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In this research, a multi-agent approach was applied to the design of a large axial flow compressor in order to optimise performance and to greatly enlarge the useful operating range of the machine. In this design a number of distributed software/hardware agents co-operate to control the internal geometry of the machine and thereby optimise the compressor characteristics in response to changes in flow conditions. The resulting machine is termed an ‘Intelligent Geometry Compressor’ (IGC). The design of a multi-agent system for the IGC was carried out in three main phases, each supported by computer simulation. In the first phase a steady-state model of the IGC was developed in which global control of the variable geometry is achieved by a single agent. This was used to help identify specific requirements for performance and the underlying parametric relationships. The subsequent phases incorporated additional agents into the machine design to meet these requirements. Initially, agents were deployed to optimise the settings of individual rows of stator vanes. In the final phase, the MAS was extended to incorporate agents into the machine design for the control of individual stator vanes. Simulation results were obtained which demonstrate the effectiveness of the intelligent geometry compressor in achieving delivery pressure regulation over a wide range of steady-state operating conditions whilst optimising overall machine efficiency and avoiding the occurrence of stall. Some of the implications for the physical design of an IGC arising from the MAS concept were briefly considered. The experience of the research supported by the specific results and observations from many simulation trials, led to the conclusion that multi-agent systems can provide an effective and novel alternative approach to the design of an intelligent geometry compressor. By implication, this conclusion may be extended to other intelligent machine applications where similar opportunity to apply a distributed control solution exists.

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Many areas in which computational optimisation may be applied are multi-objective optimisation problems; those where multiple objectives must be minimised (for minimisation problems) or maximised (for maximisation problems). Where (as is usually the case) these are competing objectives, the optimisation involves the discovery of a set of solutions the quality of which cannot be distinguished without further preference information regarding the objectives. A large body of literature exists documenting the study and application of evolutionary algorithms to multi-objective optimisation, with particular focus being given to evolutionary strategy techniques which demonstrate the ability to converge to desired solutions rapidly on many problems. Simulated annealing is a single-objective optimisation technique which is provably convergent, making it a tempting technique for extension to multi-objective optimisation. Previous proposals for extending simulated annealing to the multi-objective case have mostly taken the form of a traditional single-objective simulated annealer optimising a composite (often summed) function of the objectives. The first part of this thesis deals with introducing an alternate method for multiobjective simulated annealing, dealing with the dominance relation which operates without assigning preference information to the objectives. Non-generic improvements to this algorithm are presented, providing methods for generating more desirable suggestions for new solutions. This new method is shown to exhibit rapid convergence to the desired set, dependent upon the properties of the problem, with empirical results on a range of popular test problems with comparison to the popular NSGA-II genetic algorithm and a leading multi-objective simulated annealer from the literature. The new algorithm is applied to the commercial optimisation of CDMA mobile telecommunication networks and is shown to perform well upon this problem. The second section of this thesis contains an investigation into the effects upon convergence of a range of optimiser properties. New algorithms are proposed with the properties desired to investigate. The relationship between evolutionary strategies and the simulated annealing techniques is illustrated, and explanation of the differing performance of the previously proposed algorithms across a standard test suite is given. The properties of problems on which simulated annealer approaches are desirable are investigated and new problems proposed to best provide comparisons between different simulated annealing techniques.

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A methodology is proposed for evaluating the level of service within an airport landside system from the passenger's point of view using linguistic service criteria. The new concept of level of service for a transport system, particularly within the airports indicates that there must be strong stimulation in order to proceed with the current stereotyped service standards which are being criticised due to their being based on, either physical capacity/volume or temporal/spatial standards that directly incorporates the perception of passengers, the dominant users. Most service evaluation methodologies have been concentrated on the factors of the time spent and the space provided. These quantitative factors are reasonably simple to measure but represent a narrow approach. Qualitative service level attributes are definitely important factors when evaluating the level of service from a user's point of view. This study has adopted three main evaluation factors: temporal or spatial factors as quantitative measurements and comfort factors and reasonable service factors as qualitative measurements. The service level evaluation involves the passenger's subjective judgement as a perception for service provision. To evaluate the level of service in the airport landside system from the user's perception, this research proposes to apply a multi-decision model using fuzzy set theory, in particular fuzzy approximate reasoning. Fuzzy set theory provides a strict mathematical framework for vague conceptual phenomena and a modelling language for real situations. The multi-decision model was applied to a case study at Kimpo International Airport in Seoul, Korea. Results are presented in terms of passenger satisfaction and dissatisfaction with a variety of different values.

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Remote sensing provides methods to infer vegetation information over large areas at a variety of spatial and temporal resolutions that is of great use for terrestrial carbon cycle modeling. Understory vegetation and foliage clumping in forests present a challenge for accurate estimates of vegetation structural information. Multi-angle remote sensing was used to derive and refine new information about the vegetation structure for the purpose of improving global leaf area index mapping. A field experiment with multi-angle, high resolution airborne observations over modified and natural backgrounds (understory, moss, litter, soil) was conducted in 2007 near Sudbury, Ontario to test a methodology for the background reflectivity retrieval. The experiment showed that it is feasible to retrieve the background information, especially over the crucial low to intermediate canopy density range where the effect of the understory vegetation is the largest. The tested methodology was then applied to background reflectivity mapping over conterminous United States, Canada, Mexico, and Caribbean land mass using space-borne Multi-angle Imaging SpectroRadiometer (MISR) data. Important seasonal development of the forest background vegetation was observed across a wide longitudinal and latitudinal span of the study area. The previous first ever global mapping of the vegetation clumping index with a limited eight-month multi-angular POLDER 1 dataset was expanded by integrating new, complete year-round observations from POLDER 3. A simple topographic compensation function was devised to correct negative bias in the data set cause by topographic effects. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements, covering four continents and diverse biomes. Finally, inclusion of the new vegetation structural information, including background reflectivity and clumping index, gained from the multi-angle remote sensing was then shown to improve the performance of LAI retrieval algorithms over forests.

In this work, I present the development of analytical solutions in the Laplace domain for a fully-penetrating, horizontal well producing at a constant flow rate or constant wellbore pressure in the center of a composite, cylindrical reservoir system with an impermeable outer boundary. The composite reservoir consists of two regions. The cylindrical region closest to the wellbore is stimulated, and the permeability within this region follows a power-law function of the radial distance from the wellbore. The unstimulated outer region has homogeneous reservoir properties. The current norm for successful stimulation of low permeability reservoir rocks is multi-stage hydraulic fracturing. The process of hydraulic fracturing creates thin, high permeability fractures that propagate deep into the reservoir, increasing the area of the rock matrix that is exposed to this low-resistance flow pathway. The large surface area of the high conductivity fracture is what makes hydraulic fracturing so successful. Unfortunately, hydraulic fracturing is often encumbered by problems such as high capital costs and a need for large volumes of water. Therefore, I investigate a new stimulation concept based upon the alteration of the permeability of a large volume around the producing well assembly from its original regime to that following a power-law function. I evaluate the effectiveness of the new concept by comparing it to conventional multi-stage hydraulic fracturing. The results of this investigation show that the power-law permeability reservoir (PPR) has a performance advantage over the multi-fractured horizontal treatment (MFH) only when the fracture conductivity and fracture half-length are small. Most importantly, the results demonstrate that the PPR can provide respectable flow rates and recovery factors, thus making it a viable stimulation concept for ultra-low permeability reservoirs, especially under conditions that may not be conducive to a conventional MHF treatment.