Academic literature on the topic 'Simulation models reliability assessment gas turbine auxiliary systems'

Gas turbine-based power plants are found to play a vital role in electric power generation and act as spinning reserves for renewable electric power. A robust performance assessment tool is inevitable for a gas turbine system to maintain high operational flexibility, availability, and reliability at different operating conditions. A suitable simulation model of the gas turbine provides detailed information about the system operation under varying ambient and load conditions. This paper illustrates a systematic methodology for process history data-based modelling of a gas turbine compressor system. The ReliefF feature selection method is applied for the proper identification of the parameters influencing the compressor efficiency. Appropriate Artificial Neural Network (ANN) based models are developed for data classification and system modelling of the compressor. The model performance has been validated using actual plant operational data, and the standard deviation of the error in model output was found to be 0.38. A novel approach for suitable integration of data processing methods, machine learning tools and gas turbine domain knowledge has led to the development of a robust compressor model. The model has been utilized for the health assessment of an existing gas turbine compressor, demonstrated through an illustrative case study. The model has been found suitable for parametric analysis of compressor efficiency with operating hours, which is helpful for operational decision-making involving studies on the influence of part-load operation, compressor wash planning, maintenance planning etc

A significant problem encountered in the gas turbine industry with fuel products is the degradation of fuel and fuel systems by micro-organisms, which are largely bacteria, embedded in biofilms. These micro-organisms cause system fouling and other degradatory effects, extending often to sudden failure of components with cost implications. Current methods of assessment are only postimpact evaluation and do not necessarily quantify the effects of fuel degradation on engine performance and emission. Therefore, effective models that allow predictive condition monitoring are required for engine's fuel system reliability, especially with readily biodegradable biofuels. The aim of this paper is to introduce the concept of biofouling in gas turbines and the development of a biomathematical model with potentials to predict the extent and assess the effects of microbial growth in fuel systems. The tool takes into account mass balance stoichiometry equations of major biological processes in fuel biofouling. Further development, optimization, and integration with existing Cranfield in-house simulation tools will be carried out to assess the overall engine performance and emission characteristics. This new tool is important for engineering design decision, optimization processes, and analysis of microbial fuel degradation in gas turbine fuels and fuel systems.

The interest in RAMS (Reliability, Availability, Maintainability and Safety) and diagnostics parameters is growing in many different manufacturing fields. These branches of knowledge are nowadays crucial and play a fundamental role in industrial engineering becoming focal part of performance requirements. Modern technologies and business requirements are producing a growth in variety and complexity of manufacturing product and this trend increased number and variety of failures. System downtime and unplanned outages massively affect plant productivity. In many Oil&Gas applications an emergency shutdown produces an interruption of normal running operation, a considerable productivity reduction and a loss of thousands dollars [1-2]. This is the reason why RAMS disciplines together with fault diagnosis and condition monitoring are almost mandatory in Oil&Gas applications where products are forced to endure extreme process and environmental conditions [3]. This thesis is focused on availability improvement and takes into account maintainability and, in particular, reliability roles in order to achieve this kind of target. The goal is to develop a procedure for availability improvement that engineers may used during the early stages of product design. Availability means that a system is “on-line” if it is involved in continuous running condition or “ready to use” in case of on-demand” usage. As said before, in modern systems there are a great variety of factors that can take a system off-line, ranging from scheduled maintenance downtime to catastrophic failures. The goal of improving system availability is to detect incipient failures, minimize downtime and minimize the time needed to restore the system to normal working conditions. Obviously the margin of downtime tolerance is directly associated with the system application and this requirement impose the complexity and the corresponding cost of the solution [4-6]. Reliability prediction is the main focus of this study since it turned out to be best method in RAM (Reliability, Availability and Maintainability) analysis for industrial applications: reliability prediction is very helpful in order to evaluate design feasibility, compare design choices, identify potential failure areas, trade-off system design factors and track reliability improvement. This is the reason why the best solution to improve system availability in the early product design stages turned out to be reliability-oriented since it provides reliability feedback to design engineers in order to reduce re-design costs and time for upgrades. This thesis is organized as follows: Chapter 1 contains a brief description of Life Data Analysis focusing on the comparison of two failure distributions, Exponential and Weibull. The second Chapter shows the best Availability improvement methods starting from standby redundancy and comparing cold and warm standby solutions. Chapter 3 deepens the Reliability Allocation procedures starting from a review of the methods described in literature and showing a new solution to achieve allocation parameters in complex systems; this Chapter contains also the description of a new Reliability Importance procedure (Credible Improvement Potential) and its application on Auxiliary Systems of a gas turbine. Chapter 4 describes the Condition-based Maintenance using Markov models with some applications in case of complex repair solutions and standby spares; Chapter 5 shows the basis of fault detection, isolation, reconfiguration, diagnostics and Condition Monitoring. This Chapter contains both on-board and logic solver diagnostics with a detailed application on a gas turbine safety loop and corresponding Probability of Failure on Demand (PFD) assessment [7-8]. The final Chapter describes the Reliability Assessment Loop with the brand new approach proposed and show the potential of the tool that was developed to achieve a reliability prediction in the early product design stages.

