Browsing by Author "Tsoutsanis, Elias"

Now showing 1 - 5 of 5
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    A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions
    (Elsevier, 2022-04-26) Chen, Yu-Zhi; Tsoutsanis, Elias; Xiang, Hen-Chao; Li, Yi-Guang; Zhao, Jun-Jie
    At present, aero engine fault diagnosis is mainly based on the steady-state condition at the cruise phase, and the gas path parameters in the entire flight process are not effectively used. At the same time, high quality steady-state monitoring measurements are not always available and as a result the accuracy of diagnosis might be affected. There is a recognized need for real-time performance diagnosis of aero engines operating under transient conditions, which can improve their condition-based maintenance. Recent studies have demonstrated the capability of the sequential model-based diagnostic method to predict accurately and efficiently the degradation of industrial gas turbines under steady-state conditions. Nevertheless, incorporating real-time data for fault detection of aero engines that operate in dynamic conditions is a more challenging task. The primary objective of this study is to investigate the performance of the sequential diagnostic method when it is applied to aero engines that operate under transient conditions while there is a variation in the bypass ratio and the heat soakage effects are taken into consideration. This study provides a novel approach for quantifying component degradation, such as fouling and erosion, by using an adapted version of the sequential diagnostic method. The research presented here confirms that the proposed method could be applied to aero engine fault diagnosis under both steady-state and dynamic conditions in real-time. In addition, the economic impact of engine degradation on fuel cost and payload revenue is evaluated when the engine under investigation is using hydrogen. The proposed method demonstrated promising diagnostic results where the maximum prediction errors for steady state and transient conditions are less than 0.006% and 0.016%, respectively. The comparison of the proposed method to a benchmark diagnostic method revealed a 15% improvement in accuracy which can have great benefit when considering that the cost attributed to degradation can reach up to $702,585 for 6000 flight cycles of a hydrogen powered aircraft fleet. This study provides an opportunity to improve our understanding of aero engine fault diagnosis in order to improve engine reliability, availability, and efficiency by online health monitoring.
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    Non-linear model calibration for off-design performance prediction of gas turbines with experimental data
    (Cambridge University Press, 2017-09-18) Tsoutsanis, Elias; Li, Yi-Guang; Pilidis, Pericles; Newby, Michael A.
    One of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.
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    Performance adaptation of gas turbines for power generation applications
    (Cranfield University, 2010-06-29) Tsoutsanis, Elias; Li, Yiguang; Pilidis, Pericles; Newby, Michael A.
    One of the greatest challenges that the world is facing is that of providing everyone access to safe and clean energy supplies. Since the liberalization of the electricity market in the UK during the 1990s many combined cycle gas turbine (CCGT) power plants have been developed as these plants are more energy efficient and friendlier to the environment. The core component in a combined cycle plant is the gas turbine. In this project the MEA’s Pulrose Power Station CCGT plant is under investigation. This plant cronsists of two aeroderivative LM2500+ gas turbines of General Electric for producing a total of 84MW power in a combined cycle configuration. Cont/d.
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    Techno-economic evaluation and optimization of CCGT power plant: a multi-criteria decision support system
    (Elsevier, 2021-04-19) Chen, Yu-Zhi; Li, Yi-Guang; Tsoutsanis, Elias; Newby, Michael A.; Zhao, Xu-Dong
    A key objective of the power generation industry is to achieve maximum economic benefit without over-consuming the life of power plants and over-maintaining its assets. From a CCGT power plant operator’s perspective, the stand-alone performance analysis of the plant is not enough to support the decision-making process due to the plethora of possible scenarios characterized by variable ambient conditions, engine health (fouling, erosion), electricity prices, and power demand. This study proposes a novel methodology to support decision-making for a CCGT power plant’s operational optimization. The comprehensive techno-economic performance evaluation is conducted by multidisciplinary optimization and decision-making to enhance information integration for the combined cycle power plant operated by Manx Utilities in the Isle of Man, UK. The decision support system has the capability to recommend the optimal operation schedules to plant operators. It recommends that the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimization framework has the potential to assist power plant operators in deciding the total power output and power split between gas turbines based on optimization results that considers both immediate thermo-economic benefits and life consumption. Finally, the proposed system can facilitate similar power plants to adjust daily operations to achieve thermo-economic and lifing benefits
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    A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions
    (Elsevier, 2022-10-29) Chen, Yu-Zhi; Tsoutsanis, Elias; Wang, Chen; Gou, Lin-Feng; Nikolaidis, Theoklis
    In recent years there has been a growing interest in gas turbine fault diagnosis, especially under dynamic conditions, due to the evolving operating profile of gas turbines and the need to deploy computationally efficient and high-precision diagnostic solutions in real-time. One of the main challenges of fault diagnosis in real-time is the power imbalance between the compressor and turbine that occurs during transient operation. In addition, the heat soakage phenomenon characterizing the transient conditions has a substantial impact on the accuracy of the diagnosis. Finally, any sudden failure that might happen during transient operating conditions creates an additional challenge to fault diagnostics. The present study proposes a gas turbine diagnostic approach based on time-series measurements encapsulating steady-state and transient operating conditions. Specifically, the introduced novel approach is capable of quantifying the surplus/deficit of the power between the compressor and the turbine by utilizing the time-series data representing the observed deviations in the shaft rotational speed in order to determine the power balance in the shaft. The maximum diagnostic errors for constant fault and sudden failure are less than 0.006% during the dynamic maneuver. The results demonstrate and illustrate that the proposed method could effectively and accurately diagnose the severity of aero-engine faults at both steady-state and transient conditions. Therefore, this study has great potential for gas turbine practitioners since the diagnosis under transient conditions in real-time can enhance the capability of engine online condition monitoring and improve the condition-based maintenance of gas turbine assets.