The design of distributed control systems (DCSs) for large, civil gas turbine engines is a complex architectural challenge. To date, the majority of research into DCSs has focused on the contributing technologies and high temperature electronics rather than the architecture of the system itself. This thesis proposes a method for the architectural design of distributed systems using a genetic algorithm to generate, evaluate and refine designs. The proposed designs are analysed for their architectural quality, lifecycle value and commercial benefit. The method is presented along with results proving the concept. Whilst the method described here is applied exclusively to Distributed Control System (DCS) for jet engines, the principles and methods could be adapted for a broad range of complex systems.

No / A new approach in achieving beam steering in array antenna is introduced using the genetic algorithm optimization. The binary switching technique uses simple binary ON/OFF diodes placed in the feeding network of the array element to achieve beam steering. Constantly feeding the driven element and continuous binary variation of the ON/OFF state of each parasitic array elements which determines its conducting ability defines a beam steering angle. Each beam steered angle is distinguished by series of binary combination determined by the genetic algorithm. A uniform circular array antenna consisting of 13 elements is used to implement this technique. The simulation and result analysis of the binary switched array is presented with several beam steering angles scanned.

This study focuses on the development of a multi-objective optimisation methodology for the vacuum assisted resin transfer moulding composite processing route. Simulations of the cure and filling stages of the process have been implemented and the corresponding heat transfer and flow through porous media problems solved by means of finite element analysis. The simulations involved material sub-models to describe thermal properties, cure kinetics and viscosity evolution. A Genetic algorithm which constitutes the foundation for the development of the optimisation has been adapted, implemented and tested in terms of its effectiveness using four benchmark problems. Two methodologies suitable for multi-objective optimisation of the cure and filling stages have been specified and successfully implemented. In the case of the curing stage the optimisation aims at finding a cure profile minimising both process time and temperature overshoot within the part. In the case of the filling stage the thermal profile during filling, gate locations and initial resin temperature are optimised to minimise filling time and final degree of cure at the end of the filling stage. Investigations of the design landscape for both curing and filling stage have indicated the complex nature of the problems under investigation justifying the choice for using a Genetic algorithm. Application of the two methodologies showed that they are highly efficient in identifying appropriate process designs and significant improvements compared to standard conditions are feasible. In the cure process an overshoot temperature reduction up to 75% in the case of thick component can be achieved whilst for a thin part a 60% reduction in process time can be accomplished. In the filling process a 42% filling time reduction and 14% reduction of degree of cure at the end of the filling can be achieved using the optimisation methodology. Stability analysis of the set of solutions for the curing stage has shown that different degrees of robustness are present among the individuals in the Pareto front. The optimisation methodology has also been integrated with an existing cost model that allowed consideration of process cost in the optimisation of the cure stage. The optimisation resulted in process designs that involve 500 € reduction in process cost. An inverse scheme has been developed based on the optimisation methodology aiming at combining simulation and monitoring of the filling stage for the identification of on-line permeability during an infusion. The methodology was tested using artificial data and it was demonstrated that the methodology is able to handle levels of noise from the measurements up to 5 s per sensor without affecting the quality of the outcome.

Yes / Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCGA) based optimisation framework to optimise the design and operational parameters of the process. To provide a deeper insight of the process to the readers, the influences of membrane design parameters on dimethylphenol rejection, water recovery rate and the level of specific energy consumption of the process for two different sets of operating conditions are presented first which were achieved via simulation. The membrane parameters taken into consideration include membrane length, width and feed channel height. Finally, a multi-objective function is presented to optimise the membrane design parameters, dimethylphenol rejection and required energy consumption. Simulation results affirmed insignificant and significant impacts of membrane length and width on dimethylphenol rejection and specific energy consumption, respectively. However, these performance indicators are negatively influenced due to increasing the feed channel height. On the other hand, optimisation results generated an optimum removal of dimethylphenol at reduced specific energy consumption for a wide sets of inlet conditions. More importantly, the dimethylphenol rejection increased by around 2.51% to 98.72% compared to ordinary RO module measurements with a saving of around 20.6% of specific energy consumption.

