Riti Singh

Parameter selection for diagnosing a gas-turbine's performance-deterioration

The ability to assess faults in a system, while it is operating, requires an appropriate set of measurements. Engine availability can be increased if the faults can be detected, isolated and assessed, so enabling an optimised shutdown of the plant for maintenance to ensue. Depending on the engine-power-setting parameter, the measurements required to diagnose the faults along the gas path of a gas-turbine vary. This study used a non-linear gas-path analysis (NLGPA) model to predict the required instrumentation set, which can be optimised with respect to the number and type of sensors and their locations for the considered engine-faults. A thermodynamic model of the behaviour of a 2-shaft engine is used as a case study. Redundancy in the sensor set is shown to be unnecessary.

Assessment of Future Aero-engine Designs With Intercooled and Intercooled Recuperated Cores

Reduction in CO2 emissions is strongly linked with the improvement of engine specific fuel consumption, as well as the reduction in engine nacelle drag and weight. Conventional turbofan designs, however, that reduce CO2 emissions—such as increased overall pressure ratio designs—can increase the production of NOx emissions. In the present work, funded by the European Framework 6 collaborative project NEW Aero engine Core concepts (NEWAC), an aero-engine multidisciplinary design tool, Techno-economic, Environmental, and Risk Assessment for 2020 (TERA2020), has been utilized to study the potential benefits from introducing heat-exchanged cores in future turbofan engine designs. The tool comprises of various modules covering a wide range of disciplines: engine performance, engine aerodynamic and mechanical design, aircraft design and performance, emissions prediction and environmental impact, engine and airframe noise, as well as production, maintenance and direct operating costs. Fundamental performance differences between heat-exchanged cores and a conventional core are discussed and quantified. Cycle limitations imposed by mechanical considerations, operational limitations and emissions legislation are also discussed. The research work presented in this paper concludes with a full assessment at aircraft system level that reveals the significant potential performance benefits for the intercooled and intercooled recuperated cycles. An intercooled core can be designed for a significantly higher overall pressure ratio and with reduced cooling air requirements, providing a higher thermal efficiency than could otherwise be practically achieved with a conventional core. Variable geometry can be implemented to optimize the use of the intercooler for a given flight mission. An intercooled recuperated core can provide high thermal efficiency at low overall pressure ratio values and also benefit significantly from the introduction of a variable geometry low pressure turbine. The necessity of introducing novel lean-burn combustion technology to reduce NOx emissions at cruise as well as for the landing and take-off cycle, is demonstrated for both heat-exchanged cores and conventional designs. Significant benefits in terms of NOx reduction are predicted from the introduction of a variable geometry low pressure turbine in an intercooled core with lean-burn combustion technology.

Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine

Sensor failures are a major cause of concern in engine-performance monitoring as they can result in false alarms and, in some cases, lead to the condemnation of a non-offending component or section of the engine. This condition has the potential to increase engine downtime and thus incur higher operational costs. The fact that more than a single sensor could be faulty simultaneously should also not be overlooked. In this paper, we present a set of neural networks, modularly designed to diagnose and quantify single and dual-sensor faults in a two-shaft stationary gas-turbine. A further outcome of the analysis is the restructuring of the faulty data to a fault-free form through the filtering out of noise and bias. This restructured data can be used to perform sensor-based calculations accurately. The engine chosen for this analysis is thermodynamically similar in performance to the Rolls Royce (RR) Avon. The data used to train the networks were derived from a non-linear aero-thermodynamic model of the engines behaviour. The results obtained show the good prospects for the use of this technique.

An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks

This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using the Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the concept of a hybrid diagnostics model in which a nested neural network is used with genetic algorithm to form a hybrid diagnostics model. The nested neural network functions as a pre-processor or filter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability, and consistency of the results obtained. In addition significant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR21 engine has been used as the test engine for implementing the diagnostics model.

Overview on Contrail and Cirrus Cloud Avoidance Technology

Greenhouse gas emissions, contrails, and aviation-induced cirrus clouds are the principal pollutants from air traffic, which contribute to the anthropogenic global warming. Recent climate assessments have stressed the importance of contrails and aviation-induced cirrus clouds because they may contribute more than all other aircraft emissions combined. This paper reviews the mechanisms of contrail and aviation-induced cirrus cloud formation from which avoidance strategies are derived and discussed.

Novel approach for improving power-plant availability using advanced engine diagnostics

Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas-turbine and combined-cycle models able to undertake appropriate diagnostics and life-cycle costing. These are complex processes as they include the simulation of such issues as performance and assessment of degraded gas-turbines, life usage and risk analysis. This report describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Network (ANN) and Genetic Algorithm (GA) based techniques have provided new opportunities in the field of engine-fault diagnostics. It also discusses the potential of advanced engine-diagnostics, employing such features as ANN and GA for contributing to the management of availability of industrial gas-turbines.

