Gas Turbine Performance Research Articles

Effects of the multicavity tip coolant injection on the blade tip and the over tip casing aerothermal performance in a high-pressure turbine cascade

In high performance gas turbine engines, the over tip leakage flow driven by the lateral pressure gradient is inevitably induced inside the blade tip gap in the high-pressure stage turbine due to the freestanding airfoil design methodology. To obtain an increasing level of thermal efficiency, the turbine inlet temperature is gradually increased in terms of the Brayton cycle. Hence, the blade tip and the over tip casing are subjected to high thermal load. In real turbine blade, cavity tips are widely used to decrease the over tip leakage flow and the thermal load on the blade tip and over tip casing. In the present study, the numerical simulations were conducted to investigate the effects of the multicavity coolant injection on the blade tip and the over tip casing aerothermal performance. Three-dimensional (3-D) Reynolds-averaged Navier–Stokes (RANS) equations and standard turbulence model were solved together in the simulations. The results indicate the ribs inside the tip cavity alter the distribution of film cooling efficiency by changing flow structure within the tip gap. Most of the coolant is limited in each little cavity owing to the blockage of ribs. Here, the swirling action of each cavity vortex results in the coolant providing a wider film cooling coverage. Therefore, the increase in film cooling effectiveness on the blade tip surface is more efficient than that of over-tip casing. The blade with four tip cavities film cooling (4CFC) obtains the largest area-averaged film cooling effectiveness, which is augmented by 14.3% in comparison with the case with a single tip cavity film cooling (1CFC).

Applicability of Gas Turbine Traction in High-Speed Rail Projects in Russia

Recently, the issue of advisability of building dedicated highspeed railways (HSR) in Russia for transportation of passengers and goods has often been raised in the scientific and industry environment. The key risk of such large-scale investment projects is a significant or rather long payback period due to the lower population density in the areas of the proposed HSR compared to, for example, China. In addition, when planning such capitalintensive and resource-intensive investments, it is necessary to consider plans for development of other modes of transport, namely, express highways, air traffic, as direct competitors to speed and high-speed railways.A way to increase the competitiveness of HSR may be to reduce capital costs during the construction. The creation of HSR, where multiple-unit trains powered by gas turbine engines (GTE) and using AC-AC electric drive, will allow to renounce investments in expensive design, construction, and subsequent maintenance of energy facilities, specialised HSR catenary, which will ensure reduction in the cost of HSR projects, in construction time, and accelerated payback of railways.The article describes the advantages of operating gas turbine traction on speed and high-speed railway lines. The possible structure and layout of such trains are shown. The risks of operation of rolling stock powered by GTE are considered as well as the ways to neutralise them. The objective of the study was to identify the comparative advantages of multiple unit trains powered by GTE compared to high-speed electric trains. The study used methods of comparative analysis, content analysis of technical information, and ranking. It is concluded that introduction of GTE will reduce the investment and operating costs of speed and high-speed railways while maintaining the power and dynamic characteristics of trains.

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

Abstract As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. The results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

Turbine Vane Endwall Film Cooling and Pressure Side Phantom Cooling Performances With Upstream Coolant Flow at Various Injection Angles