Yes / Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCGA) based optimisation framework to optimise the design and operational parameters of the process. To provide a deeper insight of the process to the readers, the influences of membrane design parameters on dimethylphenol rejection, water recovery rate and the level of specific energy consumption of the process for two different sets of operating conditions are presented first which were achieved via simulation. The membrane parameters taken into consideration include membrane length, width and feed channel height. Finally, a multi-objective function is presented to optimise the membrane design parameters, dimethylphenol rejection and required energy consumption. Simulation results affirmed insignificant and significant impacts of membrane length and width on dimethylphenol rejection and specific energy consumption, respectively. However, these performance indicators are negatively influenced due to increasing the feed channel height. On the other hand, optimisation results generated an optimum removal of dimethylphenol at reduced specific energy consumption for a wide sets of inlet conditions. More importantly, the dimethylphenol rejection increased by around 2.51% to 98.72% compared to ordinary RO module measurements with a saving of around 20.6% of specific energy consumption.

Yes / The need for desalinated seawater and reclaimed wastewater is increasing rapidly with the rising demands for drinkable water required for the world with continuously growing population. Reverse Osmosis (RO) processes are now among the most promising technologies used to remove chemicals from industrial effluents. N-nitrosamine compounds and especially N-nitrosodimethylamine (NDMA) are human carcinogens and can be found in industrial effluents of many industries. Particularly, NDMA is one of the by-products of disinfection process of secondary-treated wastewater effluent with chloramines, chlorines, and ozone (inhibitors). However, multi-stage RO processes with permeate reprocessing and recycling has not yet been considered for the removal of N-nitrosodimethylamine from wastewater. This research therefore, begins by investigating a number of multi-stage RO processes with permeate-reprocessing to remove N-nitrosodimethylamine (NDMA) from wastewater and finds the best configuration in terms of rejection, recovery and energy consumption via optimisation. For the first time we have applied Species Conserving Genetic Algorithm (SCGA) in optimising RO process conditions for wastewater treatment. Finally, permeate recycling is added to the best configuration and its performance is evaluated as a function of the amount of permeate being recycled via simulation. For this purpose, a mathematical model is developed based on the solution diffusion model, which is used for both optimisation and simulation. A number of model parameters have been estimated using experimental data of Fujioka et al. (Journal of Membrane Science 454 (2014) 212–219), so that the model can be used for simulation and optimisation with high accuracy and confidence.

A water distribution system (WDS) design deals with achieving the desired network performance. WDS design can involve new and / or existing network redesigns in order to keep up with the required service performance. Very often, WDS design is expensive, which encourages cost effectiveness in the required investments. Moreover, WDS design is associated with adverse environmental implications such as greenhouse gas (GHG) emissions due to energy consumption. GHGs are associated with global warming and climate change. Climate change is generally understood to cause reduction in water available at the sources and increase water demand. Urbanization that takes into account factors such as demographics (population ageing, household occupancy rates, etc.) and other activities are associated with water demand changes. In addition to the aforementioned issues, the challenge of meeting the required hydraulic performance of WDSs is worsened by the uncertainties that are associated with WDS parameters (e.g., future water demand). With all the factors mentioned here, mitigation and adaptive measures are considered essential to improve WDS performance in the long-term planning horizon. In this thesis, different formulations of a WDS design methodologies aimed at mitigating or adapting the systems to the effects of future changes such as those of climate change and urbanization are explored. Cost effective WDS designs that mitigate climate change by reducing GHG emissions have been investigated. Also, water demand management (DM) intervention measures, i.e., domestic rainwater harvesting (RWH) systems and water saving appliance schemes (WSASs) have been incorporated in the design of WDSs in an attempt to mitigate, adapt to or counteract the likely effects of future climate change and urbanization. Furthermore, flexibility has been introduced in the long-term WDS design under future uncertainty. The flexible methodology is adaptable to uncertain WDS parameters (i.e., future water demand in this thesis) thereby improving the WDS economic cost and hydraulic performance (resilience). The methodology is also complimented by strategically incorporating DM measures to further enhance the WDS performance under water demand uncertainty. The new methodologies presented in this thesis were successfully tested on case studies. Finally, conclusions and recommendations for possible further research work are made. There are potential benefits (e.g., cost savings, additional resilience, and lower GHG emissions) of incorporating an environmental objective and DM interventions in WDS design. Flexibility and DM interventions add value in the design of WDSs under uncertainty.

