Browsing by Author "Li, Y. G."
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Item Open Access CCGT performance simulation and diagnostics for operations optimisation and risk management(Cranfield University, 2007-10) Mucino, Marco; Li, Y. G.; Moir, LanceThis thesis presents a techno-economic performance simulation and diagnostics computational system for the operations optimisation and risk management of a CCGT power station. The project objective was to provide a technological solution to a business problem originated at the Manx Electricity Authority (MEA). The CCGT performance simulation program was created from the integration of existing and new performance simulation codes of the main components of a CCGT power station using Visual Basic for Applications (VBA) in Excel ®. The specifications of the real gas turbine (GT) engines at MEA demanded the modification of Turbomatch, a GT performance simulation code developed at Cranfield University. The new capabilities were successfully validated against previous work in the public domain. In the case of the steam cycle, the model for a double pressure once-through steam generator (OTSG) was produced. A novel approach using theoretical thermohydraulic models for heat exchangers and empiric correlations delivered positive results. Steamomatch, another code developed at the university, was used for the steam turbine performance simulation. An economic module based on the practitioners’ definition for spark spread was developed. The economic module makes use of the technical results, which are permanently accessible through the user interface of the system. The assessment of an existing gas turbine engine performance diagnostics system, Pythia, was made. The study tested the capabilities of the program under different ambient and operating conditions, signal noise levels and sensor faults. A set of guidelines aimed to increase the success rate of the diagnostic under the data and sensor restricted scenario presented by at MEA was generated. Once the development phase was concluded, technical and economic studies on the particular generation schedule for a cold day of winter 2007 were conducted. Variable ambient and operating conditions for each of the 48 time block forming the schedule were considered. The results showed error values below the 2% band for key technical parameters such as fuel flow, thermal efficiency and power output. On the economic side, the study quantified the loss making operation strategy of the plant during the offpeak market period of the day. But it also demonstrated the profit made during the peak hours lead to an overall positive cash flow for the day. A number of optimisation strategies to increase the profitability of the plant were proposed highlighting the economic benefit of them. These scenarios were based on the technical performance simulation of the plant under these specific conditions, increasing the reliability of the study. Finally, a number of risk management strategies aimed to protect the operations of a power generator from the main technical and economic risk variables were outlined. It was concluded that the use of techno-economic advanced tools such as eCCGT and Pythia can positively affect the way an operator manages a power generation asset through the implementation of virtually proven optimisation and risk management strategies.Item Unknown A gas turbine diagnostic approach with transient measurements.(Professional Engineering Publishing, 2003-04-01T00:00:00Z) Li, Y. G.Most gas turbine performance analysis based diagnostic methods use the information from steady state measurements. Unfortunately, steady state measurement may not be obtained easily in some situations, and some types of gas turbine fault contribute little to performance deviation at steady state operating conditions but significantly during transient processes. Therefore, gas turbine diagnostics with transient measurement is superior to that with steady state measurement. In this paper, an accumulated deviation is defined for gas turbine performance parameters in order to measure the level of performance deviation during transient processes. The features of the accumulated deviation are analysed and compared with traditionally defined performance deviation at a steady state condition. A non-linear model based diagnostic method, combined with a genetic algorithm (GA), is developed and applied to a model gas turbine engine to diagnose engine faults by using the accumulated deviation obtained from transient measurement. Typical transient measurable parameters of gas turbine engines are used for fault diagnostics, and a typical slam acceleration process from idle to maximum power is chosen in the analysis. The developed diagnostic approach is applied to the model engine implanted with three typical single-component faults and is shown to be very successful.Item Open Access Influence of deterministic stresses on flow prediction of a low-speed axial flow compressor rear stage.(Professional Engineering Publishing, 2001-10-02T00:00:00Z) Li, Y. G.; Tourlidakis, A.Deterministic stresses are used to describe the unsteady effects on the flow field in multistage axial flow compressors. In this paper, a circumferentially non-uniform mixing plane method implemented with deterministic stresses is used for the flow prediction inside the rear stage of an axial flow compressor in a multistage environment. The deterministic stress field is calculated with an overlapped solution approach for the same flow passage and implemented with the Navier-Stokes equations in order to provide continuous interfaces. The aerodynamic boundary condition is simplified with the application of a repeating stage model where only the mass flowrate and stage exit average static pressure are required. Two computational cases for the third stage of the Cranfield low-speed research compressor, one with the mixing plane model and the second with the mixing plane model in combination with deterministic stresses, are examined and compared with each other and also with corresponding experimental data. It is shown that the implementation of the deterministic stresses is beneficial for the flow prediction but the improvement in accuracy for low-speed axial flow compressor rear stages is not significant.Item Open Access A non-linear weighted least squares gas turbine diagnostic approach and multi-fuel performance simulation(Cranfield University, 2011-01) Kamunge, Daniel; Li, Y. G.The gas turbine which has found numerous applications in Air, Land and Sea applications, as a propulsion system, electricity generator and prime mover, is subject to deterioration of its individual components. In the past, various methodologies have been developed to quantify this deterioration with varying degrees