CERES :: Browsing by Author "Sampath, Suresh"

Browsing by Author "Sampath, Suresh"

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Open Access
Aeroengine transient performance simulation integrated with generic heat soakage and tip clearance model
(Cambridge University Press, 2022-03-11) Li, Zhuojun; Li, Yi-Guang; Sampath, Suresh
The simulations and assessment of transient performance of gas turbine engines during the conceptual and preliminary design stage may be conducted ignoring heat soakage and tip clearance variations due to lack of detailed geometrical and structural information. As a result, problems with transient performance stability may not be revealed correctly, and corresponding design iterations would be necessary and costly when those problems are revealed at a detailed design stage. To make an engine design more cost and time effective, it has become important to require better transient performance simulations during the conceptual and preliminary design stage considering all key impact factors such as fuel control schedule, rotor dynamics, inter-component volume effect as well as heat soakage and tip clearance variation effects. In this research, a novel transient performance simulation approach with generically simplified heat soakage and tip clearance models for major gas path components of gas turbine engines including compressors, turbines and combustors has been developed to support more realistic transient performance simulations of gas turbine engines at conceptual and preliminary design stages. Such heat soakage and tip clearance models only require thermodynamic design parameters as input, which is normally available during such design stages. The models have been implemented into in-house transient performance simulation software and applied to a model twin-spool turbojet engine to test their effectiveness. Comparisons between transient performance simulated with and without the heat soakage and tip clearance effects demonstrate that the results are promising. Although the introduced heat soakage and tip clearance models may not be as accurate as that using detailed component geometrical information, it is able to include the major heat soakage and tip clearance effects and make the transient performance simulations and analysis more realistic during conceptual and preliminary engine design stage.
Open Access
Benefits, drawbacks, and future trends of Brayton helium gas turbine cycles for gas-cooled fast reactor and very-high temperature reactor Generation IV nuclear power plants
(American Society of Mechanical Engineers, 2020-10-02) Gad-Briggs, Arnold; Osigwe, Emmanuel O.; Pilidis, Pericles; Nikolaidis, Theoklis; Sampath, Suresh; Teixeira, Joao Amaral
Numerous studies are on-going on to understand the performance of generation IV (Gen IV) nuclear power plants (NPPs). The objective is to determine optimum operating conditions for efficiency and economic reasons in line with the goals of Gen IV. For Gen IV concepts such as the gas-cooled fast reactors (GFRs) and very-high temperature reactors (VHTRs), the choice of cycle configuration is influenced by component choices, the component configuration and the choice of coolant. The purpose of this paper to present and review current cycles being considered—the simple cycle recuperated (SCR) and the intercooled cycle recuperated (ICR). For both cycles, helium is considered as the coolant in a closed Brayton gas turbine configuration. Comparisons are made for design point (DP) and off-design point (ODP) analyses to emphasize the pros and cons of each cycle. This paper also discusses potential future trends, include higher reactor core outlet temperatures (COT) in excess of 1000 °C and the simplified cycle configurations.
Open Access
A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation
(Elsevier, 2020-06-18) Togni, Simone; Nikolaidis, Theoklis; Sampath, Suresh
The target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics.
Open Access
Comparing different schemes in a combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines online diagnostics
(American Society of Mechanical Engineers, 2022-10-28) Togni, Simone; Nikolaidis, Theoklis; Sampath, Suresh
The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise. The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations. The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise.
Open Access
Comparison of lifing results of gas turbine operated in base load and as a back up to wind turbine
(IJRES, 2019-04-30) Mohamed, Saleh; Ali, Ezeddin; Pilidis, Pericles; Sampath, Suresh
When operating the gas turbine in a flexible mode as a back up to renewable energy sources such as wind, solar, tidal and so on. A fluctuation of power produced by the GT will be apparent which in turn will cause low cycle fatigue in the high-pressure turbine blades. The drive behind this study is to estimate the life of a 100 MW GT operated in a baseload scenario and compare the lifing results with two different scenarios of operating the GT as a back up to a wind turbine operated in the UK in 2016. For the estimation of the GT lifing, some performance parameters are essential such as turbine entry temperature (TET), blade cooling temperature (Tc), and the shaft rotational speed (PCN). All these parameters are obtained from running the in-house TURBOMATCH model, which was developed in Cranfield University, under certain operating conditions (temperature and pressure). These values are used with other parameters as input to a FORTRAN code to estimate the lifing and lifing consumption of the GT. In comparison, it was found that the base load scenario has the highest value of creep while in the backup scenarios the LCF was higher due to the power fluctuation.
