In the present work URANS simulations are presented to describe the unsteady interaction process between the flow ingested/ejected from a cavity system and the main flow evolving into a Low Pressure Turbine stage. Particular care is posed on the analysis of the loss generation mechanisms acting outside the stator row and in the rear part of the axial gap separating the cavity flow ejection section and the leading edge plane of the downstream rotor row. The simulated geometry reproduces a typical engine cavity configuration, with upstream and downstream rotor rows reproduced by means of moving bars. Experimental results have been used to validate the simulations. This experimental data cannot explain and quantify alone the overall interaction process between the cavity flows and the main flow. The results of a simulation made by removing the domain of the cavity have been employed in order to better highlight and quantify the effects due to main flow and cavity flows interaction on total pressure loss. A deep inspection of the loss amount along the axial direction makes evident that losses generated in the vane row are basically increased prior to enter into the downstream rotor bars, due to cavity main flow interaction.

The uncoupled phase-shifted single-passage simulation is commonly used for turbomachinery aeroelastic problems. However, it has difficulties in dealing with unconventional phenomena such as strong fluid-structure interaction effects as well as blade mistuning effects. Regarding mistuning effects, structural mistuning has been studied extensively while aerodynamic mistuning has received far less attention. There seems to be a lack of clear and systematic understanding of physical behaviour and mechanisms of mistuned bladerows, particularly in the context of the aerodynamic mistuning versus structural one. In the present work, direct fully-coupled method is adopted to investigate the dynamics mechanism of a mistuned oscillating cascade. Both structurally and aerodynamically mistuned cascades show that the blades would couple and oscillate at a unique frequency and a constant inter-blade phase angle regardless of the individual blade’s eigen-frequency. The vibration amplitudes of blades of a mistuned row are different when excited. For structural mistuning, the mode localization effect is seen to be responsible for a monotonic increase of cascade aeroelastic stability with mistuning. On the other hand, the aerodynamically mistuned cascade shows a stabilizing effect at small amount of mistuning but exhibits a destabilizing effect at large mistuning. Such non-monotonic tendency could be explained using the aero-damping decomposition by the influence coefficient approach. At low reduced frequency, there is a striking difference between the tuned and aero-mistuned cascade. Although the tuned cascade is stable, the aero-mistuned cascade may experience flutter. A close inspection of the aero-mistuned cascade flutter reveals that there are two oscillating waves forming a beating signal.

The Paper presents a novel computationally efficient physics based framework for continuous assessment of cyclic and time dependent damage consumption for Siemens Aeroderivative power turbine components based on actual engine operation. The framework discussed in paper provides the capability for Siemens’ customers to move away from fixed overhaul schedule to a customized schedule which is based on a given gas turbines actual operation and inspection findings. This customized overhaul schedule enables the customers a flexibility to maximize the unit availability and minimize operating costs. Semi-empirical framework discussed in this paper, utilizes dynamic systems theory-based approach to estimate the cyclic & creep damage as a response to transient engine operation; characterized by relevant installed engine instrumentation data from the Engine Health Monitoring system. To estimate damage response through any given complex transient operating cycle, algorithm solves a set of ordinary differential equations (ODEs), that have been calibrated to the engine control and safety instrumentation parameters such as shaft speed, turbine temperatures, pressures etc. by pre-analyzed operating envelope cases. The framework can be setup for predicting accumulated cyclic and creep damage for all type of turbine components (Aerofoils, disks, casings, diffusers etc.), transient stress state complexity (in-phase, out-of-phase, uniaxial, multiaxial stress profiles) and is capable to handle unit specific ramp rates, start-up times, restart/cooldown effects specific and random changes in load, history. The framework for discussion in this paper has been demonstrated as applied to the stage-1 blade of an A-35 RT62 power turbine as an example.

Ceramic matrix composites can offer clear potential for a variety of engineering applications where the temperature capabilities of conventional metals are exceeded. Continued mechanical characterisation is essential to gain an understanding of their associated damage and failure mechanisms across a wide range of representative temperatures. The present paper will report ongoing research to characterize the initiation of matrix cracking at room temperature under tensile stress and subsequent damage development under fatigue loading in a SiC f /SiC composite. Imaging and mechanical property data were obtained via in-situ loading within a scanning electron microscope. The temporal nature of damage development was also recorded through the selective employment of acoustic emission. Metrics to describe the spatial distribution of cracks, crack lengths and crack opening displacement under load will be presented. The inspections also provided detailed evidence of the associated crack closure phenomena. The understanding of matrix crack saturation and matrix/fibre interfacial mechanics will be explored, together with the implications for the use of X-ray tomographic inspection of engineering components during service. The potential for these emergent techniques as a basis for future CMC characterization, via automated image recognition and machine learning, will be highlighted.

