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No description available.

No description available.

In this work an integrated set of numerical methods is developed for the analysis of gas turbine combustors, which can predict the flow, temperature and stress fields in modern geometrically complex combustor walls. A key problem for accurate flow and temperature field prediction is the wide range of geometric length scales within modern combustor components. These components typically contain multiple small-scale cooling features such as pedestals and effusion cooling holes, which cannot be resolved by a computational mesh without incurring huge penalties in terms of computer processor and memory requirements. In this work a sub-grid-scale model is developed, which accounts for the effects of small-scale features such as pedestals without resolving them in the computational mesh. Validation of this model using experimental results from the literature shows that the pressure drop, turbulence generation and heat transfer effects of pedestal arrays can be successfully modelled using this approach. Another difficulty in the analysis of combustors is coupling the interdependent temperature field predictions in the fluid and solid regions. This has led to a unified approach to conjugate heat transfer prediction being adopted in this work, whereby a structured finite volume solver is used to predict temperature fields throughout fluid and solid domains. A new conjugate heat transfer discretisation scheme is developed, which can cope with the demanding combination of strong temperature gradient discontinuities and highly skewed grids. Several test cases are presented which demonstrate the accuracy of this new scheme, as well as demonstrating the inadequacy of conventional treatment of the diffusive fluxes for the solution of conjugate problems. The assembled numerical methods are used to predict the flow, thermal and stress fields in a geometrically complex combustor heatshield/backplate assembly, typical of that found in modern engines. This calculation shows that a viable route to computational life prediction has been established.

The silencing of the exhaust from industrial gas turbines is an important element of current designs as it can affect efficiency, space, noise and gas emissions. However, exhausts are very costly and the lower the frequency, the higher is the cost involved in trying to attenuate the noise due to the amount of material and space necessary to implement the exhaust system. In this work, the sources of noise from an exhaust system of a particular gas turbine are investigated. Improvements in understanding the unsteady behaviour of the flow in the exhaust system could potentially lead to an increase in efficiency, a reduction in noise emissions, a decrease in the cost of exhaust mufflers and improved location for the plants. This work presents the experimental approach used to identify the major sources of noise and how these results were then used to create a model that could represent the sources identified. As the frequency components generated by the flow are low, this work concentrates on understanding the mechanisms that generate the low frequency noise. Results show that the major source of noise is the jet leaving the engine exhaust and that the main acoustic source is of dipole nature.

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Nickel base superalloys are used for turbine blades in gas turbine engines, and are required to operate at very high temperatures (≈900°C) for long periods of time in an aggressive environment. A particular problem is that although the operating times of such blades are generally well-known, their effective operating temperatures are less well defined due to variations in position and metal-gas temperature. The microstructure of this material is known to evolve as a function of time and temperature. The aim of this research is to develop a model that will enable the microstructure to act as a time temperature recorder for IN738LC industrial gas turbine blades.

Current commercial 'state of the art' engine simulation software is of a low fidelity. Individual component performance characteristics are typically represented via nondimensional maps with empirical adjustments for off-design effects. Component nondimensional characteristics are usually obtained through the averaging of experimental readings from rig test analyses carried out under nominal operating conditions. In those cases where actual component characteristics are not available and default maps are used instead, conventional simulation tools can offer a good prediction of the performance of the whole engine close to design point, but can deviate substantially at of design and transient conditions. On the other hand, even when real component characteristics are available, zero-dimensional engine cycle simulation tools can not predict the performance of the engine at other than nominal conditions satisfactorily. Low-fidelity simulation tools are generally incapable of analyzing the performance of individual engine components in detail, or capturing complex physical phenomena such as inlet flow distortion. Although the available computational power has increased exponentially over the last two decades, a detailed, three-dimensional analysis of an entire propulsion system still seems to be so complex and computationally intensive as to remain cost-prohibitive. For this reason, alternative methods of integrating different types and levels of analysis are necessary. The integration of simulation codes that model at different levels of fidelity into a single simulation provides the opportunity to reduce the overall computing resource needed, while retaining the desired level of analysis in specific engine components. The objective of this work was to investigate different simulation strategies for communicating the performance characteristics of an isolated gas turbine engine component, resolved from a detailed, high-fidelity analysis, to an engine system analysis carried out at a lower level of resolution. This would allow component-level, complex physical processes to be captured and analyzed in the context of the whole engine performance, at an affordable computing resource and time. More specifically, this work identified and thoroughly investigated several advanced simulation strategies in terms of their actual implementation and potential, by looking into relative changes in engine performance after integrating into the basic, nondimensional cycle analysis, the performance characteristics of i) two-dimensional Streamline Curvature (SLC) and ii) three-dimensional Computational Fluid Dynamics (CFD), engine component models. In the context of this work, several case studies were carried out, utilising different two-dimensional and three-dimensional component geometries, under different operating conditions, such as different types and extents of compressor inlet pressure distortion and turbine inlet temperature distortion. More importantly, this research effort established the necessary methodology and technology required for a full, twodimensional engine cycle analysis at an affordable computational resource.

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This thesis deals with the design and application of modern control techniques to a Rolls Royce aircraft gas turbine engine and networked systems. It is motivated by the need to fully exploit recent advances in control engineering and investigate the suitability of various control methods to gas turbine engines and networked systems. The main contributions of the first part of the thesis relate to the gas turbine engine control. Due to the nonlinearities of the gas turbine engines, the rate limiter and saturation constraints on the fuel feed, the aim is to illustrate the potential of a global nonlinear controller to cover the engine operating range. Several nonlinear control methods, gain-scheduling PID controller, approximate model predictive control (AMPC) and nonlinear model predictive control (NMPC), are presented along with the corresponding control algorithms. Since the parameters in a gain-scheduling PID controller change with the operating range, the need is apparent for a global nonlinear controller to cover its operating range. AMPC and NMPC are then demonstrated to be capable of providing a global nonlinear controller for the engine and can be used in the place of the gain scheduling PID controller. It is shown that AMPC is more preferable than NMPC if computational time is at a premium. The main theme of the second part of the thesis is the design and application of the networked predictive control (NPC) to compensate for the network delay and data packet dropout in both forward and backward channels for networked systems. NPC using both modified model predictive control and generic polynomial method is presented along with the corresponding control algorithms. For both approaches, the system stability for a fixed network delay is presented and an analytical stability criterion is obtained. This provides some guidelines on how to choose the NPC parameters in the case of random network delay. The performance of NPC can be further improved by using a robust NPC (RNPC). To validate the performance using the proposed control methods, a servo motor system is then used for both Intranet and Internet based simulations and practical experiments. A networked control test rig along with the network delay measurement method is used for real-time implementation. It is shown that both NPC and RNPC can efficiently compensate for the network delay and data packet dropout in both channels. This thesis provides basis for the real-time implementation of advanced control methods in gas turbine engines. While this work was applied to a gas turbine engine, these techniques can be applied to a range of nonlinear control systems. The work on the networked predictive control presented in this thesis can provide basis for further research relating to this area.