Open Access
Convolutional neural network denoising autoencoders for intelligent aircraft engine gas path health signal noise filtering
(American Society of Mechanical Engineers, 2022-10-31) Zhao, Junjie; Li, Yiguang; Sampath, Suresh
Removing noise from health signals is critical in gas path diagnostics of aircraft engines. An efficient noise filtering/denoising method should remove noise without using future data points, preserve important changes, and promote accurate diagnostics without time delay. Machine Learning (ML)-based methods are promising for high fidelity, accuracy, and computational efficiency under the motivation of Intelligent Engines. However, previous ML-based denoising methods are rarely applied in actual engineering practice because they cannot accommodate time series and cannot effectively capture important changes or are limited by the time delay problem. This paper proposes a Convolutional Neural Network Denoising Autoencoder (CNN-DAE) method to build a denoising autoencoder structure. In this structure, a convolutional operation is used to accommodate time series, and causal convolution is introduced to solve the problem of using future data points. The proposed denoising method is evaluated against NASA's Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) software. It has been proved that the proposed method can accommodate time series, remove noise for improved denoising accuracy and preserve the important changes for enhanced diagnostic information. NASA's blind test case results show that Kappa Coefficient of a common diagnostic method using the processed data is 0.731 and is at least 0.046 higher than the other diagnostic methods in the open literature. Processing health signals using the proposed method would significantly promote accurate diagnostics without time delay. The proposed method could support intelligent condition monitoring systems by exploiting historical information for improved denoising and diagnostic performance.
Open Access
Dealing with missing data for prognostic purposes
(IEEE, 2017-01-19) Loukopoulos, Panagiotis; Sampath, Suresh; Pilidis, Pericles; Zolkiewski, G.; Bennett, I.; Duan, F.; Mba, David
Centrifugal compressors are considered one of the most critical components in oil industry, making the minimization of their downtime and the maximization of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones.
Open Access
Design feasability of the electrical network for turboelectric aircraft propulsion.
(2020-01) Ibrahim, Kingsley; Sampath, Suresh; Nalianda, Devaiah
The motivation for this research is the need for safer and more environmentally friendly air transport system. Electrical propulsion systems have been identified as a potential method for improving aircraft performance going forward. The implementation of electrical drive trains for future aircraft propulsion comes with many challenges, due to the novelty and scale of the intended deployments. Major technological advancements and research are ongoing at system and component level to meet this ambition. However, the feasibility aspects of these studies have focused more on the engine side than on the electrical aspects, especially with regards to system reliability and stability. These have been considered in the earlier proposed sizing methods, using assumed fault and transient current magnitudes. Such assumption implies that the control and protection systems, may not properly handle abnormal operational scenarios. The aim of this research is to establish a procedure for sizing components of an electric propulsion system considering reliability and stability. The major objective is to properly quantify the operating parameters in non-steady state operations, like transients and fault scenarios, and establish that components are operating within their thermal limits at all operational stages. The contribution of this work is the development of a method that incorporates stability and reliability in the sizing process of electrical propulsion networks. The practicality of the proposed methods has also been validated experimentally, using a test facility set up for this study. The impact of this work is the reduction of the design uncertainties, resulting from assumed fault and transient characteristics of an electrical propulsion system. The results show that the assumptions in earlier researches do not suffice for the investigated architectures. A considerable mass penalty is incurred, with the power electronic devices having to be sized for slightly higher than the maximum transient currents.