The inclusion of Full Solution Rejuvenation (FSR ® ) in repairs of flight and aero-derivative gas turbine blades has shifted the primary cause for blade retirement from creep life consumption which is a function of service hours to primarily geometric limitations that are more governed by the cumulative number of repair cycles. For internally cooled components, one of the most significant causes for rejection is the remaining wall thickness of the airfoil. Operating blades with under-sized wall thickness can reduce the load-bearing capability and can increase the stresses that develop under transient thermal conditions found in operation. Typically, ultrasonic wall thickness measurement techniques are used during repair processing for determining remaining wall thickness on components but a number of limitations to obtaining accurate results with this process have been identified. Computed Tomography (CT) wall thickness inspection has addressed these limitations and become an important tool for extending the life of components beyond the typical OEM limits during repair. Entirely from the CT equipment user’s perspective, this paper explores a number of technical findings in the development of a highly accurate CT wall thickness inspection process for flight and aero-derivative gas turbine blades for utilization during repair after one or more service intervals. The importance of the accuracy of these wall thickness measurements is to ensure undersized blades are rejected and blades above the minimum wall thickness are accepted. Reducing uncertainty in the wall thickness measurements allows reconsideration of the acceptance limit and can result in more repairable blades returned for full service intervals. The target accuracy for measurements process was .002”. The findings described include aspects of equipment configuration, process parameters for the initial CT scanning, post-processing and interpretation, results validation specific to the component being measured and process limitations encountered.

The inspection of power generators is a critical activity that will grow in importance during the next decades as operators are seeking solutions to continuously reduce outage duration and improve availability and reliability of the equipment. Automatic and systematic data collection on the status of health of the generator enables the possibility to implement predictive diagnostic/maintenance strategies and reduce the downtime due to repair activities. In this context robotic inspections play a crucial role since they allow a quick and efficient method to acquire data on the status of the generator. This paper describes an innovative robotic system that, thanks to a novel multi-sensor analysis method, is able to inspect and evaluate the overall status of power generators. The detection system, which is located on board a crawler robot, consists of inspection cameras, a micro-hammering system that is able to detect the acoustic response of fixing components of the generator bars and an Electromagnetic Core Imperfection Detection (EL-CID) sensor, which aims to detect insulation failures. Additionally, there is a description of how the robot has been designed to be adaptable to a wide range of different generators, and how it was tested and validated in the field.

Several image processing methods have been implemented over recent years to assist and partially replace on-site technician visual inspection of both manufactured parts and operational equipments. Convolutional neural networks (CNNs) have seen great success in their ability to both identify and classify anomalies within images, in some cases they do this to a higher degree of accuracy than an expert human. Several parts that are manufactured for various aspects of turbomachinery operation must undergo a visual inspection prior to qualification. Machine learning techniques can streamline these visual inspection processes and increase both efficiency and accuracy of defect detection and classification. The adoption of CNNs to manufactured part inspection can also help to improve manufacturing methods by rapidly retrieving data for overall system improvement. In this work a dataset of images with a variety of surface defects and some without defects will be fed through varying CNN set-ups for the rapid identification and classification of the flaws within the images. This work will examine the techniques used to create CNNs and how they can best be applied to part surface image data, and determine the most accurate and efficient techniques that should be implemented. By combining machine learning with non-destructive evaluation methods component health can be rapidly determined and create a more robust system for manufactured parts and operational equipment evaluation.

Most numerical engineering simulation is performed on pristine, as-designed representations of the components and systems in question. Although the rate of through-life performance degradation is hugely important when considering the total cost/benefit of a system, engineers have had profound difficulties in modelling the physical changes that components undergo in service due to the quasi-random and organic nature of the mechanisms such as wear, corrosion, icing and fouling. Typically, the creation of ‘worn’ models is based on a posteriori inspection or scanning of a failing or failed component. This paper presents a novel method for modifying geometry in response to scalar field variables directly accessed from the embedded physical models within physics-based simulation. It uses a distance field, managed as a Level-set, to drive time-dependent changes to the geometry surface, borrowing heavily from technology which has seen widespread use in the computer graphics industry to create and modify items in a natural organic way. A computational mesh can then be constructed around and within the modified geometry so that the simulation can be performed on the now ‘in-service’ version of the components. This greatly improves the predictive power of such simulations and provide a priori predictions of component performance in response to, for example, corrosive environments. The method is robust, can manage and create meshes for arbitrarily complex geometry, is insensitive to large scale topology changes such as hole blockage or passage burn-through, is highly suitable for automated simulation workflows and can represent both additive (fouling, icing) and reductive (erosion, corrosion, burn though) shape change.

