|Satheesan, Manoj Kumar
|Numerical simulation methods coupled with genetic algorithm to predict cooling energy consumption and infection transmission in buildings
|Mui, Kwok-wai (BEEE)
Wong, Ling-tim (BEEE)
|Air conditioning -- Mathematics
Buildings -- Energy consumption -- Mathematical models
Buildings -- Energy conservation
Airborne infection -- Prevention
Health facilities -- Heating and ventilation
Hong Kong Polytechnic University -- Dissertations
|Department of Building Environment and Energy Engineering
|xxviii, 211 pages : color illustrations
|The human-induced climate change is the primary cause of a rise in heat waves and other extreme weather events experienced across the globe. Buildings are one of the major contributors to greenhouse gas emissions, the primary cause of climate change. It is anticipated that climate change will reduce the global demand for heating and increase the global need for cooling. Already, the demand for space cooling has tripled since 1990. Hence, designing and constructing sustainable buildings using less energy to maintain a suitable indoor temperature is an essential strategy for reducing carbon emissions from the building sector.
Predicting the cooling energy required for a building is a complex yet vital method for creating low-energy-demand structures. This study develops a hybrid simulation methodology that combines the strengths of physical simulation (EnergyPlus™) and data-driven methodologies (artificial neural network) to estimate the cooling energy consumption of buildings. This simulation strategy is superior to its counterparts in terms of simulation time, accuracy, robustness, and flexibility to forecast the cooling energy demand. The proposed model’s goodness of fit with energy plus simulations and peer literature data was assessed to ascertain its validity, and a high degree of concordance between the results verified its capacity to be an alternative to conventional energy estimation techniques. The impact of wall and window material selection, window-to-wall ratio, shading coefficient, and indoor set-point temperature on building cooling energy consumption is evaluated. Apartments' energy consumption could be reduced by increasing thermal insulation, decreasing the window-to-wall ratio, and raising the indoor set-point temperature relative to current standards.
Despite the great benefits of the hybrid simulation approach, its development time is significant. Therefore, for its benefits to be realised, it should be applicable to a variety of structures and not just one. The generalisation potential of the model was evaluated in two distinct settings: a subdivided unit (SDU) and a general inpatient ward. Both have building parameters that exceed the simulated training range of the hybrid model. The model's goodness-of-fit test with energy plus simulation results indicated a good generalisation capability. Using the generalised hybrid simulation model, the energy-saving measures in an SDU were analysed, and it was determined that apartment flow area, occupant per floor area, and indoor set-point temperature are crucial for energy savings.
Design exploration using standard methods is a laborious endeavour. In addition, the outcome cannot be attributed to an optimal design. Consequently, a genetic algorithm (GA) is combined with the generalised hybrid simulation model to assist the user in iteratively analysing the various design parameters and their impact on cooling energy consumption. The coupled technique would rapidly identify the optimal or sub-optimal design option from a pool of solutions, resulting in the least or highest building cooling energy consumption, respectively. A typical inpatient ward cubicle was chosen as a case study to highlight the benefits of the optimisation technique. A combination of (i) design parameters resulting in minimum envelope heat gain, (ii) greater recirculation ratio, and (iii) a reduction in lighting power density from 13 W/m2 to 7.3 W/m2, would be an energy-efficient strategy for a general inpatient ward, according to this study. Furthermore, infection control is comparable to or greater than the energy requirement in a general inpatient ward unit. Thus, approaches to prevent the spread of infection within a general inpatient hospital cubicle are further explored.
Infections in healthcare facilities can result in significant public health issues and financial burdens. Therefore, enhancing infection control in healthcare settings is crucial. Ventilation systems are critical in maintaining the air quality inside the building. In particular, healthcare facilities must consider infection control when designing ventilation functions. In hospitals, inpatient wards occupy a substantial amount of floor space. Yet, ventilation design guidelines for patient environments, particularly wards, remain vague. Computational fluid dynamics (CFD) was used to analyze the combined effects of air change rate and exhaust flow rate on airflow and exposure risk distributions due to droplet nuclei of size 0.167 µm (Middle east respiratory syndrome coronavirus) in an air-conditioned ward cubicle. The association between ventilation and the mechanism of infection transmission within the ward cubicle was apparent. In addition to the air change rate, the configuration of a ventilation system is identified to serve as a crucial factor in controlling pathogen exposure. The utilization of CFD yielded significant insights into the distribution of airflow and bioaerosols within an inpatient ward, with a high degree of temporal and spatial precision. However, despite the great precision and details of flow parameters provided by CFD, it is coupled with a lengthy computation time and a high cost.
Multiple factors influence the airflow and dispersion of pathogens in an inpatient unit. Developing effective ventilation strategies encompassing these factors through trial and error would necessitate numerous modifications between the initial and final designs. As a result, determining the best ventilation strategy by relying solely on CFD and the traditional method of optimization is seen as ineffective and time-consuming. Therefore, an evolutionary algorithm (GA) and an assessment mechanism (CFD) are coupled. The aim is to improve patient safety by limiting the spread of infections. The proposed method would execute fewer CFD simulations while assessing more design options iteratively. Based on the design exploration conducted with the GA-CFD approach, the location of an infected patient, the air change rate, the flow rate through a local exhaust grille, as well as the number, location, and size of supply diffusers and local exhaust grilles, that can significantly minimize the likelihood of an infection spreading from one patient to another within a ward is identified. A simple, cost-effective optimal ventilation solution that decreases infection transmission within a ward is proposed. The study also highlights the necessity for healthcare personnel to practise and implement conventional infection control guidelines, such as adequate hand cleanliness, eye protection, and always wearing a high-filtration face mask, regardless of ventilation technique.
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