Open Access
Diagnostics of gas turbine systems using gas path analysis and rotordynamic response approach
(ISABE, 2017-09-08) Jombo, Gbanaibolou; Sampath, Suresh; Gray, Iain
The modern gas turbine is plagued with issues centred on improving engine availability and limiting component degradation. The integrated use of different condition monitoring techniques presents a solution to addressing these challenges. This paper lays a foundation for the integration of gas path analysis and the rotordynamic response of the compressor to monitor the effect of fouling in the compressor. In investigating the resultant interaction between the aerodynamic and rotordynamic domain in a compressor caused by fouling, an approach involving the interaction of four different models is explored. The first model, a gas turbine engine performance model is used to simulate a fouled compressor and quantify the extent of performance deterioration with gas path analysis. The extent of performance deterioration from the engine performance model represented by scaling of the compressor maps becomes an input in the second model, a Moore-Greitzer compression system model, which evaluates the disturbed flow field parameters in the fouled compressor. The third model, a momentum-based aerodynamic force model, predicts the fouling induced aerodynamic force based on the disturbed flow field parameters. The aerodynamic force acting as a forcing function in the fourth model, a compressor rotordynamic model, produces the vibration response. From the investigation carried out in this work, it is observed, as the rate of fouling increases in the compressor, typified by a decrease in compressor massflow, pressure ratio and isentropic efficiency, there is a corresponding increase in the vibration amplitude at the first fundamental frequency of the compressor.
Open Access
Economic analysis of a zero-carbon liquefied hydrogen tanker ship
(Elsevier, 2022-07-10) Alkhaledi, Abdullah N. F. N. R.; Sampath, Suresh; Pilidis, Pericles
The green hydrogen economy is considered one of the sustainable solutions to mitigate climate change. This study provides an economic analysis of a novel liquified hydrogen (LH2) tanker fuelled by hydrogen with a total capacity of ∼280,000 m3 of liquified hydrogen named ‘JAMILA’. An established economic method was applied to investigate the economic feasibility of the JAMILA ship as a contribution to the future zero-emission target. The systematic economic evaluation determined the net present value of the LH2 tanker, internal rate of return, payback period, and economic value added to support and encourage shipyards and the industrial sector in general. The results indicate that the implementation of the LH2 tanker ship can cover the capital cost of the ship within no more than 2.5 years, which represents 8.3% of the assumed 30-year operational life cycle of the project in the best maritime shipping prices conditions and 6 years in the worst-case shipping marine economic conditions. Therefore, the assessment of the economic results shows that the LH2 tankers may be a worthwhile contribution to the green hydrogen economy.
Open Access
Effect of working fluid on selection of gas turbine cycle configuration for Gen-IV nuclear power plant system
(JSME, 2019-05-31) Osigwe, Emmanuel O.; Gad-Briggs, Arnold; Pilidis, Pericles; Nikolaidis, Theoklis; Sampath, Suresh
The cycle configuration of the energy conversion system in a nuclear power plant tends to have a governing effect on the overall performance and acquisition cost. Interestingly, one factor that could greatly affect the design choice of the cycle configuration which may not have been explored extensively in many literatures reviewed is the choice of the working fluid. This paper presents a technical analysis on the effect of working fluid on selection of the cycle arrangement for a Generation IV nuclear power plant. It provides insight on potential performance gains that justifies the benefit for an additional cost of a complex cycle, and how the working fluid can influence this choice. The study identifies candidate working fluid that may be suitable for simple, inter-cooled-recuperated, recuperated and other complex cycles. The results obtained shows that for fluid like carbon dioxide, its optimal performance is achieved above it critical points which will require pressurizing the system or operating at high pressure ratio, hence, it would be suitable for a re-compressed inter-cooled cycle configuration. Similar, for fluid like helium with low molecular weight and high gas properties, the simple cycle configuration seem more realistic for its highest cycle efficiency of 41% and turbomachinery design.
