of industrial processes, including chemical reactions, separation processes, and energy systems. Our research in this area contributes to more efficient and sustainable technologies.

In the field of chemical reactor engineering, we apply CFD to gain detailed insights into the flow patterns, mixing characteristics, heat transfer, and reaction kinetics within chemical reactors. This allows for the optimization of reactor design and operating conditions, leading to enhanced efficiency, selectivity, and safety in chemical production.

Our work on porous scale modelling focuses on understanding transport phenomena within porous media, which is relevant to applications such as filtration, catalysis, and groundwater flow. We develop and apply numerical models to characterize flow and transport at the pore scale, providing fundamental insights that can inform the design and optimization of macroscopic processes.

Finally, our research in public health utilizes CFD to model the spread of airborne diseases, assess the effectiveness of infection control measures in healthcare settings, and evaluate the impact of environmental factors on public health outcomes. More specifically, we also initiated research activities in the field of environmental health and exposure and investigation of biopathogen-fluid interactions in droplets combined with different modes of transmission and contamination using CFD, as well as the location distance between patient and health staff in hospitals and long-term care facilities (LTCF). This interdisciplinary approach contributes to evidence-based strategies for disease prevention and public health protection. In addition, through these interconnected research area, our group strives to also advance.

Through these interconnected research areas, our group strives to advance the understanding of complex fluid flow and transport phenomena and to translate this knowledge into practical solutions that enhance safety, health, and environmental sustainability.