Intelligent optimisation system for airport operation: Hajj Terminal in Saudi Arabia

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Airport operation level of service (LOS) and performance management are among the major concerns by any airport authority. Two aspects considered in that kind of measurement: passengers prospective and operators prospective. This thesis tries to combine both in its produced optimisation system. This study was carried out in the Hajj terminal of the King Abdul-Aziz international airport and classified the processing time among the most important measures affecting the users’ observation of the level of service. Produced survey has helped to generate performance measure upon passengers prospective. On the other hand a simulation model of the process flow is utilised to formulate driven data model of the terminal process flow operations. The model built on Arena software and correlation study is made from the multiple “what if” scenarios of the model. Then a linear regression is used to generate a model for each variable. Levenberg–Marquardt (LM) algorithm is used after to carry out better regression model then Neuro-Fuzzy (NF) model found to be more efficient as it is picked and used to generate a best observed prediction. The system is optimised through the generated Neuro-Fuzzy (NF) logic model using both Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A validation in addition to the testing made in the optimisation system. Analysis shows a great deal of improvement in predictions using fuzzy logic instead of linear regression for all dependent variables. PSO and GA optimisations are carried out and compared to the actual results gathered from the Arena simulation report

Wind energy in the built environment: a design analysis using CFD and wind tunnel modelling approach

Renewable energies are a critical element for reducing greenhouse gases emissions and achieving a sustainable development. Until recently, building integration of renewable sources was focused on solar technologies. Nevertheless,building integrated wind turbines can and must be part of the solution to the global energy challenge. This research investigated the potential of integrating small vertical wind turbines between medium-rise buildings. Wind velocities were measured around 7 fifteenstorey towers. The measurements were carried out for nine different configurations,using a boundary layer wind tunnel and computational fluid dynamics (CFD) simulations. Computed and measured results showed reasonable agreement. The differences were more apparent at ground level. It was established that building orientation and the separation between buildings defines to a great extent the wind environment around buildings. It was found that a distance between buildings of 15 metres and an orientation of θ=260˚ produced the higher augmentation factors. This configuration produced up to 17,812kWh in a typical Nottingham UK year, using six vertical wind turbines of 2.5kW each. Results suggested that the use of CFD as a visualisation tool is extremely useful at design stages in projects involving the integration of wind turbines. Nevertheless, the results of CFD simulations are highly dependent on the type of roughness modification applied to the wall functions, the choice of the turbulence model and the modelling of the inlet wind velocity profile. Because servicing buildings accounts for around half of the UK’s total energy consumption, the need to reduce the consumption of fossil fuels is central to good building design. That is why the architectural practice must respond professionally by delivering buildings that successfully integrate wind energy technologies, which can only be achieved if the designer actively engages with the environmental design principles and improves his understanding of building physics

Wind energy in the built environment: a design analysis using CFD and wind tunnel modelling approach

Renewable energies are a critical element for reducing greenhouse gases emissions and achieving a sustainable development. Until recently, building integration of renewable sources was focused on solar technologies. Nevertheless,building integrated wind turbines can and must be part of the solution to the global energy challenge. This research investigated the potential of integrating small vertical wind turbines between medium-rise buildings. Wind velocities were measured around 7 fifteenstorey towers. The measurements were carried out for nine different configurations,using a boundary layer wind tunnel and computational fluid dynamics (CFD) simulations. Computed and measured results showed reasonable agreement. The differences were more apparent at ground level. It was established that building orientation and the separation between buildings defines to a great extent the wind environment around buildings. It was found that a distance between buildings of 15 metres and an orientation of θ=260˚ produced the higher augmentation factors. This configuration produced up to 17,812kWh in a typical Nottingham UK year, using six vertical wind turbines of 2.5kW each. Results suggested that the use of CFD as a visualisation tool is extremely useful at design stages in projects involving the integration of wind turbines. Nevertheless, the results of CFD simulations are highly dependent on the type of roughness modification applied to the wall functions, the choice of the turbulence model and the modelling of the inlet wind velocity profile. Because servicing buildings accounts for around half of the UK’s total energy consumption, the need to reduce the consumption of fossil fuels is central to good building design. That is why the architectural practice must respond professionally by delivering buildings that successfully integrate wind energy technologies, which can only be achieved if the designer actively engages with the environmental design principles and improves his understanding of building physics