Open Access
Examination of material variation on the life of gas turbine backing-up renewable energy sources
(International Organization of Scientific Research, 2019-02-28) Ali, Ezeddin; Sampath, Suresh; Pilidis, Pericles; Mohammed, Saleh
Gas turbine life and efficiency depend on the operating environment and material performance. Material selection is of prime importance to achieve high life and efficiency. This paper focuses on the study of the effect of material properties and variation in alloy composition ofa high-pressure turbine blade on gas turbine life when works in the flexible mode as a pick-up of renewable sources.A tool has been developed wherein different scenarios can be simulated to obtain engine life consumption factors. The engine life is examined according to the different material for different operating scenarios. It is observed that blade life is highly affected by changing material properties and moreover it is noted that the small change in the mass percentage of some constituent elements of an alloy results in a significant difference in HPT blade life.
Open Access
A fast-convergence algorithm for reliability analysis based on the AK-MCS
(Elsevier, 2021-04-16) Xiong, Yifang; Sampath, Suresh
In the field of reliability engineering, assessing the probability of failure of an event is usually a computationally demanding task. One way of tackling this issue is by metamodelling, in which the original computational-expensive model is approximated by a simpler metamodel. A method called AK-MCS for Active learning reliability method combining Kriging and Monte Carlo Simulation, was developed for the metamodel construction, and proved effective in reliability analysis. However, the performance of the AK-MCS algorithm is sensitive to the candidate size and the Kriging trend. Moreover, it cannot take advantage of parallel computing, a highly efficient way to speed up the simulation process. Focusing on the identified issues, this study proposes three methods to improve algorithm performance: the candidate size control method, multiple trends method, and weighted clustering method. These three methods are integrated into the AK-MCS structure, with their individual and combined performances being tested using four examples. Results suggest that all three methods contribute to the improvement of algorithm efficiency. When the three methods working together, the computational time is reduced significantly, and in the meantime, higher accuracy can be achieved.
Open Access
Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm
(Cranfield University, 2003-08) Sampath, Suresh; Singh, R.
The major challenges faced by the gas turbine industry, for both the users and the manufacturers, is the reduction in life cycle costs , as well as the safe and efficient running of gas turbines. In view of the above, it would be advantageous to have a diagnostics system capable of reliably detecting component faults (even though limited to gas path components) in a quantitative marmer. V This thesis presents the development an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using advanced concepts in genetic algorithm. The diagnostics model operates in three distinct stages. The rst 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 heuristics modification of genetics algorithm parameters through a master-slave type configuration. The third stage uses the elitist model concept in genetic algorithm to preserve the accuracy of the solution in the face of randomness. The above fault diagnostics model has been integrated with a nested neural network to form a hybrid diagnostics model. The nested neural network is employed as a pre- processor or lter 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 signicant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR2l engine has been used as the test engine for implementing the diagnostics model.
Open Access
GT-ACYSS: gas turbine arekret-cycle simulation modelling for training and educational purposes
(ASME, 2019-05-08) Osigwe, Emmanuel O.; Pilidis, Pericles; Nikolaidis, Theoklis; Sampath, Suresh
This paper presents the modelling approach of a multi-purpose simulation tool called GT-ACYSS; which can be utilized for simulation of steady-state and pseudo transient performance of closed-cycle gas turbine plants. The tool analyses the design point performance as a function of component design and performance map characteristics predicted based on multi-fluid map scaling technique. The off-design point is analyzed as a function of design point performance, plant control settings and a wide array of other off-design conditions. GT-ACYSS can be a useful educational tool since it allows the student to monitor gas path properties throughout the cycle without laborious calculations. It allows the user to have flexibility in selection of four different working fluids, and the ability to simulate various single-shaft closed-cycle configurations, as well as the ability to carry out preliminary component sizing of the plant. The modelling approach described in this paper has been verified with case studies and the trends shown appeared to be reasonable when compared with reference data in the open literature, hence, can be utilized to perform independent analyses of any referenced single-shaft closed-cycle gas turbine plants. The results of case studies presented herein demonstrated that the multi-fluid scaling method of components and the algorithm of the steady state analysis were in good agreement for predicting cycle performance parameters (such as efficiency, and output power) with mean deviations from referenced plant data ranging between 0.1% and 1% over wide array of operations.