The present paper discusses the results of a large experimental data set describing transitional boundary layers. Time resolved Particle Image Velocimetry (PIV) measurements have been adopted to survey the boundary layer developing over a flat plate under prescribed adverse pressure gradients typical of turbomachinery components. The tests have been performed while varying the pressure gradient, the Reynolds number and the inlet free-stream turbulence intensity (FSTI). Two exemplary cases, referring to bypass and separated flow transition, are discussed by means of principal axis analysis and proper orthogonal decomposition (POD). The POD is used to provide statistical representation of the flow structures and to compute the turbulence production (i.e., the mean flow energy dissipation) due to the dynamical features observed for the different transition types. Reduced order model representations of the flow field are provided and their contribution to the total turbulence kinetic energy production is isolated. This analysis is closed by the inspection of the eigenvectors of the strain rate and Reynolds stress tensors. For the separated flow case, it is shown that the eigenvectors of strain rate and shear tensor are almost perfectly aligned downstream of the maximum displacement of the bubble. The reduced order model reconstruction of the Kelvin-Helmholtz shed vortices provides the largest part of the overall TKE production. For the high FSTI induced transition, the eigenvectors of the shear and stress tensors do not have the same direction. The loss generation is related to the local maximum Reynolds normal stress in the streamwise direction, induced by the boundary layer streaks and their breakdown.

Today’s advanced Gas Turbine (GT) combustion systems are designed to achieve low emission limits over a wide operational range. Regular combustion tuning is usually required to ensure that emissions are kept well within the plant compliance limits and that combustion dynamics levels are low enough to meet the mechanical integrity targets of all hot gas parts. These targets must be met during the full lifetime of the power plant, regardless of ambient conditions, fuel composition or engine degradation. Considering also the recent increase of combustor inspection intervals and the change in the GT loading profile due to the intermittent nature of renewable energies, it became advantageous to develop an automated GT26 combustion tuning control. Ansaldo Energia self-learning AutoTune system, based on the well proven product developed by Power Systems Manufacturing (PSM), tunes engine operating parameters to maintain emission and combustion dynamics within specified limits under varying ambient conditions, load ranges, gas compositions and increasing engine degradation. It increases the robustness of the operation concept by introducing dynamic set points instead of fixed settings. This results in tuning flexibility, continuous engine optimization and it helps to potentially eliminate manual engine adjustments during a major inspection cycle. After a general introduction on the operating concept of the GT26, the interaction between AutoTune and the GT controller is described together with the set-up and tuning principles. The universal AutoTune algorithm was adapted to the unique GT26 reheat architecture and flexibility, allowing for: (i) GT26 EV and SEV tuning for emissions and pulsations during stable conditions, (ii) tuning to minimize the minimum environmental load (MEL) with changing fuel gas properties or ambient temperature with the Low Part Load (LPL) concept, (iii) handling of very reactive fuels with high C2+ concentrations, (iv) tuning during transient conditions accounting for the variation of the GT thermal state. Based on robust experience after five successful GT26 installations, tuning attempts and successful tunings are statistically analyzed on a commercial plant over a period of several weeks to demonstrate the enlarged operational flexibility and performance of the GT26 equipped with AutoTune. The data analysis focuses on the Low Part Load operation, where Autotune achieved a robust decrease of MEL of 20MW, corresponding to a reduction of 4% in terms of combined cycle relative load.

Large gas turbine design and service business are challenged with increased demands towards flexible operations, increasing number of start-stop cycles and intermediate cycles. Probabilistic fracture mechanics (PFM) simulation design tools have matured and became robust and reliable. We present a probabilistic re-evaluation of Siemens E-class turbine disks by the combination of probabilistic two-dimensional axisymmetric part analysis of the disk and a novel probabilistic approach for the three-dimensional blade attachment. The first addresses the risk of inherent forging flaws, the latter combines the risk of surface crack initiation, growth and failure. Both models consider the heterogeneous nature of material properties, flaw geometries and detectability. These novel concepts developed at Siemens allow for an optimization of resource usage and safety, as well as the development of new service and inspection concepts for a variety of service frames and classes.

