Jet engines are comprised of many complex components, and overall engine efficiency and fuel consumption depend on the individual performance of these. This thesis investigates the use of novel analysis/design approaches that can lead to improvements in engine component aerodynamic performance, and therefore have the potential to reduce jet engine fuel consumption. This work focuses on design improvements that can be used to benefit future advanced high bypass ratio engines. Several key engine components are studied: fan blades, compressor blades and engine intakes. The various investigations and outcomes are summarised here. A novel compressor blade shaping approach that uses free-form parameterisation is first investigated. This is shown to offer the potential to increase blade efficiency over alternative parameterisations. Increased design flexibility and the ability to combine 3D blade shaping with aerofoil section modification allows the maximum benefit to be achieved. It is demonstrated that the off-design performance of the optimised blade is satisfactory, meeting the same choke mass flow (once skewed) and achieving reasonable performance at different rotational speeds. The use of shock control bumps is found to be beneficial on transonic fan and compressor blades. It is shown that shock control can delay and reduce separation for a highly-loaded compressor blade, increasing its efficiency without the need for 3D deformations of the blade. The benefit of shock control bumps in reducing shock-induced separation is also demonstrated for a modern, low-speed fan blade, providing an increase in stall margin. Tip clearance effects for modern fan blades are also investigated. The influence of trenches that become worn in the abradable liner is analysed and a model developed that can predict fan blade efficiency for combinations of non-uniform tip clearances and liner trenches. This model allows designers to predict fan efficiency variation over their lifetime and set cold build clearances for optimal lifetime performance. It is also shown how tip leakage behaviour can influence flow behaviour and separation in other regions of the span, and the influence that the shock position has on the leakage aerodynamics. Through the use of the approaches demonstrated and knowledge developed in this thesis, engineers have the potential to improve future engine designs in terms of efficiency, fuel consumption and also engine operability.

No description available.

Pressure drop of single-phase flow across 90 ◦ sharp-angled mitre elbows connecting straight circular pipes is studied in a bespoke experimental facility by using water and air as working fluids flowing in the range of bulk Reynolds number 500 < Re < 60000. To the best of our knowledge, the dependence on the Reynolds number of the pressure drop across the mitre elbow scaled by the dynamic pressure, i.e. the pressure-loss coefficient K, is reported herein for the first time. The coefficient is shown to decrease sharply with the Reynolds number up to about Re=20000 and, at higher Reynolds numbers, to approach mildly a constant K=0.9, which is about 20% lower than the currently reported value in the literature. We quantify this relation and the dependence between K and the straight-pipe friction factor at the same Reynolds number through two new empirical correlations, which will be useful for the design of piping systems fitted with these sharp elbows. The pressure drop is also expressed in terms of the scaled equivalent length, i.e. the length of a straight pipe that would produce the same pressure drop as the elbow at the same Reynolds number. Air-water flow in horizontal and vertical straight pipes and through 90 ◦ sharp-angled mitre elbows, is investigated visually by using high-speed high-resolution camera. The flow is studied in pipes with three diameters for about 600 conditions of air-water flows, characterized by superficial velocities in the ranges of jL =0.297-1.015 m/s for water and jG =0.149-33.99 m/s for air. The portion of the pipe upstream of the elbow is always positioned horizontally, while the portion of the pipe downstream of the elbow is oriented horizontally or vertically with the flow moving upward. Plug, slug, slug-annular and annular flows are observed in horizontal straight pipes, while slug, churn and annular regimes are recorded in vertical straight pipes. These flow patterns are well predicted by the Mandhaneet al. [1] map for horizontally oriented straight pipes and by the Hewitt and Roberts [2] map for vertically oriented straight pipes. The prediction of the flow patterns along the straight portions of the pipe improves by expressing the maps in non-dimensional form. The changes of the flow patterns as the fluids pass through the mitre elbows are thoroughly discussed. A multiple membrane flow structure is observed in the vertical upward flow at much higher Reynolds numbers, based on the water superficial velocity, than in the vertical downward case previously reported in the literature. The flow patterns through the elbows are expressed for the first time in terms of rescaled Mandhane et al. [1] maps, which simultaneously represent the flow patterns both upstream and downstream of the elbows. The dimensional analysis proves that a rigorous way to present the flow regimes of an incompressible isothermal air-water flow for a given geometry is a map in the space of the Reynolds numbers based on the superficial velocities of air and water for fixed Froude number. The pressure drop generated by air-water flows was measured in horizontal and vertical straight pipes and across 90◦ sharp-angled mitre elbows for the same flow conditions of visual investigations. Two new pattern-based values of the Lockhart-Martinelli parameter C are found for the pressure drop in horizontal pipes with the presence of mitre elbows. A dimensional analysis is employed to scale the pressure drop data for straight pipes and across the elbows. New pattern-based empirical correlations are proposed to fit the scaled frictional pressure drops for the flows through the straight portions of the pipe and across the elbows. The flow perturbation length upstream of the elbow is located at less than 32.5D for single-phase and two-phase flows, while the flow recovery length downstream of the lbow was less than 32.5D ∗ and 60D ∗ for single-phase and two-phase flows, respectively. The peripheral pressure upstream and downstream of the elbow is found to be axially symmetric farther than 7D upstream and downstream of the elbow for horizontal orientation in single-phase and two-phase flows.