Abstract In this paper, the endwall film cooling and vane pressure side surface phantom cooling performances of a first nozzle guide vane (NGV) with endwall contouring, similar to an industry gas turbine, were experimentally and numerically evaluated at the simulated realistic gas turbine operating conditions (high inlet freestream turbulence level of 16%, exit Mach number of 0.85, and exit Reynolds number of 1.7 × 106). A novel numerical method for the predictions of adiabatic wall film cooling effectiveness was proposed based on a double coolant temperature model. The credibility and accuracy of this numerical method were validated by comparing the predicted results with experimental data. Results indicate that the present numerical method can accurately predict endwall film cooling performance and vane surface phantom cooling performance for both the ideal low density ratio (DR = 1.2) and the typical high density ratio (DR = 2.0) conditions. The endwall film cooling effectiveness, vane surface phantom cooling effectiveness, and secondary flow field were compared and analyzed for the axisymmetric convergent contoured endwall at three coolant injection angles (small injection angle of θ = 40 deg, design injection angle of θ = 50 deg, large injection angle of θ = 60 deg), two density ratios (low density ratio of DR = 1.2 and typical high density ratio of DR = 2.0), and the design blowing ratio (M = 2.5), based on the commercial CFD solver ansys fluent. An analysis method of the coolant momentum flux φ (decomposed into the axial component φx and vertical component φz) was proposed to describe and explain the migration and mixing mechanisms of coolant flow. Results indicate that the proposed analysis method of the coolant momentum flux φ can accurately and reliably describe and explain the coolant flow physics, including the coolant lift-off, the interaction between the coolant and the mainstream, and the coolant migration in the vane passage. The increased coolant injection angles result in a deterioration of the endwall film cooling performance and vane pressure surface side phantom cooling performance. Nevertheless, the sensitivity of endwall film cooling effectiveness and phantom cooling effectiveness to coolant injection angles is variable, and is significantly affected by density ratio. This suggests that the coupling effects of the coolant injection angles and density ratio should be taken into account for the prediction of endwall film cooling and phantom cooling performances. It is very necessary for the optimized design of the coolant injection angles and the predictions of film cooling performance at the realistic density ratio.

Multi-objective control of isolated power systems under different uncertainty approaches

This paper proposes and compares a set of multi-objective supervisory controllers for an isolated power system including a gas turbine operating in load following mode as a dominant source of generation, a battery energy storage system, and stochastic renewable generation. It analyzes their capability to coordinate the gas turbine and energy storage to provide isochronous speed control, achieving fast frequency regulation for the local grid, while the gas turbine can still operate with minimum deviation from its optimal loading and the storage system can follow a pre-scheduled reference state of charge trajectory. In more detail, we consider and compare different control strategies to handle model and disturbance uncertainties, including stochastic model predictive control under various parametrizations of the scenario approach, and robust control under the H∞ paradigm. The various controllers are compared against a benchmark, i.e., a deterministic predictive control strategy. Instead of point-to-point comparisons for some arbitrary cases (i.e., worst-case or expected), empirical distributions of the controller’s performance for the whole probability spectrum are derived, leading to more accurate and representative comparisons. The analyzed performance indicators are nominal dynamic performance, constraint violation probabilities, and expected system operation performance. The results indicate the clear superiority of stochastic control over both robust and deterministic control in dealing with both parametric and disturbance uncertainty. Moreover, choosing an appropriate parametrization is shown to be essential to achieve both good nominal performance and lower violations probability, indicating that the superiority of stochastic control comes with the drawback of needing user-defined tuning from the designer.

Phase composition and thermophysical properties of Yb–Y Co-doped SrZrO3 coating prepared by suspension plasma spray

In this study, the Yb–Y co-doped SrZrO3 (SZ) coatings [SZYY-1 (Sr1.0(Zr0.9Yb0.05Y0.05)O2.95), SZYY-2 (Sr0.9(Zr0.9Yb0.05Y0.05)O2.85), and SZYY-3 (Sr0.8(Zr0.9Yb0.05Y0.05)O2.75)] were deposited by SPS (Suspension plasma spray) using the corresponding co-precipitation prepared precursors suspension. In the three as-sprayed SZYY coatings, there were some typical suspension plasma-sprayed coating structures, such as columnar crystals, vertical cracks, and micro/nanopores.The TCs (Thermal conductivities) of the three as-sprayed SZYY coatings were 1.23–1.88, 0.80–1.41, and 0.88–1.43 W/(m · K) at 100–1400 °C, respectively, which were at least 19% lower than that of the SZ coating (1.25 W/(m · K), 1000 °C). The TECs (Thermal expansion coefficients) of the three as-sprayed SZYY coatings were (7.8–8.7) × 10−6, (8.0–9.8) × 10−6, and (8.1–9.8) × 10−6/K, respectively (200–1360 °C). The sintering coefficients of the three as-sprayed SZYY coatings were 5.69 × 10−6, 4.29 × 10−6, and 3.84 × 10−6/s, respectively. The SZYY-3 coating showed the best SR (Sintering resistance), followed by the SZYY-2 coating and SZYY-1 coating. The SPS SZYY-2 coating with good phase stability, low TC (Thermal conductivity) and good SR is a promising new TBCs (Thermal barrier coatings) material. It is expected to be an alternative TBCs material for the next generation of high performance gas turbines.

