Gas Turbine Performance Models Research Articles

Reduction of the Emission Footprint of Gas Turbines in Future Energy System Scenarios Through Optimized Hydrogen Admixture Strategies

Abstract Due to the inherent volatility of renewable power generation technologies, dispatchable components, such as gas turbines (GT) will have to be used increasingly for residual load balancing. In addition, GTs are expected to operate fewer hours per year, more flexibly, and at lower capacities. However, fuel utilization in GT is inherently linked to emissions. As a potentially CO2-free energy carrier, hydrogen is a promising fuel for GTs and manufacturers are working on suitable combustor technologies. However, the availability of large quantities of CO2-free H2 remains unclear in the near future. In this study, a physical-based gas turbine performance model and an emission calculation tool are used to derive an optimized H2 admixture strategy for different load profiles. Characteristic load demand scenarios are derived from actual load profiles of gas power plants and the emission footprints are comprehensively evaluated by different environmental impact categories. In general, the emission footprint is increased significantly and moderately for capacity reduction and flexibility increase of GT operation. The availability of H2 in the near future is derived from forecasts for Germany, and the corresponding quantities are allocated to the partial loads according to the optimized strategy. In most scenarios, the addition of H2 is associated with a reduction in emissions compared to conventional fossil fuel operation. The greatest leverage of H2 admixture in reducing the environmental footprint is found when applied from the lowest load up, thus assisting in the start-up and shut-down process.

A Deterministic Calibration Method for the Thermodynamic Model of Gas Turbines

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model’s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts.

Research on a Method for Online Damage Evaluation of Turbine Blades in a Gas Turbine Based on Operating Conditions

Performing online damage evaluation of blades subjected to complex cyclic loads based on the operating state of a gas turbine enables real-time reflection of a blade’s damage condition. This, in turn, facilitates the achievement of predictive maintenance objectives, enhancing the economic and operational stability of gas turbine operations. This study establishes a hybrid model for online damage evaluation of gas turbine blades based on their operational state. The model comprises a gas turbine performance model based on thermodynamic simulation, a component load calculation model based on a surrogate model, an updated cycle counting method based on four-point rainflow, and an improved damage mechanism evaluation model. In the new model, the use of a surrogate model for the estimation of blade loading information based on gas turbine operating parameters replaces the conventional physical modeling methods. This substitution enhances the accuracy of blade loading calculations while ensuring real-time performance. Additionally, the new model introduces an updated cycle counting method based on four-point rainflow and an improved damage mechanism evaluation model. In the temperature counting part, a characteristic stress that represents the stress information during the cyclic process is proposed. This inclusion allows for the consideration of the impact of stress fluctuations on creep damage, thereby enhancing the accuracy of the fatigue damage assessment. In the stress counting part, the model incorporates time information associated with each cycle. This concept is subsequently applied in determining the identified cyclic strain information, thereby improving the accuracy of the fatigue damage evaluation. Finally, this study applies the new model to an online damage evaluation of a turbine stationary blade using actual operating data from a micro gas turbine. The results obtained from the new model are compared with the EOH recommended by the OEM, validating the accuracy and applicability of the new model.

Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine

The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots.

Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling

An accurate gas turbine performance model is important for system performance evaluation. To minimize the simulated performance error, an adaption method using coefficients of scaling factors is applied to tune the component characteristic maps and make the gas turbine model meet measurements of a few randomly sampled points. However, the field data are non-homogeneously distributed. In this situation, randomly selecting a few sampling may lead to the inappropriate correction of the component characteristic maps and lower the prediction accuracy of model. Firstly, the coefficients of scaling factors are introduced to construct the performance adaption optimization problem of the gas turbine model. Secondly, the k-means clustering algorithm is applied to divide the 146 field data points into 10 different categories, and then the points closest to the cluster centers are selected to form the sampling set. Thirdly, a particle swarm optimization algorithm is used to search the optimal scaling factors. As a result, the model error decreases from 2.947% to 0.610%. Finally, the proposed method is validated with the remaining field data of a real E-class gas turbine. The average predicted error is 0.466%. Compared with the performance results obtained by random sample, the model based on cluster sampling shows a better accuracy.

Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine

Classification is an essential task for many applications, including text classification, image classification, data classification, and so on. The present study investigates the accuracy of different machine learning classification algorithms with three different data smoothing techniques for gas turbine fault detection and isolation task. The gas turbine performance model was developed by considering variable inlet guide vane and bleed air. Fouling and erosion were injected into all six main components of the gas turbine engine. Faulty and non-faulty data were generated from the developed performance model. Based on sensitivity analysis, 12 measurement parameters and 11,824 data points were selected for the development of a fault detection and isolation model. The faulty and non-faulty data were balanced, smoothed, corrected and normalized. Finally, the classification accuracy of the machine learning techniques was analyzed. The result shows that K-Nearest Neighbours, Neural Network and Decision Tree classifiers exhibited high classification accuracy, about 99% with all three data smoothing techniques. It is also observed that the computation time of Support Vector Machine is higher whereas K-Nearest Neighbours shows the lowest. Finally, the research proves that K-Nearest Neighbours is the best classification technique for gas turbine engine fault detection and isolation application.

A Compressor Off-Line Washing Schedule Optimization Method With a LSTM Deep Learning Model Predicting the Fouling Trend

Abstract Compressor fouling is one of the most prevalent fault modes that contribute to the performance degradation of a gas turbine power plant. Off-line washing is a standard maintenance procedure to recover the fouling degradation, but with washing cost. In this paper, an off-line washing schedule optimization method with a long short-term memory (LSTM) prediction model is proposed to maximize the plant net profit. First, a mechanism model-based gas path analysis method is developed to identify the fouling indications of compressor flow rate degradation (DGC) and compressor efficiency degradation (DEC). Second, a sliding window prediction method based on LSTM is proposed to accurately predict the nonlinear fouling trends. The prediction models are trained and tested by the true trends of the DGC and DEC that are identified from the field data of a real gas turbine power plant. The comparison results prove that the LSTM algorithm outperforms other machine learning algorithms. The mean relative square error of the DGC LSTM model is 9.72 × 10−4, and DEC is 4.08 × 10−4. Finally, a detailed economic model is developed by coupling the fouling prediction model with the gas turbine performance model. On this basis, an optimization method of the washing schedule is developed to maximize the net profit. Two case studies, under full load and field data, are carried out to verify the proposed optimization method. The results show that the washing schedules of the two case studies are much similar, in which three washing tasks with gradually reduced intervals are provided. Furthermore, the comparison results of different schedules show that the proposed optimal schedule has a huge potential in saving the net profit. It can save 3.26 million Yuan compared with the practical schedule adopted by the real power plant.

Generating axial compressor maps to zero speed

Gas turbine performance models typically rely on component maps to characterize engine component performance throughout the operational regime. For the sub-idle case, the lack of reliable rig test data or inability to run design codes far from design conditions entails that component maps have to be generated from the extrapolation of existing data at higher speeds. This undermines the accuracy of whole-engine sub-idle performance models, at times impacting engine development and certification of aviation engines and the accuracy of start-up performance prediction in industrial gas turbines. One of the main components driving this issue is the core compression system, which can present operability concerns during light-up and which also sets the combustor airflow required for ignition. This paper presents, discusses, and draws on previous approaches to describe a method enabling the creation of sub-idle compressor maps from analytical and physical grounds. The method relies on the calculation of zero-speed and torque-free lines to generate a map down to zero speed along with analytical interpolation. A method for the interpolation process is described. A sensitivity study is carried out to assess the effects that different elements of the map generation process may have on the accuracy of the resulting performance calculation. Overall, a method for the generation of accurate, consistent maps from limited geometry data is identified.

PZL-10 Turboshaft Engine–System Design Review

Abstract The PZL – 10-turboshaft gas turbine engine is straight derivative of GTD-10 turboshaft design by OKMB (Omsk Engine Design Bureau). Prototype engine first run take place in 1968. Selected engine is interested platform to modify due gas generator layout 6A+R-2, which is modern. For example axial compressor design from successful Klimov designs TB2-117 (10A-2-2) or TB3-117 (12A-2-2) become obsolete in favour to TB7-117B (5A+R-2-2). In comparison to competitive engines: Klimov TB3-117 (1974 – Mi-14/17/24), General Electric T-700 (1970 – UH60/AH64), Turbomeca Makila (1976 – II225M) the PZL-10 engine design is limited by asymmetric power turbine design layout. This layout is common to early turboshaft design such as Soloview D-25V (Mil-6 power plant). Presented article review base engine configuration (6A+R+2+1). Proposed modifications are divided into different variants in terms of design complexity. Simplest variant is limited to increase turbine inlet temperature (TIT) by safe margin. Advanced configuration replace engine layout to 5A+R+2-2 and increase engine compressor pressure ratio to 9.4:1. Upgraded configuration after modification offers increase of generated power by 28% and SFC reduction by 9% – validated by gas turbine performance model. Design proposal corresponds to a major trend of increasing available power for helicopter engines – Mi-8T to Mi-8MT – 46%, H225M – Makila 1A to 1A2 — 9%), Makila 1A2 to Makila 2-25%.

Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

ABSTRACTOne 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.

Development of Easy Maintenance Assistance Solution (EMAS) for Gas Turbine

The solution was developed for the maintenance decision support of combined cycle power plant gas turbine. The developed solution provides the calculated result of optimal overhaul interval through the following modules: Overhaul Interval Prediction, Real Time Performance Monitoring, Model-Based Diagnostics, Performance Trend Analysis, Compressor Washing Period Management, and Blade Path Temperature Analysis. Model-Based Diagnostics module analyzed the differences between the data of MHI501G gas turbine performance model and the online measurement. Gas turbine performance model can be modified by the type of gas turbine of each combined cycle power plant. Compressor washing management module suggests the optimal point of balancing between the compressor performance and the maintenance cost. The predicted results of compressor washing period and overhaul period are able to support the operators in combined cycle power plant to make a proper decision of maintenance task. The developed solution was applied to MHI501G gas turbine and is, in present, on the process of field test at Gunsan combined cycle power plant, South Korea.

A new component map generation method for gas turbine adaptation performance simulation

The accuracy of component maps significantly affects gas turbine performance simulation results. Unfortunately, the information in component maps is usually insufficient to performance simulation. In this paper, a new compressor map generation method is presented with the primary objective of improving the accuracy of the gas turbine performance simulation model under insufficient information. In this method, the compressor map is first generated as an initial map according to the operating points determined by a set of steady-state operating data, and the coefficients that determine the shape of the compressor map are analyzed and tuned through a multi-objective optimization scheme to match multiple sets of transient data. To verify the validity of the new method, an LM2500 compressor map from the public literature was generated and the results were compared with those of existing fitting methods. Furthermore, the new map generation method, developed in Simulink, was implemented in a dynamic gas turbine model and tested in off-design steady-state and transient conditions. The results showed that the method is able to overcome the shortcomings of the existing methods and to effectively improve the accuracy of the gas turbine performance model under insufficient information.

Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

Overview of coupling noise prediction for turbofans with engine and aircraft performance

In this work, a comparison of the noise levels of different aero-engines is presented. The coupling is based on integrating a gas turbine design and performance model with an aircraft model for long and short hauls and a noise model made of established empirical methods available in the open literature for turbofan noise prediction at design and off-design points. After joining together the performance models with the noise methods, the authors have studied three ultra high bypass ratio engine configurations vis-à-vis their baseline counterpart of equivalent thrust rating. Also, the effect of application of passive noise attenuators (liners) to reduce the noise is explored. The results obtained showed that though there are derivable benefits from the ultra high bypass ratio turbofans, such as reduction in fuel consumption, in jet noise and reduction of broadband noise for the fan, the new engines generate higher noise at lower frequencies in the fan.

High-bypass turbofan model using a fan radial-profile performance map

Abstract Accurate gas turbine performance models based on thermodynamic principles can bring many benefits. In such models it is a normal practice to represent the different components by means of its performance maps or component characteristics describing the relationships between the thermodynamic variables representing the component. It is well known that the fans of high-bypass engines have strong radial profiles of all thermodynamic variables. It is common to average these profiles so that one or two characteristic maps can represent the fan. The present paper describes how the radial profiles can be used to make an estimation of turbofan performance. The results are somewhat different to those produced using a component performance maps.

A Computer Model as an Educational Tool for Gas Turbine Performance

The use of gas turbine performance models for educational purposes is discussed and a particular instance of such a model is presented. The model facilitates the presentation of the basic rules which govern gas turbine engine behaviour and helps in the understanding of different aspects of its operation. It can graphically present engine operating point data in a number of different ways: operating line, pressure and temperature distributions along the gas path, operating points of the components, variation of particular quantities with operating conditions etc. Apart from operating as an engine simulator, the model can also be used to demonstrate the principles of component matching and the behaviour of numerical algorithms for building an engine model. Finally, it facilitates familiarization with the principles of gas turbine fault diagnosis, by the incorporation of some diagnostic possibilities, again providing information in a graphical manner.

A Simple Gas Turbine Performance Model for Various Types of Combined Cycle Power Plants. Calibration of the model by the simple cycle data.

Recently, gas turbines have been used as key components of a variety of combined cycle power plants including integrated coal gasification combined cycle and humid air turbine etc., and their characteristics strongly influence the total performance of these power plants. The purpose of this article is to demonstrate the usefulness of a relatively simple model of gas turbine performance, which is commonly applicable to modern air-cooled high temperature gas turbines. Gas turbine performance in such combined cycles can be estimated with the model calibrated by the simple cycle data of the turbine.