The evolution of industrial gas turbines towards increased efficiency and performance requires ever higher operating temperatures for the engines. In order to remain competitive in the market, OEM companies continuously need to develop maintenance programs and reconditioning technologies able to extend the life of these components as much as possible. During the operation, turbine blades and vanes are prone to deterioration due to the high temperature of the gas passing along the platform surface, causing hot corrosion and thermomechanical fatigue. In particular, this results in deterioration and crack formation at platform edges. If oxidized, cracked or otherwise deteriorated locations are found during service inspections, the component platform has to be rebuilt. The standard method for rebuilding the platform is to remove the deteriorated material volume and rebuild it by welding. In particular, the most common welding method for rotor blades and stator vanes restoration today is the tungsten inert gas welding (GTAW). However, tungsten inert gas welding has a large heat affected zone, which can later lead to major distortion, due the elevated thermal input, and loss of structural integrity of the reconditioned part. In particular, for single crystal material high thermal input can promote recrystallization issues and this phenomenon compromises the mechanical properties and considerably reduces the life of the component. The most recent reconditioning techniques developed by Ansaldo Energia involves the use of laser metal deposition technology to rebuild the damaged areas and restore the platform to the original shape. Prior to the automated process the area, on which the clad will be laid, requires precise preparation. This paper describes a customized, automatic process to restore the deteriorated blade or vane area by the laser metal deposition technique for equiaxial or single crystal material, minimizing the thermal input and resulting in high resistance rebuilding.

The unsteady aerodynamics of floating offshore wind turbine (FOWT) rotors is more complex than that of fixed–bottom turbine rotors, and the uncertainty of low-fidelity aerodynamic predictions, such as those of the blade element momentum theory (BEMT) codes, is higher for the former rotors. Navier-Stokes CFD can improve the understanding of FOWT rotor and wake aerodynamics, and help improve lower-fidelity models. To highlight this potential, blade–resolved analyses of the in-house compressible CFD COSA code and the commercial incompressible CFD code FLUENT were used to investigate the unsteady flow of the NREL 5 MW rotor subjected to prescribed harmonic pitching past the tower base. CFD results were compared to the predictions of the FAST wind turbine code, using BEMT for rotor aerodynamics. Improved tuning of the COSA numerical set-up enabled close matching of the FAST rotor power and loads in fixed–tower mode, and high resolution of the near field wake dynamics; similar agreement levels were obtained with the FLUENT simulations. A novel user-defined function approach, enforcing an additional rigid body motion of the rotor grid conformal to the tower motion, enabled the FLUENT FOWT rotor simulations of this study, and provided new general-purpose FLUENT functionalities for FOWT analyses. All predicted periodic patterns of rotor power and thrust were found to be qualitatively similar, but the power peaks of both CFD predictions were significantly higher than those of FAST. Inspection of the CFD profiles of blade static pressure highlighted and quantified significant compressible flow effects on FOWT rotor power and loads. The blade–resolved analyses of the rotor downstream flow field revealed wake features unique to pitching turbine rotors, primarily the space- and time–dependence of the wake generation, highlighted by the intermittency of the tip vortex shedding.

Compressor fouling is one of the main causes of gas turbine performance degradation. Microsized particles adhere to the blade surfaces increasing the surface roughness and modifying the airfoil shape. In the present work, the contamination of the Allison 250 C18 multistage compressor engine with four sorts of micrometric dust has been provided. The tests were performed changing the relative humidity at the compressor inlet and the unit rotational speed. After each test, a photographic inspection of the internal fouled parts has been realized and the digital pictures have been analyzed employing an image processing package. The deposits build-up of stator vanes and rotor blades have been post-processed and the most affected regions of each compressor stage have been highlighted. Besides, a numerical simulation of the machine has been performed. The numerical flow field has been used to highlight the blade regions which show the most favorable conditions for particle deposition. A theoretical model has been applied to the flow field to simulate the particle deposition. The combination of the deposition model with the results of the CFD simulations gives the chance to better understand the experimentally-founded deposition patterns. Those results have been finally compared to the pictures of the patterns. The possibility to detect and measure the deposition patterns on a rotating test rig and the comparison with models and experiments gave the possibility to assess in detail the particle deposition phenomenon on a multistage axial compressor flow path.