Effects of membrane processed renewable biogas fuels on natural gas designed turbine's power cycle and fuel consumption

This research analyzed and explored two different configurations of membranes, carbon dioxide selective and methane selective membranes, to produce processed biofuels (Fuel 2 and 3) from crude biogas plant (Fuel 1) for the first time. In addition, the effect of crude and processed fuels on gas turbine performance was researched in order to assess the viability of the biogas as a renewable resource to generate electrical power. Under optimum conditions that maximized product purity and recovery while keeping the operating parameters at a minimum level, carbon dioxide selective membrane produced methane purity of 87% and recovery of 90% (Fuel 2) at selectivity of 1084, stage cut of 0.31, and feed pressure of 32 bar. Methane selective membrane with selectivity of 746, stage cut of 0.65, and feed pressure of 35 bar produced processed biogas with higher methane purity of 95% and recovery of 92% (Fuel 3) than those from the carbon dioxide selective membrane. The processed fuel also was found to be the most compatible when used in the gas turbine. This can be noticed in the gas turbine graphs, as the processed fuels were found to nearly replicate the gas turbine working in design conditions with natural gas. Although the crude biogas (Fuel 1) containing 69% methane, 27% carbon dioxide and 4% water worked with the gas turbine with the highest efficiency, there were issues related to the highest fuel consumption and aerodynamic-related aspects. The upgraded biogases (Fuel 2 and 3) containing 87% and 95% methane, 9% and 5% carbon dioxide, 4% and 0% water, respectively on the other hand, provided an almost similar gas turbine performance with the natural gas. The use of the upgraded biogases in the gas turbine are important to ensure that the gas turbine works well as it is intended to perform.

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.

Stresses within rare-earth doped yttria-stabilized zirconia thermal barrier coatings from in-situ synchrotron X-ray diffraction at high temperatures

There is a growing interest for smart coatings that can be integrated into turbine engines for in-situ temperature measurements or health monitoring. The addition of rare-earth dopants into standard thermal barrier ceramic top coat materials is used to obtain luminescent coatings that enable spectral measurements, for real-time temperature or health monitoring. The thermomechanical performance and durability of such novel coating compositions in extreme environments still remains to be evaluated. Consequently, the ability to manufacture sensor coatings which present suitable thermal properties needs to be demonstrated. For this study, highly luminescent erbium and europium doped yttria-stabilized zirconia and state-of-the-art yttria-stabilized zirconia coatings manufactured by air plasma spray were characterized to determine the effects of the embedded rare-earth dopants on coating internal strain and stress and to quantify and compare their high temperature response using synchrotron X-ray diffraction. In-situ depth-resolved strain measurements were performed at 15 μm intervals along the depth of the coatings to evaluate materials response at key locations, specifically at layer interfaces. In-plane stress was calculated for the coatings and a finite element model was implemented to supplement the results and enable further predictions. The results show that the sensor coatings that were manufactured in this work revealed only minor variations in the strain response of sensor coatings under a typical thermal cycle and especially at temperatures closer to that of gas turbine operating conditions, compared to state-of-the-art coatings. This work demonstrates the viability of manufacturing rare-earth doped yttria-stabilized zirconia coatings that provide beneficial spectroscopic monitoring capabilities while having minimal impact on the thermomechanical response of the thermal barrier coatings.

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.

Numerical study of effects of solid particle erosion on compressor and engine performance

