The flow entering a high-pressure turbine in a gas turbine engine is characterised by a loss of symmetry due to temperature distortions in both radial and circumferential directions, known as hot streaks. In industrial simulations it is common practice to assume uniform inlet temperature conditions to simplify the aerodynamic analysis. However, hot streaks may have significant impact on the turbine aerodynamics with the redistribution of the hot fluid affecting the development of secondary flows with consequent effects on enhanced local heat transfer and aerodynamic losses. The loss of symmetry has also been linked to the excitation of low-order nodal diameter assembly modes of the downstream rotor blades leading to potential blade failure and thus, should be taken into account during the design process. In today’s carbon-constraint environment additional parameters arise as gas turbines are challenged to adapt to variations of the fuel composition driven by the need of efficient and lowCO2 power generation. Introducing syngas, a synthesis gas fuel that is used to power integrated gasification combined cycle (IGCC) power plants, is likely to affect the operating conditions of existing gas turbines leading to the requirement of re-design of components. With particular focus on the turbine hot flow path, the propagation mechanism of hot streaks throughout the turbine will be affected with consequent impact on the turbine aerodynamics and forced response excitation levels originating from the different hot flow patterns. Motivated by the lack of relevant studies, the current work provides a first step towards the evaluation of the effects of syngas on hot streaks aerodynamics and the induced forced response excitation levels. Using full annulus multi-bladerow unsteady 3D CFD simulations and applying combustor representative hot streak profiles in two different gas turbines, a complete analysis of the hot streaks migration is achieved, with respect to a number of geometric parameters such as the hot streaks shape and injection location in both spanwise and circumferential directions, the coolant configurations as well as the combined effects on the secondary flow development. The aerodynamic analysis indicated the propagation of the hot streaks up to the exit of the turbines under investigation with differences in characteristics depending on design parameters. With respect to the effect of fuel composition variations on the blades temperature levels and the flow pattern is observed between the natural gas and syngas turbine with the syngas showing a more concentrated wake shape. In effect of the syngas different flow pattern, differences are observed in the secondary flows with consequent interaction with the hot streaks. Contrast to initial expectations, the forced response analysis iii resulted slightly lower amplitude unsteady force of lower harmonics for syngas compared to natural gas; however, both fuels showed significant levels of the hot streak induced low engine order excitation compared to the burners and stator related blade passing frequency vibration.

Over recent years, with growing concern over climate change, the need for energy which is sustainable, economical and in line with legalisation has led to a substantial surge of interest in organic Rankine cycles (ORC). With the ability to convert low temperature heat sources into power, ORC technology is at the forefront of many sustainable technologies such as biomass, solar, geothermal and waste heat recovery. Despite successful commercialisation for large-scale systems (> 200 kWe), more development is required at the small-scale to realise its potential. For low temperature (< 150 °C), low power applications, volumetric expanders are the preferred choice. However, for a 10 kWe system, a well-designed radial inflow turbine could achieve a higher efficiency, and bridge an observed gap between the output powers of existing volumetric expander systems. This thesis investigates the design and analysis of radial inflow turbines for this application. A thermodynamic ORC model is first developed, which combines cycle analysis with component design. This model is coupled with a multi-objective optimisation, and a novel objective function is developed that considers the trade-off between system performance and system complexity. Following a cycle analysis case study, a radial inflow turbine design method for ORC turbines is developed which extends existing ideal gas design methods to be applicable for real gases. Two candidate turbine designs are developed and are validated using computational fluid dynamics (CFD). For small-scale systems to be economically feasible it is reasonable to assume that the same turbine will be implemented within a number of different systems. This requires off-design models, and the suitability of using non-dimensional performance maps, obtained using similitude theory, has been investigated using further CFD studies. This has led to the development of a modified similitude theory, suitable for subsonic ORC turbines. This modified similitude theory has been implemented within another thermodynamic model, and the results from a case study show how the same turbine can be effectively utilised within a number of different ORC systems. This is done by selecting a working fluid to match the available heat source. Overall, this thesis successfully demonstrates the development of modelling methods for small-scale low temperature ORCs utilising radial inflow turbines. This has considered design and off-design performance models, and ultimately the results demonstrate how the economy of scale of these systems can be improved, aiding in the future commercialisation of the technology.