Implications of engine deterioration for a high-pressure turbine-blade's low-cycle fatigue (LCF) life-consumption

Abstract As a result of experiencing a deterioration of efficiency and/or mass flow, an aero-engine will automatically adjust to a different set of operating characteristics; frequently resulting in changes of rpm and/or turbine entry-temperature in order to provide the same thrust. As such, the stresses that the engine is subjected to will change (and thereby alter the blades low-cycle fatigue-life consumption) relative to that for an engine suffering no deterioration (i.e. in the jargon—a ‘clean’ engine). Rises in the turbines entry-temperatures and the high-pressure turbines rotational-speed result in greater rates of creep and fatigue damage being incurred by the hot-end components and thereby higher engines life-cycle costs. Possessing a better knowledge of the effects of engine deterioration upon the aircrafts performance, as well as fuel and life usages, helps the users to take wiser management decisions and hence achieve improved engine utilization. For a military aircraft, by employing a bespoke computer simulation, the consequences of engine deterioration on a high-pressure turbine blades low-cycle fatigue-life consumption are predicted.

Potential of reducing the environmental impact of aviation by using hydrogen Part I: Background, prospects and challenges

The main objective of the paper is to evaluate the potential of reducing the environmental impact of civil subsonic aviation by using hydrogen fuel. The paper is divided into three parts of which this is Part I, where the background, prospects and challenges of introducing an alternative fuel in aviation are outlined. In Part II the aero engine design when using hydrogen is covered, and in Part III the subjects of optimum cruising altitude and airport implications of introducing liquid hydrogen-fuelled aircraft are raised. Looking at the prospect of alternative fuels, synthetic kerosene produced from biomass turns out to be feasible and offers environmental benefits in the short run, whereas hydrogen seems to be the more attractive alternative in the long run. Powering aero engines and aircraft with hydrogen has been done successfully on a number of occasions in the past. Realising this technology change for a fleet of aircraft poses formidable challenges regarding technical development, energy requirement for producing hydrogen, handling, aircraft design and making liquid hydrogen economically compatible with kerosene.

Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks

Transient and steady state data may contain the same essential fault information but some faults have been shown to be more easily detectable from transient data because the transient records provide significant diagnostic content especially as the fault effects are magnified under transient. Various traditional and conventional techniques such as fault trees, fault matrixes, gas path analysis and its variants have been applied to gas path fault diagnosis of gas turbines. Recently, artificial intelligence techniques such as artificial neural networks (ANN) as well as optimization techniques such as genetic algorithm (GA) are being explored for fault diagnosis activities. In this paper, a novel approach to gas path fault diagnosis is proposed. The method involves the use of ANN with engine transient data. A set of nested neural networks designed to estimate independent parameter (efficiencies and flow capacities) changes due to faults within single or multiple components of a turbofan engine are presented. The approach involves classification and approximation type networks. Measurements from the engine are first assessed by a trained network and if a fault is diagnosed, are then classified into two groups — those originating from sensor faults and those from component faults, by another trained network. Other trained networks continue the fault isolation process and finally the magnitude of the fault(s) is quantified. A computer simulation of the process shows that results from a batched process of these networks can be obtained in less than three seconds. Four of the gas path components — intermediate pressure compressor (IPC), high pressure compressor (HPC), high pressure turbine (HPT) and low pressure turbine (LPT) — and measurements from eight sensors are considered. Sensor noise and bias are also considered in this analysis. The comparison of fault signatures from a steady state and transient process show that diagnosis with transient data can improve the accuracy of gas turbine fault diagnosis.Copyright

Computational analysis of the effects of a boundary layer ingesting propulsion system in transonic flow

Continuous requirements for more efficient aircrafts lead to the design and analysis of novel propulsion configurations, with an example being the boundary layer ingestion. The complexity and integration challenges in such aircraft synergistic propulsion system characterize the research in this field, driven by the potential benefits. The aim of this article is to investigate the effects of boundary layer ingestion on the aerodynamics of a transonic wing, together with the quality of the flow ingested by the propulsion system. A two-dimensional computational model of a transonic airfoil with boundary layer ingesting propulsion system is developed in order to assess boundary layer ingestion for a commercial air transport at cruise conditions and highlight the complex integration issues arising from such configuration. A parametric analysis of the effects of flight conditions, nacelle geometry and engine operating point, on lift, pressure recovery, distortion, total pressure and velocity distribution at the intake, comes to enhance understanding of the performance of this configuration. The pressure distribution around the airfoil and the boundary layer growth are both substantially affected by the engine operating condition, which is represented by the mass flow ratio, with a direct impact on pressure recovery and lift. Mach number and angle of attack influences on lift and drag ingested are also investigated. Intake size and position on the airfoil appear to have significant effects on lift and losses ingested. In general, the results of this study include several aspects related to wing aerodynamics and ingested flow quality, which may facilitate design and integration of the boundary layer ingestion propulsion system for future commercial aircraft.