of success. No single method addresses all issues pertaining to gas turbine diagnostics and thus, room for improvement exists. The first part of this research investigates the feasibility of non-linear W eighted Least Squares as a gas turbine component deterioration quantification tool. Two new weighting schemes have been developed to address measurement noise. Four cases have been run to demonstrate the non-linear weighted least squares method, in conjunction with the new weighting schemes. Results demonstrate that the non-linear weighted least squares method effectively addresses measurement noise and quantifies gas path component faults with improved accuracy over its linear counterpart and over methods that do not address measurement noise. Since Gas turbine diagnostics is based on analysis of engine performance at given ambient and power setting conditions; accurate and reliable engine performance modelling and simulation models are essential for meaningful gas turbine diagnostics. The second part of this research therefore sought to develop a multi-fuel and multi-caloric simulation method with the view of improving simulation accuracy. The method developed is based on non-linear interpolation of fuel tables. Fuel tables for Jet-A, UK Natural gas, Kerosene and Diesel were produced. Six case studies were carried out and the results demonstrate that the method has significantly improved accuracy over linear interpolation based methods and methods that assume thermal perfection.Item Open Access Performance based creep life estimation for gas turbines application(Cranfield University, 2011-12) Abdul Ghafir, Mohammad Fahmi Bin; Li, Y. G.Accurate and reliable component life prediction is crucial to ensure both the safety and economics of gas turbine operations. In the pursuit of improved accuracy and reliability, current model-based creep life estimation methods have become more and more complicated and therefore demand huge amounts of work and significant amounts of computational time. Because of the underlying problems arising from current life estimation methods, this research aims to develop an alternative performance-based creep life estimation method that is able to provide a quick solution to creep life prediction while at the same time maintaining the achieved accuracy and reliability as that of the model-based method. Using an artificial neural network, the existing creep life prediction subprocesses and secondary inputs are ‘absorbed’ into simple parallel computing units that are able to create direct mapping between various gas turbine operating and health conditions or gas path sensors and creep life. The outcome of this research is the creation of three proposed neural-based creep life prediction architectures known as the Range-Based, Functional-Based and Sensor-Based. An integrated creep life estimation model was first developed and incorporated into an in-house performance simulation and diagnostics software. Using the integrated model, the effects of several operating and health parameters on a selected turbo-shaft engine model turbine blade’s creep life was initially performed using an introduced Creep Factor approach. The outcomes of this investigation were then used to populate input-output samples to train and validate the neural-based creep life prediction architectures. To ensure that the proposed neural architectures are able to achieve generalisation and produce accurate creep life prediction for both clean and degraded engine conditions, four-stage assessments were carried out. Finally, the effects of input uncertainties on the creep life prediction were investigated to assess how sensitive the proposed architectures are to different levels of uncertainty. The results show that all of the proposed neural architectures were able to produce accurate creep life predictions for both clean and degraded engine conditions. When comparing the three proposed architectures, the Sensor-Based architecture was found to be the most accurate in both conditions. Despite the accurate creep life prediction, it was also found that all of the proposed architectures were sensitive to input uncertainties with the Functional-Based architecture being the least sensitive to the uncertainty.Item Open Access Performance-analysis-based gas turbine diagnostics: a review.(Professional Engineering Publishing, 2002-09-01T00:00:00Z) Li, Y. G.Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960’s when Urban introduced Gas Path Analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aero and industrial applications. Until now a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance analysis based methods available thus far for gas turbine fault diagnosis on open literature.Item Open Access Rough set based gas turbine fault isolation study(Cranfield University, 2010-07) Wang, Lihui.; Li, Y. G.Gas path fault isolation is one of the key techniques in Engine Health Management systems. In order to accomplish gas path fault isolation successfully for a gas turbine engine, both an accurate off-design performance model and an effective fault isolation approach are necessary. In this thesis, two original and useful contributions to knowledge are presented: a new gas turbine off-design performance model adaptation approach and a new gas turbine fault isolation approach. This new adaptation approach uses optimal multiple scaling factors obtained by using a Genetic Algorithm to scale inaccurate component characteristic maps in gas turbine performance models to improve their prediction accuracy in different off-design conditions. The major feature of this approach is that it provides non- linear map scaling and therefore is able to provide more effective adaptation. The new fault isolation approach can be used to discover knowledge hidden in engine fault samples, transfers that knowledge into rules, and then uses those rules for fault isolation. In addition, it is also capable of selecting appropriate measurements for fault isolation, dealing with uncertainty caused by measurement noise. Enhanced fault signatures, which are represented by the measurement deviations and their ranking pattern in terms of magnitude, are developed to make gas turbine faults easier to distinguish and hence make this fault isolation approach more effective. The new adaptation approach was applied to the off-design performance model adaptation of a gas turbine, while the new fault isolation approach was employed for fault isolation in a gas turbine. The results show that the new adaptation approach is very effective in improving the prediction accuracy of off- design performance models and the new fault isolation approach is not only effective in fault isolation but also in selecting measurements for isolation and generating fault isolation rules.