The construction industry has been challenged by the UK Construction Foresight Panel to apply advanced information and communication technology to improve the performance, in terms of sustainability, of the existing built environment and infrastructure. Traditionally, built-environment maintenance is a capital-cost-driven activity that relies either upon the subjective assessment of a built environment and infrastructure condition (i.e. a stock condition survey) to identify maintenance needs, or upon a reactive response to a component failure. The effectiveness and efficiency of the stock condition survey process to support planned maintenance has previously been questioned and a more sustainable approach, based on an objective assessment of a built environment and infrastructure performance, has been suggested. Previous attempts to develop objective-based (though not performance–based) maintenance models have largely failed, due to the limitations of technology, the daunting task of managing large amounts of data, and the inability of mathematically based models to cope with the complexity of real-life situations. This thesis addresses this challenge by exploring the feasibility of a performance-based assessment methodology to determine the maintenance needs of a buried oil steel-pipeline system and the impact that any changes in condition may have on the performance and integrity of related components in the pipeline system. The thesis also contains an evaluation of the ability and effectiveness of piezoelectric elements in pipeline defect (crack) signature detection to predict changes in component performance with data sets derived experimentally using laboratory bench testing. Vibration sound-emission detection techniques performed on various oil steel-pipeline defects, using non-destructive testing methods, were validated using attenuation and waveform analysis. Defect size and progression (i.e. the pattern characteristics of the defect) were monitored, measured and identified through spectrum analysis of multiple emission signals in combination with a number of frequency bands. Two series of tests were undertaken to evaluate the ability of vibration sound emission characteristics to identify steel pipeline defects, including leakage. Test Series 1 established the frequency (waveforms) of the generation of the acoustic emission signal caused by normal fluid dynamics (water flow) through the experimental steel pipe and the resulting signal propagation characteristics. Test Series 2 detected and monitored changes in the signal characteristics for incipient defects: (a) small-nail damage, (b) medium-sized nail damage, (c) large-nail damage and (d) crack to leakage source [sealed holes as a simulated corrosion to total failure]; oil was the fluid medium. The defect sources and leakage signals were also studied, and compared with theoretical models. The results of the theoretical analysis and the laboratory experiments confirmed the ability of non-destructive testing, based on vibration sound emission techniques, to detect and distinguish between different failure modes. The ability to carry out a basic inspection, analysis and report of a pipeline using an integrated-sensor device offers many potential benefits. The use of an integrated-sensor device is expected to provide valuable pipeline management information. Specifically the ability to detect and locate mechanical damage at the incipient stage and provide an assessment of the overall pipeline operating condition, including changes in performance profile and prediction of an estimated time to failure, has been shown to be feasible as part of a pipeline maintenance and rehabilitation programme.

Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.

The research discussed in this thesis explores a new method for the detection of grinding burn temperature using a laser irradiation acoustic emission (AE) sensing technique. This method is applicable for the grinding process monitoring system, providing an early warning for burn detection on metal alloy based materials (specifically nickel alloy based materials: Inconel718 and MarM002). The novelty in this research is the laser irradiation induced thermal AE signal that represents the grinding thermal behaviour and can be used for grinding burn detection. A set of laser irradiation experiments were conducted to identify key process characteristics. By controlling the laser power, the required grinding temperatures were simulated on alloy test materials. The thermal features of the extracted AE signal were used to identify the high, medium and low temperature signatures in relation to the off-focal laser distances. Grinding experiments were also conducted to investigate burn conditions. The extracted AE data was used to identify grinding burn and no burn signatures in relation to the depth of cuts. A new approach using an artificial neural network (ANN) was chosen as the pattern recognition tool for classifying grinding burn detection and was used to classify grinding temperatures by extracting the mechanical-thermal grinding AE signal. The results demonstrated that the classification accuracy achieved was 66 % for Inconel718 and 63 % for MarM002 materials. The research established that the wheel wear has a large influence on the creation of burn within the workpiece surface. The results demonstrated that the AE signals in each grinding trial presents different levels of high, medium and low temperature scales. This type of information provides a foundation for a new method for monitoring of grinding burn and wheel wear.

Data mining refers to the automation of data analysis to extract patterns from large amounts of data. A major breakthrough in modelling natural patterns is the recognition that nature is fractal, not Euclidean. Fractals are capable of modelling self-similarity, infinite details, infinite length and the absence of smoothness. This research was aimed at simplifying the discovery and detection of groups in data using fractal dimension. These data mining tasks were addressed efficiently. The first task defines groups of instances (clustering), the second selects useful features from non-defined (unsupervised) groups of instances and the third selects useful features from pre-defined (supervised) groups of instances. Improvements are shown on two data mining classification models: hierarchical clustering and Artificial Neural Networks (ANN). For clustering tasks, a new two-phase clustering algorithm based on the Fractal Dimension (FD), compactness and closeness of clusters is presented. The proposed method, uses self-similarity properties of the data, first divides the data into sufficiently large sub-clusters with high compactness. In the second stage, the algorithm merges the sub-clusters that are close to each other and have similar complexity. The final clusters are obtained through a very natural and fully deterministic way. The selection of different feature subspaces leads to different cluster interpretations. An unsupervised embedded feature selection algorithm, able to detect relevant and redundant features, is presented. This algorithm is based on the concept of fractal dimension. The level of relevance in the features is quantified using a new proposed entropy measure, which is less complex than the current state-of-the-art technology. The proposed algorithm is able to maintain and in some cases improve the quality of the clusters in reduced feature spaces. For supervised feature selection, for classification purposes, a new algorithm is proposed that maximises the relevance and minimises the redundancy of the features simultaneously. This algorithm makes use of the FD and the Mutual Information (MI) techniques, and combines them to create a new measure of feature usefulness and to produce a simpler and non-heuristic algorithm. The similar nature of the two techniques, FD and MI, makes the proposed algorithm more suitable for a straightforward global analysis of the data.