This thesis examines the design of supervisory controllers for windfarms of pitch-controlled wind turbines. The control objectives are the maximisation of the financial income from the generated electricity and the minimisation of the turbines' fatigue damage. The design exploits the wide variations in the ratio of financial income to fatigue damage which are found both spatially across windfarms and as a function of time. The supervisory control strategy makes use of the ability of pitch-controlled turbines to operate with variable power set points; a capability which is rarely exploited in practice. A windfarm simulation which has been developed for the purposes of testing supervisory controllers is described. It is shown that the simulation is a suitable test-bed for this application. Results are presented which demonstrate how the fatigue damage of a turbine's gearbox and structural components vary as functions of the mean wind-speed, turbulence intensity and power set point, both for isolated turbines and for turbines experiencing wake effects. A lifetime performance function is proposed and 'ideal' power set point curves are evaluated using a genetic search algorithm. It is shown that significant improvements in performance can be achieved if the operation of the turbines is altered to take account of variable electricity tariffs. A windfarm control strategy that splits the turbines into interacting and non-interacting categories is found to give good results. Using data generated by the simulation, it is shown that simple cost functions can be developed for non-interacting turbines which, when used in a controller, give performance that is close to the 'ideal'. A similar cost function is applied to a group of three interacting turbines, and it is found that substantial reductions in all measures of total annual fatigue damage are achieved for a small reduction in total annual financial income. The on-line implementation of windfarm supervisory controllers is discussed and the behaviour of a simple hill-climbing algorithm is examined using a simulated group of three interacting turbines.

This thesis deals with design of flush–mounted planar disc antenna suitable for use in vehicles. For each of the bands GSM900 and GSM1800, which are used in Czech republic, an independent antenna has been created. As a simulator of the electromagnetic field the program IE3D has been used. For the improvement of theoretical results an optimisation in the program MATLAB has been developed. As the optimisation method the genetic algorithms have been selected.

Decentralised power systems with a high portion of power generated from renewable energy sources and cogeneration units (CHP) are emerging worldwide. Optimising the energy usage of such systems is a difficult task as the stochastic fluctuations of generation from renewable sources, the coupling of electrical and thermal power generation by CHP and the time dependence of necessary storage devices require new approaches. Evolutionary algorithms are able to solve the optimisation task of the energy management. They use the principles of erroneous replication and cumulative selection that can be observed in biological processes, too. Very often recombination is included in the optimisation process. Using these quite simple principles the algorithm is able to explore difficult, large and high dimensional solution spaces. It will converge to the optimal solution in most of the cases quite fast, compared to other types of optimisation algorithms. At the example of an one dimensional replicator it is derived that the convergence speed in optimising convex functions increases by several orders of magnitude even after a few cycles compared to Monte-Carlo-simulation. For several types of equipment models are developed in this work. The cost to operate a given power system for a given time span is chosen as objective function. There is a variety of parameters (more than 15) that can be set in the algorithm. With quite extensive investigations it could be shown that the product of number of replicators and the number of calculated cycles has the most important influence on the quality of the solution but the calculation time is also proportional to this number. If there are reasonable values chosen for the remaining parameters the algorithm will find appropriate solutions in adequate time in most of the cases. Although a pure evolutionary algorithm will converge to a solution the convergence speed can be greatly enhanced by extending it to a hybrid algorithm. Grouping the replicators of the first cycle in suggestive regions of the solution space by an intelligent initialisation algorithm and repairing bad solutions by introducing a Lamarckian repair algorithm makes the optimisation converge fast to good optima. The algorithm was tested using data of several existing energy systems of different structure. To optimise the energy usage in a power system with 15 different types of units the required computation time is in the range of 15 minutes. The results of this work show that extended hybrid evolutionary algorithms are suitable for integrated optimisation of energy usage in combined local energy systems. They reach better results with the same or less effort than many other optimisation methods. The developed method of optimisation of energy usage can be applied in energy systems of small and large size and complexity as optimisation computations of energy systems on the island of Cape Clear, at FH Offenburg and in the Allgäu demonstrate.