Open Access
Helicopter gearbox bearing fault detection using separation techniques and envelope analysis
(IEEE, 2017-01-19) Zhou, L.; Duan, F.; Mba, David; Corsar, Michael; Greaves, Matthew J.; Sampath, Suresh; Elasha, Faris
The main gearbox (MGB) is a crucial part of a helicopter. MGB bearings suffer intensively from stress and friction during flights hence concerns for their health condition and detecting potential defects become critical for the sake of operation safety and system reliability. In this study, bearing defects were seeded in the second epicyclic stage bearing of a commercial Class A helicopter MGB. Vibration and tachometer signals were recorded simultaneously for the purpose of fault diagnosis. The tests were carried out at different power and speed conditions for various seeded bearing defects. This paper presents a comparison of signal processing techniques employed to identify the presence of the defects masked by strong background noise generated from an operation helicopter MGB.
Open Access
A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics
(Elsevier, 2022-12-23) Zhao, Junjie; Li, Yi-Guang; Sampath, Suresh
Future trends in engine health management (EHM) systems are information fusion, advanced analytical methods, and the concept of the Intelligent Engines. Machine Learning (ML)-based aero-engine gas path diagnostic methods are promising under the motivation of these trends. However, previous ML-based diagnostic structures are rarely applied in actual engineering practice because they are purely mathematical and lack physical insight or are limited by the error accumulation problem. Developing an accurate, flexible and interpretable intelligent diagnostic method has always posed a challenge, especially when physical knowledge is also available for more diagnostic information. Instead of modifying and applying existing ML methods for classification or regression, this study proposes a novel hierarchical diagnostic method to get insight into the physical systems, build hierarchies automatically, and recommend the classification structures. The proposed hierarchical diagnostic method is evaluated against a NASA model high-bypass two-spool turbofan engine. NASA's blind test case results show that Kappa Coefficient of the proposed hierarchical diagnostic method is 0.693 and is at least 0.008 higher than the other diagnostic methods in the open literature. It has been proved that the proposed method can quantify the dependence relationships between the fault classes for enhanced diagnostic information, recommend the best diagnostic structure for reduced complexity, and solve the error accumulation problem for improved diagnostic accuracy. The proposed method could support intelligent condition monitoring systems by effectively exploiting physical and data-based information for improved model interpretability, model flexibility, diagnostic visibility, diagnostic accuracy, and diagnostic reliability.
Open Access
A hydrogen fuelled LH2 tanker ship design
(Taylor and Francis, 2021-06-09) Alkhaledi, Abdullah N. F. N. R.; Sampath, Suresh; Pilidis, Pericles
This study provides a detailed philosophical view and evaluation of a viable design for a large liquid hydrogen tanker fuelled by liquid hydrogen. Established methods for determining tank sizing, ship stability, and ship characteristics were used to evaluate the preliminary design and performance of the liquefied hydrogen tanker named ‘JAMILA’, designed specifically to transport liquid hydrogen. JAMILA is designed around four large liquid hydrogen tanks with a total capacity of ∼280,000 m3 and uses the boil-off gas for propulsion for the loaded leg of the journey. The ship is 370 m long, 75 m wide, and draws 10.012 m at full load. It has a fully loaded displacement tonnage of 232,000 tonnes to carry 20,000 tonnes of hydrogen. Its propulsion system contains a combined-cycle gas turbine of approximately 50 MW. The volume of the hydrogen cargo pressurised to 0.5 MPa primarily determines the size and displacement of the ship.