Fabrication processes for the high volume production of parts with micro and nano scale features are very important in the global research and industry efforts to meet the increasing needs for device miniaturisation in numerous application areas. Processes for the replication of surface geometries are promising technologies that are capable to meet the demand of manufacturing products at a low cost and in high volume. Among these technologies, hot embossing is a process which relies on raising the temperature of a sheet of polymer up to its melting range and on pressing a heated master plate into the polymer for triggering a local flow of the material to fill the cavities to be replicated. This technique has attracted increased attention in recent years in particular due to the relatively simple set-up and low cost associated with its implementation in comparison to other replication techniques. The present work is concerned with investigating the process factors that influence hot embossing outcomes. In particularly, a detailed study of the process parameters’ effect on the cavity pressure, demoulding force and uniformity of the residual layer for different materials is conducted to analyse the further potential of this process. A review of the current state of the art on these topics reported in Chapter 2, is also used to assess the capability of this replication technology. Chapter 3 presents an experimental study on the effects of process parameters on pressure conditions in cavities when replicating parts in PMMA and ABS. To measure the pressure state of a polymer inside mould cavities, a condition monitoring system was implemented. Then, by employing a design of experiment approach, the iii pressure behaviour was studied as a function of different process conditions. In particular, the effects of three process parameters, embossing temperature and force and holding time, on the mould cavity pressure and the pressure distribution were investigated. In addition, using a simple analytical model, the minimum required embossing force to fill the cavities across the mould surface was calculated. The theoretical value obtained was then used to inform the design of the experiments. It was shown that cavity pressure and pressure distribution were dependent on both materials and processing conditions. The obtained results indicate that an increase in temperature and holding time reduced the pressure in the central and edge cavities of the mould and the pressure distribution while the opposite effect takes place when considering the embossing force. Also, it was observed that an increase of the embossing force has a positive effect on cavity filling but a negative influence for homogenous filling. In Chapter 4, a theoretical model was proposed to predict demoulding forces in hot embossing by providing a unified treatment of adhesion, friction and deformation phenomena that take place during demoulding of polymer microstructures. The close agreement between the predicted results and those measured experimentally suggests that the model successfully captures the relationship between mould design, feature sidewall, applied pressure, material properties, demoulding temperature and the resulting demoulding force. The theoretical results have been confirmed through comparisons with the demoulding experiments. The temperature at which the demoulding force is minimised depends on the geometry of the mould features along with the material properties of the mould and replica. The applied pressure has an important influence on the demoulding force iv as the increase in pressure augments the adhesion force due to changes in material dimensions and reduces the friction force due to resulting decrease in the thermal stress. Furthermore, the relationship between the residual layer uniformity and three process parameters was investigated in Chapter 5, using simulation and experimental studies when processing PMMA sheets. In particular, the characteristics of the residual layer thickness of embossed parts were analysed as a function of the moulding temperature, the embossing force and the holding time. Increasing the moulding temperature resulted in a reduction on the average residual layer thickness and on its non-uniformity. An increase in the embossing force led to a decrease in the homogeneity of the residual layer. Also, an improvement of the residual layer thickness uniformity was also observed when embossing with a longer holding time. The results of the conducted experimental and simulation studies were analysed to identify potential ways for improving the hot embossing process. Finally, in Chapter 6 the results and main findings from each of the investigations are summarised and further research directions are proposed.