Solid particle erosion (SPE) is a major source of damage to, and cause of failure of, turbomachinery, including centrifugal and axial compressors and gas turbines. There is considerable interest within the turbomachinery industry to develop the means to enhance durability and improve prediction of likely breakdowns of machinery operating in locations and conditions where ingestion of, for example, dust and sand grains is unavoidable during operation. The present study examines the implications of degradation of the compressor on gas turbine cycle performance, caused by operating in eroding environments, linking compressor aerodynamics with the thermodynamic cycle. The research engine employed in the study comprised a fan, bypass, and two stages of the low-pressure compressor (booster). A numerical simulation using Computational Fluid Dynamics (CFD) software was performed to predict the likely erosion patterns due to solid particles (SPs) impacting on the blades of the first two stages of an axial compressor. This study introduces a method that provides the freedom to apply roughness to a particular region of the blade following the SPE patterns identified as part of the CFD simulations so as to represent the effects of the erosion spots on the overall flow field. The localised roughness method employs a perturbation of the geometry of the blade in the regions corresponding to the location predicted by the SPE model. The intensity of the perturbation, which is governed by a “fraction factor” coded in a Matlab script, was calibrated by reference to a roughness case, applied over the entire blade surface, with equivalent sand-grain roughness (ks) of 60 μm. Turbomatch, the Cranfield in-house gas turbine performance simulation software, was employed to model the degradation of engine performance. The research confirmed that SPE is linked with significant decreases in engine performance parameters such as isentropic efficiency (ηis) and pressure ratio (PR). These quantities showed, for the SPE cases considered, a drop of 8.8% and 5.2%, respectively. These findings are useful to link local SPE events to global GTE cycle performance.

Research on combustion performance of a micro-mixing combustor for methane-fueled gas turbine

This research proposes an H-class micro-mixing gas turbine combustor prototype with a thermal power of 1.15 MW, which employs 40 exterior jet-in-crossflow micro-mixing nozzles with a tapered round outlet to reduce pollutant emissions and prevent the flashback. These nozzles are located by a distribution arrangement in order to improve the temperature distribution uniformity of field flow. To respond to off-design operation of gas turbine, the combustion performance of the micro-mixing combustor prototype is investigated numerically at the rated fuel temperature of 473 K and other ones of 373, 423, 523 and 573 K. The results indicate that NO emission has a relatively high augment as the fuel temperature decreases, which is due to the rising local equivalence ratio. The variation trends of the temperature distribution uniformity, combustion efficiency and total pressure loss are not obvious as the fuel temperature changes. Based on the rated and off-design combustion performance at different radial planes, this research finally proposes an optimal relationship between the axial liner length and micro-mixing nozzle outlet diameter.

Thermodynamic analysis of gas turbine performance using the enthalpy–entropy approach

The enthalpy–entropy approach, which is a rigorous and accurate method, was used to model the actual cycle of a gas turbine (GT). The constructed GT C# programming language code was used to study the effects of ambient temperature ( T am ), relative humidity (RH), and ambient pressure ( P am ) on GT output parameters. The results of the GT modeling program showed that the maximum (minimum) difference in GT output power ( P GT ) was 1.12% (0.01%) at a T am of 39.7 °C (23.3 °C) compared with the actual output power of El Atf power station. As T am increased from 14 °C to 40 °C, the P GT decreased by 19.01% (18.38%) of nominal GT power (250 MW) at 15% (95%) RH while GT thermal efficiency ( η th ) decreased by 2.3058% (2.4431%) and the specific fuel consumption (SFC) increased by 7.12% (7.59%). The P GT decreased by 0.1497% and 0.7674% with an RH increase from 15% to 95%, respectively. The P GT decreased by 0.03675 and 0.16835 MW while η th decreased by 0.0047% and 0.0243% at T am values of 15 and 40 °C, respectively, when P am was decreased from 1.013 to 0.990 bar. Using the compressor assumptions of each stage increases the humid air pressure by the same value and the efficiency of each stage is the same and equal to the compressor overall efficiency is more logical and leads to accurate results. The compressor isentropic efficiency was not affected by the bleeding air percent according to the assumptions used.

Brush seal design and secondary air system modification of a heavy-duty gas turbine to improve the output power

The overall performance of heavy-duty gas turbines is strongly affected by the mass flow distribution of the secondary air system. Reducing the mass flow of the secondary air is a remarkable method to improve the performance of a gas turbine. The aim of this article is to implement a brush seal as an alternative to labyrinth seal. Based on the experiences gained from the integrity of brush seals into a gas turbine, installing a brush seal would not necessarily improve turbine performance. This is because of SAS mass flow redistribution, which may not lead to overall SAS mass flow reduction. Therefore, some other components in the SAS arrangement should be modified as controlling variables to overcome this problem. The effect of these modifications on the airflow distribution is investigated using an in-house network analysis code. To establish an optimum solution (optimized brush seal geometry and controlling variables), the network analysis code is coupled with an in-house optimization code that benefits from the Genetic Algorithms and Artificial Neural Networks. Some constraints including upstream and downstream cavity purge flow and vane cooling air are considered in the optimization process. The final network results show a 33.27% reduction in overall SAS mass flow. To ensure an improvement in the performance of the new three-stage turbine, a CFD analysis is conducted, which indicates 1.0% more power with respect to the original turbine. In the end, the aerodynamic and mechanical behavior of the brush seal is analyzed using CFD and FEM.