Open Access
Improved gas turbine diagnostics towards an integrated prognostic approach wiht vibration and gas path analysis
(2017-01) Jombo, Gbanaibolou; Sampath, Suresh; Pilidis, Pericles
The degradation of a gas turbine engine in operation is inevitable, leading to losses in performance and eventually reduction in engine availability. Several methods like gas path analysis and vibration analysis have been developed to provide a means of identifying the onset of component degradation. Although both approaches have been applied individually with successes in identifying component faults; localizing complex faults and improving fault prediction confidence are some of the further benefits that can accrue from the integrated application of both techniques. Although, the link between gas path component faults and rotating mechanical component faults have been reported by several investigators, yet, gas path fault diagnostics and mechanical fault diagnostics are still treated as separated toolsets for gas turbine engine health monitoring. This research addresses this gap by laying a foundation for the integration of gas path analysis and vibration to monitor the effect of fouling in a gas turbine compressor. Previous work on the effect of compressor fouling on the gas turbine operation has been on estimating its impact on the gas turbine’s performance in terms of reduction in thermal efficiency and output power. Another methodology often used involves the determination of correlations to characterize the susceptibility and sensitivity of the gas turbine compressor to fouling. Although the above mentioned approaches are useful in determining the impact of compressor fouling on the gas turbine performance, they are limited in the sense that they are not capable of being used to access the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor. In this work, a Greitzer-type compression system model is applied to predict the flow field dynamics of the fouled compressor. The Moore-Greitzer model is a lumped parameter model of a compressor operating between an inlet and exit ii duct which discharges to a plenum with a throttle to control the flow through the compression system. In a nutshell, the overall methodology applied in this work involves the interaction of four different models, which are: Moore-Greitzer compression system model, Al-Nahwi aerodynamic force model, 2D transfer matrix rotordynamic model and a gas turbine performance engine model. The study carried out in this work shows that as the rate of fouling increases, typified by a decrease in compressor massflow, isentropic efficiency and pressure ratio, there is a corresponding increase in the vibration amplitude at the compressor rotor first fundamental frequency. Also demonstrated in this work, is the application of a Moore-Greitzer type compressor model for the prediction of the inception of unstable operation in a compressor due to fouling. In modelling the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor, linear simplifications have been adopted in the compression system model. A single term Fourier series has been used to approximate the resulting disturbed flow coefficient. This approximation is reasonable for weakly nonlinear systems such as compressor operating with either an incompressible inlet flow or low Mach number compressible inlet flow. To truly account for nonlinearity in the model, further recommendation for improvement includes using a second order or two-term Fourier series to approximate the disturbed flow coefficient. Further recommendation from this work include an extension of the rotordynamic analysis to include non-synchronous response of the rotor to an aerodynamic excitation and the application of the Greitzer type model for the prediction of the flow and pressure rise coefficient at the inlet of the compressor when fouled.
Open Access
An integrated combined methodology for the outline gas turbines performance-based diagnostics and signal failure isolation.
(Cranfield University, 2019-11) Togni, Simone; Theoklis, Nikolaidis; Sampath, Suresh
The target of this research is the performance-based diagnostics of a gas turbine for the online automated early detection of components malfunctions with the presence of measurements malfunctions. The research proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high rate of success for single and multiple failures with the presence of measurement malfunctions – measurement noise. A combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman Filter has in his strength the measurement failure treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile, the neuro-fuzzy logic the estimation precision, used for the quantification and the fuzzy logic the categorization flexibility, which are used to classify the components failure. All contributors are also a valid technique for online diagnostics, which is a key objective of the methodology. In the area of gas turbine diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This research investigates the key contribution of each component of the methodology and reaches a success rate for the component health estimation above 92.0% and a success rate for the failure type classification above 95.1%. The results are obtained with the first configuration, running with the reference random simulation of 203 points with different level of deterioration magnitude and different combinations of failures type. If a measurement noise 5 times higher than the nominal is considered, the component health estimation drop to a minimum of 70.1% (reference scheme 1) while the classification success rate remains above 88.9% (reference scheme 1). Moreover, the speed of the data processing – minimum 0.23 s / maximum 1.7 s per every single sample – proves the suitability of this methodology for online diagnostics. The methodology is extensively tested against components failure and measurement issues. The tests are repeated with constant simulations, random simulation and a deterioration schedule that is reproducing several months of engine operations.