The presence of a crack in a blade can change the natural frequencies of that blade. It has long been a goal to detect blade cracks by assessing the change in a measured vibration frequency of the blade over time. It has been found that prior frequency assessment methods can be less accurate than is desirable to reliably detect the relatively small frequency changes that are typically associated with blade crack sizes of practical interest. This paper describes a method in which potential temporal changes in the frequencies of individual blades are assessed by periodically analyzing complete rows of blades using mistuning analysis techniques that treat the blade rows as coupled systems, in contrast to other techniques that consider each blade individually in turn. This method, while computationally complicated and challenging, has been found to be capable of detecting blade root cracks that are much smaller than those that can be detected using other techniques. Moreover, this method has been demonstrated to detect cracks that are much smaller than the critical size for mechanical separation of the blade from the rotor. This improved frequency assessment technique has been used to identify more than 30 blades with frequency changes that were considered to be potential indicators of blade cracks. Subsequent inspections verified indications in all of those blades. In addition to providing operational guidance, the frequency change data were used to infer the time periods during which crack growth had occurred.

The present article deals with the Zonal Detached Eddy Simulation of the fan module of a modern turbofan engine. The fan module, tested at the AneCom facility, is equipped with rotating fan blades and stationary outlet guide vanes (OGV). The simulation was performed to capture the interaction of the turbulent fan wakes with the OGV walls. The final goal of this simulation is the prediction of the associated broadband noise, not adressed here. In this paper, only the aerodynamic aspects are treated. The simulation relies on a hybrid RANS/LES approach with a zonal strategy: the core airflow is treated in RANS while the bypass airflow is solved with the hybrid approach. Mesh criteria meeting both RANS/LES and acoustic requirements were fulfilled, leading to a mesh of 380 million cells. The simulation was performed during five revolutions and statistical convergence was reached. Inspections of the flow-fields highlight a consistent behaviour of the shielding function (border between RANS and LES solving areas) around the blade walls, at the trailing-edge and in the tip gap flow areas. Comparisons with performance and hot-wire measurements are also presented. Aerodynamic performance and radial evolution of averaged velocities on a plane in-between the fan and the OGV are well retrieved, both in shape and levels. For the turbulent quantities, the shape of the radial profiles are close to the measurements, with much better accuracy in the upper region compared to the RANS solution.

Foreign Object Damage (FOD) to compressor airfoils is a common problem in operating aircraft engines that occurs when objects or debris are sucked into the engines. Especially small surface defects or impact damage (100μm – 300μm depth) can be problematic, as it only becomes noticeable during engine maintenance process, but can have a strong influence on the fatigue strength and service life of individual airfoils. Usually the blade and vane inspection during maintenance is carried out by visual examinations. The inspection findings are individually assessed and as a result the airfoils are accepted, repaired or replaced. This manual inspection process has a significant optimization potential by the means of automatization. This paper presents a novel methodology to automatically detect FOD on compressor airfoils. For the investigation and validation, numerous used compressor blades and vanes were digitized on site with a high precision optical 3D scanning system. A first approach is based on a machine learning algorithm. The idea is the surface segmentation of the digitized airfoil into typical affected areas such as the leading edge (LE), trailing edge (TE), pressure side (PS) or suction side (SS), wherein irregularities during the segmentation can be an indication for FOD. For a second approach, the surface curvature of the airfoil is considered. Locally limited regions with high curvature and concave shapes are sought as an indication for FOD. The required parameters position and depth associated to the individual FOD are calculated in both approaches. The results of both approaches are compared to each other and are validated against the results of a commercial software tool, which uses the approach of digital stoning to create surface defect maps. Furthermore, the results are verified by manually examining the airfoil scans. In the case of relatively small FOD, both approaches generate meaningful results. In terms of larger damages and deformations, both approaches have difficulties detecting it. This problem can be compensated by parametrization of the scanned airfoils with a section based approach using NACA like profile parameters. Unusual changes of specific airfoil parameters (e.g. stagger angle and chord length) over the airfoils height can indicate large FOD or deformation.