Gas turbines enclosures entail a high number of auxiliary systems which must be preserved from heat, ensuring therefore the long term operation of the internal instrumentation and of the data acquisition system. A dedicated ventilation system is designed to keep the enclosure environment sufficiently cool and dilute any gas coming from potential internal leakage to limiting explosion risks. These systems are equipped with axial fans, usually fed with air coming from the filter house which provides air to the gas turbine combustion system, through dedicated filters. The axial fans are embedded in a ducting system which discharges fresh air inside the enclosure where the gas turbine is housed. As the operations of the gas turbine need to be guaranteed in the event of fan failure, a backup redundant system is located in a duct parallel to the main one. One of the main requirements of a ventilation fan is the reliability over the years as the gas turbine can be installed in remote areas or unmanned offshore platforms with limited accessibility for unplanned maintenance. For such reasons, the robustness of the ventilation system and a proper understanding of coupling phenomena with the axial fan is a key aspect to be addressed when designing a gas-turbine system. Here a numerical study of a ventilation system carried out with RANS and LES based methodologies will be presented where the presence of the fan is synthetized by means of static pressure discontinuity. Different operations of the fans are investigated by means of RANS in order to compare the different operating points, corresponding to 1) clean and 2) dirty filters operations, 3) minimum and 4) maximum pressure at the discharge section. Large Eddy Simulations of the same duct were carried out in the maximum loading condition for the fan to investigate the unsteady response of the system and validate its correct arrangement. All the simulations were carried out using OpenFOAM, a finite volume open source code for CFD analysis, treating the filters as a porous medium and the fan as a static pressure discontinuity according to the manufacturer’s characteristic curve. RANS modelling was based on the cubic k-ε model of Lien et al. while sub-grid scale modelling in LES was based on the 1 equation model of Davidson. Computations highlighted that the ventilation system was able to work in similarity for flow rates between 15 m3/s and 23.2 m3/s and that the flow conditions onto the fan suggest that the aerodynamic stress on the device could be reduced introducing in the duct flow straighteners or inlet guided vanes.

A significant problem encountered in the gas turbine industry with fuel products is the degradation of fuel and fuel systems by microorganisms, which are largely bacteria, embedded in biofilms. These microorganisms cause system fouling and other degradatory effects, extending often to sudden failure of components with cost implications. Current methods of assessment are only post-impact evaluation and do not necessarily quantify the effects of fuel degradation on engine performance and emission. Therefore, effective models that allow predictive condition monitoring are required for engine’s fuel system reliability, especially with readily biodegradable biofuels. The aim of this paper is to introduce the concept of bio-fouling in gas turbines and the development of a bio-mathematical model with potentials to predict the extent and assess the effects of microbial growth in fuel systems. The tool takes into account mass balance stoichiometry equations of major biological processes in fuel bio-fouling. Further development, optimization and integration with existing Cranfield in-house simulation tools will be carried out to assess the overall engine performance and emission characteristics. This new tool is important for engineering design decision, optimization processes and analysis of microbial fuel degradation in gas turbine fuels and fuel systems.

Abstract Adaptive modelling (AM) based Gas Path Analysis (GPA) is a powerful diagnostic and prognostic technique for turbofan engine maintenance. This involves the assessment of turbofan component condition using thermodynamic models that can iteratively adapt to measurements values in the gas path by changing component condition parameters. The problem with this approach is that newer turbofan engines such as the General Electric GEnx-1B have fewer gas path sensors installed causing the AM equation systems to become underdetermined. To overcome this problem, a novel approach has been developed that combines the AM model with an Evolutionary Algorithm (EA) optimization scheme and applies it to multiple operating points. Additionally, these newer turbofan engines provide performance data continuously during flight. Information on variable geometry and bleed valve position, active clearance control state and power off-take is included and can be accounted for to further enhance AM model accuracy. A procedure is proposed where the selection of operating points is based on steady-state stability requirements, cycle model operating point uncertainty and parameter outlier filtering. The Gas turbine Simulation Program (GSP) is used as the non-linear GPA modelling environment. A Multiple Operating Point Analysis (MOPA) is chosen to overcome the problem of underdetermination by utilizing multiple data sets at different operating points. The EA finds the best fit of health parameter deviations by minimizing the multi-point objective function using the GSP AM model. A sub-form of the EA class named Differential Evolution (DE) has been chosen as the optimizer. Like all EAs, DE is a parallel direct search method in which a population of parameter vectors evolves following genetic operations towards an optimum output candidate. The resulting hybrid GPA tool has been verified by solving for different simulated deterioration cases of a GSP model. The tool can identify the direction and magnitude of condition deviation of 10 health parameters using 6 gas path sensors. It has subsequently been validated using historical in-flight data of the GEnx-1B engine. It has demonstrated successful tracking of engine component condition for all 10 health parameters and identification of events such as turbine blade failure and water washes. The authors conclude that the tool has proven significant potential to enhance turbofan engine condition monitoring accuracy for minimizing maintenance costs and increasing safety and reliability.