This thesis describes the development of grid generation and numerical methods for predicting the flow in variable geometry, positive displacement screw machines. It has been shown, from a review of available literature, that the two main approaches available to generate deforming grids for the CFD analysis of 3D transient flow in screw machines are algebraic and differential. Grids that maintain the cell count and connectivity, during solution, provide the highest accuracy and customised grid generation tools have the capability to accommodate large mesh deformations. For the analysis of screw rotors with a variable lead or varying profile, these techniques are suitable but are required to be developed further with new procedures that can define the three dimensional variation of geometry of the rotors onto the computational grid. An algebraic grid generation method was used for deforming grid generation of variable lead and varying profile rotors. Functions were developed for correlating a specified lead variation along the rotor axis with the grid spacing. These can be used to build a continuously variable lead with linear, quadratic or higher order functions. For variable profile rotors, a novel approach has been developed for three dimensional grid structuring. This can be used to specify a continuously variable rotor profile, a variable lead, and both internal and external rotor engagement, thus making it possible to generate rotor domains with conical and variable lead geometries. New grid distribution techniques were developed to distribute boundary points on the rotors from the fixed points on the rack and the casing. These can refine the grid in the region of interlobe leakage gaps between the rotors, produce a one to one connected interface between them and improve the cell quality. Inflation layers were applied and tested for mesh refinement near the rotor boundaries. Case studies have been presented to validate the proposed grid generation techniques and the results have been compared with experimental data. Simulated results agreed well with measured data and highlighted the conditions where deviations are highest. Results with variable geometry rotors showed that they achieve steeper internal pressure rise and a larger discharge port area could be used. With variable lead rotors the volumetric efficiency could be improved by reducing the sealing line length in the high pressure zone. Calculations with inflation layers showed that local velocities were better predicted but there was no substantial influence on the integral performance parameters.

Metal loss due to corrosion is a serious threat to the integrity of pressurised oil and gas transmission pipes. Pipe metal loss defects are found in either single form or in groups (clusters). One of the critical situations arises when two or more defects are spaced close enough to act as a single lengthier defect, causing major impact on the pressure containing capacity of a pipe and leading to rupture rather than localised leak at the centre of defects. There have been many studies conducted to determine the distance needed for defects to interact leading to a failure pressure lower than that when the defects are treated as single and not interacting. Despite such efforts, there is no universally agreed defect interaction rule and pipe operators around the world have various rules to pick and choose from. In this work, the effects of defect shapes and orientations on closely spaced defects are tested experimentally and further analysed using finite element analysis. Burst pressures of commonly used ductile steel pipes in the oil and gas industries, namely X52 and X60, are measured under internal pressure loading. The pipes were machined with circular and curved boxed defects at different orientations to simulate actual metal loss defects. The burst pressure results were compared with those obtained using existing analytical methods. Comparison of the results showed conservatism in the existing analytical methods which may potentially lead to unnecessary plant shutdowns and pipe repairs. A failure criterion for both single and interacting defects was proposed and validated numerically using the experimental data obtained in this research work. The numerical results when using the proposed failure criterion showed that defect shapes and orientations have a great influence on the failure pressure of pipes containing interacting defects. A simplified mathematical model based on the parametric results and relevant to the cases studied is proposed with the objective of reducing the known conservatism in the existing pipe standards when it comes to the assessment of defect interaction.

The piston-cylinder liner interface comprises more than half of the total engine rubbing friction. With current demand being for internal combustion engines with better fuel economy, lower exhaust emissions and higher performance, reducing this form of friction is the subject of much study. The research reported in this thesis is concerned with the development of an existing floating liner rig to measure the friction in this region. The performance features of the modified setup have also been assessed. Parametric studies have been undertaken with the modified setup to identify the potential means of friction reduction. Modifications undertaken in the sealing method and driveline assembly has facilitated friction measurement at higher gas loading of up to 80 barg. The modified sealing assembly with a sealing ring overcame the problem of arbitrary force interruption through irregular liner and seal contacts. Addition of five times higher inertia flywheel aided the motor to support the rig with adequate torque during high gas loading operations. Calibrations have been performed at each different build of piston-liner combination to reduce build to build variability in measurement. Experimental studies have been undertaken to assess the friction characteristics for different factors such as operating pressure, temperature and speed, lubricant oil formulation, piston-liner clearance, piston material etc. Tests have been undertaken at a range of operating conditions; peak pressure of 0 to 80 barg, speed of 1000 to 2000 rpm and average mid-liner temperature of 40 to 90 ˚C. Peak cylinder pressure has been observed to be dominating the friction followed by temperature and speed. Friction spikes were observed near the top dead centre for pressurised operations; where normal load on the rings are highest in a cycle. Higher speed generally results in a higher total frictional loss. However at higher temperature and peak pressure, contrasting effect of speed on total friction has been reported. The study further identified that piston motion play important roles in determining mixed/boundary friction along with the local gas pressure, velocity and oil film temperature. Friction reductions have been obtained by using a lower viscosity oil and higher piston-liner clearance. Maximum friction reduction of 18% has been reported in this study by using SAE 0W-30 oil in place of SAE 5W-30. Diametric clearance of 80 μm obtained a maximum reduction of 12% compared to a lower clearance of 20 μm. The use of steel piston has shown potential in reducing friction over aluminium piston but the design and weight of piston played a dominant role in the frictional loss.