Experimental and numerical study of fogging cooling performance through a cylindrical duct for a micro gas turbine

Fogging cooling is one of the most effective methods for cooling gas turbine inlet air in hot areas. Most of the previous studies focused only on the influence of the fogging cooling effect on the gas turbine performance. Nevertheless, due to the limited space available for installing a system, the fogging system requires special attention, notably in micro gas turbines, to avoid the thermal stress and blade erosion risks at a compressor intake. Hence, in the current paper, the performance of the fogging cooling system for micro gas turbines has been investigated experimentally and numerically to enhance the cooling efficiency, temperature stability and accomplish complete droplets evaporation. An experimental setup has been designed and installed to study the fogging cooling effect through a cylindrical duct. Eulerian-Lagrangian CFD model was also adopted to simulate the trajectories of the injected droplets and to predict the evaporation cooling process through the spray duct. The CFD model was validated with the current experiments, and there is good agreement between the experimental data and simulation results with an average deviation of less than 6.4%. The droplets' size and injected mass flow were measured at different nozzles orifice diameters and injection pressures to select the appropriate spray nozzles at various test conditions. The fogging performance has been tested under various operating conditions, including nozzle arrangement and flow directions, to identify the crucial factors for obtaining fully saturated air and stable temperature from the spray duct. The results reveal that employing a single nozzle to achieve full saturation is ineffective. On the other hand, increasing the number of nozzles with the same input water flow rate improves droplets distribution across the duct and enhances the evaporation rate. It was found that the temperature drop can be reduced by 1.5 °C due to dividing the required water mass flow on four nozzles instead of one. Furthermore, it was observed that the cooling performance of the counter-current flow case could be improved by 3.4% compared to the co-flow one with a high uniform temperature profile through the duct. Finally, a counter-current spray design with four nozzles is proposed for micro gas turbines since it meets the design constraints best compared to other operating scenarios.

Comparative evaluation of Integrated Solar combined cycle plant with cascade thermal storage system for different heat transfer fluids

The existing conventional combined cycle power plant(s) are generally not getting the schedules for supply of power because of the high price of Natural Gas and are forced to remain idle or to run on part load. On the other side, the focus is on the capacity addition of power generation units based on the alternate sources mainly solar to counter the ill effects on environment through fossils fuel-based plant(s). In such a scenario, the combined cycle power plant(s) with its integration with solar (ISCC) can pave the way forward to provide an optimal solution to tap most of the power generation through solar power and to ensure grid stability through utilising the installed combined cycle power plant(s) at part or full load. It will be a win-win situation for solar plants as well as combined cycle power plants. This study presents an integration of Linear Fresnel collector solar system and latent heat cascade storage system with a 328.10 MW combined cycle power plant of north India for operating it as integrated solar combined cycle (ISCC). The present work discusses the configurations of integration of solar power generation cycle with the combined cycle power generation and presents the analysis of the performance of these integration configurations including the operation of gas turbine(s) at full load or part load. A MATLAB code is used for simulation to analyse the performance of ISCC by varying direct normal irradiation at different part-load condition of gas turbine with different heat transfer fluids such as water and molten salt, then also compare the performance of latent heat cascade thermal storage system with oil & molten salt as HTF. The effect of cascade thermal storage has also been analysed on the plant performance. The efficiency of ISCC with molten salt as HTF is 50.87% which is more than with water as HTF by 1.51%. The present study also brings out the saving of the fuel to figure out the financial benefit. However, the financial model has not been developed in the present work due to the dynamics of the price of natural gas internationally. The study also presents the analysis of the enhanced fuel-saving ratio and reduced emissions.

