Elias Tsoutsanis

Transient Gas Turbine Performance Diagnostics Through Nonlinear Adaptation of Compressor and Turbine Maps

Gas turbines are faced with new challenges of increasing flexibility in their operation while reducing their life cycle costs, leading to new research priorities and challenges. One of these challenges involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis, and prognosis schemes, which will be able to handle and address the gas turbines ever-growing flexible and dynamic operational characteristics. Predicting accurately the performance of gas turbines depends on detailed understanding of the engine components behavior that is captured by component performance maps. The limited availability of these maps due to their proprietary nature has been commonly managed by adapting default generic maps in order to match the targeted off-design or engine degraded measurements. Although these approaches might be suitable in small range of operating conditions, further investigation is required to assess the capabilities of such methods for use in gas turbine diagnosis under dynamic transient conditions. The diversification of energy portfolio and introduction of distributed generation in electrical energy production have created need for such studies. The reason is not only the fluctuation in energy demand but also more importantly the fact that renewable energy sources, which work with conventional fossil fuel based sources, supply the grid with varying power that depend, for example, on solar irradiation. In this paper, modeling methods for the compressor and turbine maps are presented for improving the accuracy and fidelity of the engine performance prediction and diagnosis. The proposed component map fitting methods simultaneously determine the best set of equations for matching the compressor and the turbine map data. The coefficients that determine the shape of the component map curves have been analyzed and tuned through a nonlinear multi-objective optimization scheme in order to meet the targeted set of engine measurements. The proposed component map modeling methods are developed in the object oriented MATLAB/SIMULINK environment and integrated with a dynamic gas turbine engine model. The accuracy of the methods is evaluated for predicting multiple component degradations of an engine at transient operating conditions. The proposed adaptive diagnostics method has the capability to generalize current gas turbine performance prediction approaches and to improve performance-based diagnostic techniques. Copyright © 2015 by ASME.

Dynamic Performance Simulation of an Aeroderivative Gas Turbine Using the Matlab Simulink Environment

In fossil fuel applications, such as air transportation and power generation systems, gas turbine is the prime mover which governs the aircrafts propulsive and the plants thermal efficiency, respectively. Therefore, an accurate engine performance simulation has a significant impact on the operation and maintenance of gas turbines as far as reliability and availability considerations are concerned. Current trends in achieving stable engine operation, reliable fault diagnosis and prognosis requirements do motivate the development and implementation of real-time dynamic simulators for gas turbines that are sufficiently complex, highly nonlinear, have high fidelity and include fast response modules. This paper presents a gas turbine performance model for predicting the transient dynamic behavior of an aero derivativ e engine that is suitable for both mechanical drive and power generation applications. The engine model has been developed in the Matlab/Simulink environment and combines both the inter-component volume and the constant mass flow methods. Dynamic equations of the mass momentum and the energy balance are incorporated into the steady state thermodynamic equations. This allows one to represent the engine model by a set of first order differential and algebraic equations. The developed Simulink model in an object oriented environment, can be easily adapted to any kind of gas turbine configuration. The model consists of a number of subsystems for representing the gas turbines components and the thermodynamic relationships among them. The components are represented by a set of suitable performance maps that are available from the open literature. The engine model has been validated with an established gas turbine performance simulation software. Time responses of the main variables that describe the gas turbine dynamic behavior are also included. The proposed gas turbine model with its dynamic simulation characteristics is a useful tool for development of real-time model-based diagnostics and prognostics technologies. Copyright © 2013 by ASME.

Part-load performance of gas turbines - Part I: A novel compressor map generation approach suitable for adaptive simulation

Part-load performance prediction of gas turbines is strongly dependent on detailed understanding of engine component behavior and mainly that of compressors. The accuracy of gas turbine engine models relies on the compressor performance maps, which are obtained in costly rig tests and remain manufacturers proprietary information. The gas turbine research community has addressed this limitation by scaling default generic compressor maps in order to match the targeted off-design measurements. This approach is efficient in small range of operating conditions but becomes less accurate for wide range of operating conditions. In this part of the paper a novel method of compressor map generation which has a primary objective to improve the accuracy of engine models performance at part load conditions is presented. This is to generate a generic form of equations to represent the lines of constant speed and constant efficiency of the compressor map for a generic compressor. The parameters that control the shape of the compressor map have been expressed in their simplest form in order to aid the adaptation p rocess. The proposed compressor map generation method has the capacity to refine current gas turbine performance adaptation techniques, and it has been integrated into Cranfields PYTHIA gas turbine performance simulation and diagnostics software tool. Copyright © 2012 by ASME.

Part-Load Performance of Gas Turbines: Part II — Multi-Point Adaptation With Compressor Map Generation and GA Optimization

Accurate gas turbine performance simulation is a vital aid to the operational and maintenance strategy of thermal plants having gas turbines as their prime mover. Prediction of the part load performance of a gas turbine depends on the quality of the engine’s component maps. Taking into consideration that compressor maps are proprietary information of the manufacturers, several methods have been developed to encounter the above limitation by scaling and adapting component maps.This part of the paper presents a new off-design performance adaptation approach with the use of a novel compressor map generation method and Genetic Algorithms (GA) optimization. A set of coefficients controlling a generic compressor performance map analytically is used in the optimization process for the adaptation of the gas turbine performance model to match available engine test data.The developed method has been tested with off-design performance simulations and applied to a GE LM2500+ aeroderivative gas turbine operating in Manx Electricity Authority’s combined cycle power plant in the Isle of Man. It has been also compared with an earlier off-design performance adaptation approach, and shown some advantages in the performance adaptation.Copyright

AN EFFICIENT COMPONENT MAP GENERATION METHOD FOR PREDICTION OF GAS TURBINE PERFORMANCE

NPRP grant No. 4-195-2-065 from the Qatar National Research Fund (a member of Qatar Foundation).

Performance-based prognosis scheme for industrial gas turbines

In this paper, we present a novel method for performance-based prognostics of industrial gas turbines. The concept of performance adaptation is implemented through a dynamic engine model that is developed in Matlab/Simulink environment to diagnose the health of the gas turbine. The proposed method is tested under variable operating conditions at both steady state and transient operational modes for estimating and predicting the compressor degradation. Different types of mathematical representations are used to fit the diagnosis results and consequently prognose the performance behavior of the engine. The results demonstrate the promising prospect of our proposed method for predicting accurately and efficiently the performance of gas turbine compressors as they degrade over time.

Forecasting the health of gas turbine components through an integrated performance-based approach

In this study, we present an integrated method for detecting and forecasting the health of gas turbine components as degraded over time. An advanced model-based real time performance adaptation approach is developed for detecting the degradation of engine components via a dynamic engine model that is built in Simulink. The detected health parameters of the engine component are then implemented in a discrete window-based analysis by a regression method in order to forecast their evolution. The proposed approach is tested for an engine with increased flexibility that characterizes modern gas turbine operations. The results demonstrate the promising capabilities of our advanced proposed method for accurate and efficient detection and forecast of the health of gas turbine compressors as degraded over time.