No description available.

No description available.

Low adhesion between wheel and rail is a recurrent problem for the rail industry. Low adhesion can lead to wheel slides and slips during acceleration and deceleration, which can cause large amounts of damage to the wheel and rail as well as causing safety issues and delays if a train cannot accelerate or decelerate when necessary. Adhesion in the wheel-rail contact is affected by the third body layer which is present in the contact patch between wheel and rail. It is composed naturally from steel wear debris and iron oxides, but often contains other contaminants such as organic matter, ballast dust, soil and grease. Different environmental conditions such as temperature, precipitation and humidity change the properties of this third body layer and therefore change adhesion conditions on the railway. Low adhesion has been well documented throughout the autumn season due to organic contamination, but also takes place throughout the year when no visible contamination is seen on the railhead, known as the “wet-rail” phenomenon. It is thought to occur when there are low levels of water on the railhead, formed by dew, mist or light rain, rather than heavy rain. The conditions and mechanisms that cause the phenomenon are not fully understood. Low adhesion does not occur very often and under what is likely to be a narrow window of conditions, which means that it can be difficult to simulate and study. The aim of this work was to use a combination of tribology and chemistry to better understand the cause of low adhesion throughout the year, known as the wet-rail phenomenon. It investigated low adhesion conditions that occur all year round, initially focusing on the role of iron oxide in low adhesion as it has previously been hypothesised that oxides could play a major role in the wet-rail phenomenon. Testing was carried out over a range of conditions on three different tribological test rigs to attempt to simulate low adhesion due to the wet-rail phenomenon, which produced valuable information about the causes of low adhesion. It was found that, under certain conditions, a combination of iron oxides and water could cause low adhesion in a simulated wheel-rail contact. Test methods were designed to simulate the wet-rail phenomenon, which can be used as a platform to better understand the causes of low adhesion and to test future mitigation methods.

Optical visualisation of flames plays an important role in the in-depth understanding of complex combustion phenomena. In particular, a high-speed camera can provide nonintrusive and continuous monitoring of flames. Through the recorded images, further analysis on colour, temperature, flame dynamics, and a variety of other information can be achieved, which is essential for physical study and numerical modelling. The main objectives of the present work are to apply visualisation monitoring to different combustion conditions, quantitatively analyse the combustion performance, and integrate these analyses with their inherent nature to achieve physical insights into these combustion phenomena. Overall, this work improves the understanding of combustion, and contributes towards the development of tools for flame performance analysis and evaluation. These benefits could be crucial for future fuels and engines.

No description available.

Structural health monitoring (SHM) is emerging as a crucial technology for the assessment and management of important assets in various industries. Thanks to the rapid developments of sensing technology and computing machines, large amounts of sensor data are now becoming much easier and cheaper to obtain from monitored structures, which consequently has enabled data-driven methods to become the main work forces for real world SHM systems. However, SHM practitioners soon discover a major problem for in-service SHM systems; that is the effect of environmental and operational variations (EOVs). Most assets (bridges, aircraft engines, wind turbines) are so important that they are too costly to be isolated for testing and examination purposes. Often, their structural properties are heavily in uenced by ambient environmental and operational conditions, or EOVs. So, the most important question raised for an effective SHM system is, how one could tell whether an alarm signal comes from structural damage or from EOVs? Cointegration, a method originating from econometric time series analysis, has proven to be one of the most promising approaches to address the above question. Cointegration is a property of nonstationary time series, it models the long-run relationship among multiple nonstationary time series. The idea of employing the cointegration method in the SHM context relies on the fact that this long-run relationship is immune to the changes caused by EOVs, but when damage occurs, this relationship no longer stands. The work in this thesis aims to further strengthen and extend conventional linear cointegration methods to a nonlinear context, by hybridising cointegration with machine learning and time series models. There are three contributions presented in this thesis: The first part is about a nonlinear cointegration method based on Gaussian process (GP) regression. Instead of using a linear regression, this part attempts to establish a nonlinear cointegrating regression with a GP. GP regression is a powerful Bayesian machine learning approach that can produce probabilistic predictions and avoid overfitting. The proposed method is tested with one simulated case study and with the Z24 Bridge SHM data. The second part concerns developing a regime-switching cointegration approach. Instead of modelling nonlinear cointegration as a smooth function, this part sees cointegration as a piecewise-linear function, which is triggered by some external variable. The model is trained with the aid of the augmented Dickey-Fuller (ADF) test statistics. Two case studies are presented in this part, one simulated mulitidegree-of-freedom system, and also the Z24 Bridge data. The third part of this work introduces a cointegration method for heteroscedastic data. Heteroscedasticity, or time-dependent noise is often observed in SHM data, normally caused by seasonal variations. In order to address this issue, the TBATS (an acronym for key features of the model: Trigonometric, Box-Cox transformation, ARMA error, Trend, Seasonal components) model is employed to decompose the seasonal-corrupted time series, followed by conventional cointegration analysis. A simulated cantilever beam and real measurement data from the NPL Bridge are used to validate the proposed method.

No description available.