Thermodynamics of CMC bladed marine gas turbine-LM 2500: Effect of cycle operating parameters

Recent developments in ceramic-matrix composites and their successful use in combustor liners and shrouds have generated interest among researchers to adopt these materials in rotating gas turbine blades, especially in the first stages of high-pressure turbines where gas temperatures are highest. CMC blades have the potential of being retrofitted to replace superalloy turbine blades in operating gas turbines. In this paper, a comparative study on the thermodynamic performance of a marine gas turbine engine, LM 2500, featuring directionally solidified nickel superalloy blades versus novel CMC blades in the high-pressure turbine sections has been reported. Mathematical modeling of the gas turbine cycle components has been developed and then coded in C++ language. The effects of turbine inlet temperature on thermodynamic efficiencies, coolant mass flow rates, and work ratios on the two systems have been analyzed. Finally, the exergy analysis of the systems’ components has been done to identify the benefits of adopting CMC blades in the LM 2500 system. It has been observed that when compared to the directionally solidified bladed turbine system, the projected first law efficiency of CMC bladed LM 2500 gas turbines can be enhanced over 7% (from 34.17% to 41.21%). The projected work ratio can be improved by over 16% (from 0.49 to 0.57) at the turbine inlet temperature of 1725 K.

Experimental and Numerical Study on the Effect of Annular Combustor Design on Thermal Uniformity Jet in Crossflow

Abstract Non-uniformity of the exit flow temperature represents one of the significant damages to gas-turbine components, particularly turbine blades. This may occur in the course of gas-turbine operation. This paper aims to provide passive techniques by modifying the combustor design rather than changing the flow parameters to improve the thermal uniformity and turbine blades to reduce thermal stresses and increase turbine blades’ life span. An acceptable agreement between the numerical and experimental results has been achieved, and the agreement includes the velocity and temperature profile. Four different angles have been tested numerically and experimentally with a maximum error of 5% at two different Reynolds numbers. Designing the outer combustor surface with a 45-deg angle bend can give a more uniform temperature distribution of 37% higher than the basic design with only a 0.5% higher pressure drop.

Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method

In this study, artificial neural network (ANN) based on operating data is adjusted to detect fault operation of large-scale industrial gas turbine. To detect faults, the operating characteristics of gas turbine are first predicted for fuel injection characteristics because, in general, lots of the operating failures are occurred in start-up procedure when fuel distribution characteristics are frequently changed. Data from the GE 7FA gas turbine were used, and the fuel distribution and other operating parameters are set by input and output parameters, respectively, to analysis effect of fuel distribution. The start-up procedure is divided with eight operating process (OP) to detect fault operation by fuel distribution characteristics. An overheating start is detected when turbine exhaust temperature (TET) reaches 110%–120% of the design value with the conventional detecting method; by contrast, the proposed method can detect this problem in advance before OP4. Further, sensitivity analysis of gas turbine operating characteristic parameters for each nozzle was performed to detect not only operation failures but also problems in the fuel supply system. Fault detectability was different with each OP and nozzle, and nozzle system abnormalities can be detected in advance by the results of sensitivity analysis.

Optimal Classifier to Detect Unit of Measure Inconsistency in Gas Turbine Sensors

Label noise is a harmful issue that arises when data are erroneously labeled. Several label noise issues can occur but, among them, unit of measure inconsistencies (UMIs) are inexplicably neglected in the literature. Despite its relevance, a general and automated approach for UMI detection suitable to gas turbines (GTs) has not been developed yet; as a result, GT diagnosis, prognosis, and control may be challenged since collected data may not reflect the actual operation. To fill this gap, this paper investigates the capability of three supervised machine learning classifiers, i.e., Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors, that are tested by means of challenging analyses to infer general guidelines for UMI detection. Classification accuracy and posterior probability of each classifier is evaluated by means of an experimental dataset derived from a large fleet of Siemens gas turbines in operation. Results reveal that Naïve Bayes is the optimal classifier for UMI detection, since 88.5% of data are correctly labeled with 84% of posterior probability when experimental UMIs affect the dataset. In addition, Naïve Bayes proved to be the most robust classifier also if the rate of UMIs increases.