Modelling of neighbourhood effect in cities by coupling computational fluid dynamics and building energy simulation techniques

Building energy simulation (BES) is widely applied to assess indoor comfort and building energy demand, which is reported to encompass a one-third share of the world’s energy demand in 2021. There is a range of worldwide accepted building energy simulation packages in hand, such as EnergyPlus ©, Revit©, DOE-2©. These tools comprise series of subroutines to predict the behaviour of systems within the buildings. The calculations in these programs are based on the combination of well-defined laws (i.e., energy and mass balance) and empirical algorithms (e.g., convective heat transfer coefficient). In particular, despite numerous updates over the empirical algorithms of convective heat transfer coefficient (CHTC), the current packages are still considered not proper in representing the local CHTC values in many urban occasions as they simplify the surrounding airflows with homogeneous patterns. Furthermore, it has been reported that the inadequate understanding of outdoor airflow can lead to up to 20 – 40 % error in building energy predictions. This weakness, thus, initiated a subject of the research in the past decades to couple BES with computational fluid dynamics (CFD) tools, which are known for their strengths in airflow modelling, especially in representing the neighbourhood effects in an urban area. Dynamic coupling of BES to computational fluid dynamics (CFD) techniques are a common strategy to improve the simulation performance. The precedent research of CFD-BES coupling mainly focuses on the indoor environment, but only a few consider the outdoor microclimate conditions. This research aims to investigate the neighbourhood effect on the convection of buildings' exterior surfaces and enhance their presence in the local convective heat transfer coefficient format in building energy modelling. Among the dynamic coupling strategies, the fully dynamic coupling is understood as computationally intensive and impractical in medium-to-long-term modelling or even short-term (hourly, daily or weekly) modelling of naturally ventilated scenarios on a neighbourhood scale. Therefore, though it provides a more accurate assessment than the quasi-dynamic approach, it is less popular than the latter one. In this study, frameworks of fully dynamic coupling and virtual dynamic coupling are proposed for short-term and medium-to-long-term (monthly, seasonally or annually) modelling, respectively. Three case studies are performed for 1) short-term modelling of scenarios with all buildings sealed from outdoors without natural ventilation (sealed scenarios); 2) short-term modelling of scenarios with all buildings under the natural ventilation during the night-time (ventilated scenarios); 3) medium-to-long-term modelling of sealed scenarios. where ‘sealed’ here means the rooms are sealed from outdoors with the windows closed all the time. The first case study proves the feasibility of the developed benchmark coupling framework. After that, the second case study expands the framework for application in scenarios of natural ventilation with fast calculations. The last case study provides the virtual dynamic coupling of the CFD and BES with artificial neural network for medium-to-long-term prediction of CHTCs. All case studies are performed in typical hot weather for a simple city community in Los Angeles, U.S. The results highlight the importance of the neighbourhood effect. For short-term modelling of sealed scenarios, the difference between the prediction of the hourly averaged external convection using the coupling method and that of the standalone conventional BES models is up to 64 %. Furthermore, for short-term modelling of ventilated scenarios, the proposed model substantially reduces the computational cost of the dynamic coupling procedure, taking almost 1/200 of time as the conventional method. Concerning the medium-to-long-term modelling using a virtual dynamic coupling, the predictions of the local CHTCs on the external surfaces are found satisfactory with an accuracy of 0.88. Moreover, ten is the effective number of days to train the neural network tools for a one-month simulation—the proposed approach saves approximately 2/3 of the required computational time using an ordinary approach