Thermal barrier coatings (TBC) found on turbine blades are a key element in the performance and reliability of modern gas turbines. TBC reduces the heat transfer into turbine blades by introducing an additional surface thermal resistance; consequently allowing for higher gas temperatures. During the service life of the blades, the TBC surface may be damaged due to manufacturing imperfections, handling damage, service spalling, or service impact damage, producing chips in the coating. While an increase in aerofoil temperature is expected, it is unknown to what degree the blade will be affected and what parameters of the chip shape affect this result. During routine inspections, the severity of the chipping will often fall to the discretion of the inspecting engineer. Without a quantitative understanding of the flow and heat transfer around these chips, there is potential for premature removal or possible blade failure if left to operate. The goal of this preliminary study is to identify the major driving parameters that lead to the increase in metal temperature when TBC is damaged, such that more quantitative estimates of blade life and refurbishing needs can be made. A two-dimensional computational Conjugate Heat Transfer model was developed; fully resolving the hot gas path and TBC, bond-coat, and super alloy solids. Representative convective conditions were applied to the cold side to emulate the characteristics of a cooled turbine blade. The hot gas path properties included an inlet temperature of 1600 K with varying Mach numbers of 0.30, 0.59, and 0.80 and Reynolds number of 5.1×10 5 , 7.0×10 5 , and 9.0×10 5 as referenced from the leading edge of the model. The cold side was given a coolant temperature of 750 K and a heat transfer coefficient of 1500 W/m 2 *K. The assigned thermal conductivities of the TBC, bond-coat, and metal alloys were 0.7 W/m*K, 7.0 W/m*K, and 11.0 W/m*K, respectively, and layer thicknesses of 0.50 mm, 0.25 mm, and 1.50 mm, respectively. A flat plate model without the presence of the chip was first evaluated to provide a basis of validation by comparison to existing correlations. Comparing heat transfer coefficients, the flat plate model matched within uncertainty to the Chilton-Colburn analogy. In addition, flat plate results captured the boundary layer thickness when compared with Prandtl’s 1/7th power-law. A chip was then introduced into the model, varying the chip width and the edge geometry. The most sensitive driving parameters were identified to be the chip width and Mach number. In cases where the chip width reached 16 times the TBC thickness, temperatures increased by almost 30% when compared to the undamaged equivalents. Additionally, increasing the Mach number of the incoming flow also increased metal temperatures. While the Reynolds number based on the leading edge of the model was deemed negligible, the Reynolds number based on the chip width was found to have a noticeable impact on the blade temperature. In conclusion, this study found that chip edge geometry was a negligible factor, while the Mach number, chip width, and Reynolds number based on the chip width had a significant effect on the total metal temperature.

Since the advent of premix combustion technology in industrial gas turbines, regular manual combustion tuning and engine adjustments have been necessary to maintain engines within emission regulatory limits and to control combustion dynamics (pulsations) for hardware integrity. The emissions and pulsation signatures of premix combustors are strongly driven by ambient conditions, engine performance, degradation and fuel composition. As emissions limits became more stringent over the years, higher combustion dynamics were encountered and challenges to maintain acceptable settings after yearly combustion inspections were regularly encountered. This challenge was further increased as sites operating advanced Gas Turbines (GT) eliminated Combustion Inspections (CI) and required uninterrupted generation at optimum settings for up to three years. The case for automated tuning systems became evident for the Industrial Gas Turbine (IGT) market in the mid 2000’s and different IGT manufacturers and service providers began developing them. Power Systems Manufacturing (PSM) developed the AutoTune (AT) system in 2008 and has since installed it in over fifty units, accumulating close to a million hours of operation. The history of PSM’s AT system development as well as a description of its fundamental principles and capabilities are discussed. The power generation market is changing rapidly with the injection of renewables, thus driving the demand for operational flexibility, the design of PSM’s multi-platform compatible, AutoTune system; allowing for increased peak power, extended turndown and transient tuning is discussed. The paper also describes, how, using the same tuning principles, the application for an AutoTune system can be extended to the Balance Of Plant (BOP) equipment.

Dry, low NO x gas turbines are extremely complex machines that are heavily relied upon in the power industry as baseload, cycling, and/or peaking units. These low-emission gas turbines present potential maintenance and monitoring challenges due to the intrinsically harsh pressure and temperature environments, which make diagnostics and prognostic capabilities extremely difficult. One such challenge involves understanding and interpreting combustion dynamics data. This paper focuses on gas turbine combustion dynamics monitoring (CDM) and describes an algorithm to determine combustor health based upon dynamic pressure. The ongoing CDM and diagnostic work has progressed from taking basic binned FFT data and transforming this data to statistically-based health indicators that can be continuously calculated to determine combustion system anomalies. These anomalies can be detected hours, days, and sometimes even weeks before passive CDM alarm levels are reached, thus, giving additional time to plan for shutdown, inspection, and repair. The paper will discuss real-time observed successes and challenges associated with combustor health monitoring, including sensor health determination and factors associated with the non-linear nature of combustion dynamics. Overall, this work is helping to better alleviate the user’s “black box” perspective of combustion dynamics monitoring systems through automated, real-time interpretation for combustion system health for can annular gas turbines.