Improved Compressor Maps Using Approximate Solutions to the Moore-Greitzer Model

Producing compressor maps is time consuming, costly and error prone and many data samples must be collected to give sufficient accuracy. Even then, expert input is typically required to fine tune the map to the appropriate shape. In this paper, we take some of that expertise and incorporate it in the smoothing process. The main piece of knowledge used is the cubic approximation for speed lines derived from the Moore Greitzer model. This well accepted approximation captures much of the general performance properties of compressors. But it is also widely recognized as only being very roughly true of real compressors. Nevertheless, we show that embedding this approximation, however limited, in the smoothing process results in accurate interpolation and extrapolation. The aim of this work is to substantially reduce the need for human input in the fitting process. We also anticipate a number of other benefits: less data is needed, with the commensurate time and money saved; the data collection process can be monitored for possible problems; changes in the map can be quantified and, when sufficiently small, data collection can be terminated.

A Survey of New Technologies Used by Siemens Energy for the Monitoring and Diagnosis of a Global Fleet of Power Generation Systems

Siemens has been on the cutting edge of the power generation business for over a century and has been providing diagnostics systems design and implementation since the early 1980s. Siemens Power Diagnostics® Services is designed to maximize plant performance, availability and profitability. Engineering knowledge, combined with the use of sophisticated tools, provides trending and analysis capabilities to address a broad range of operating needs specific to each customer. The goal of Power Diagnostics® is to enhance Siemens assistance to our customers through the detection of impending operational problems thereby helping to minimize unplanned outages and maximize power generation availability. A variety of new technologies are being harnessed to further this goal. A survey and discussion of these technologies will be the goal of this paper. Some of the projects discussed will be; Advances in the Power Plant Automated Diagnostics Systems, Blade Vibration Monitor (BVM), Fiber Optic Vibration Monitor (FOVM), and the Radio Frequency Monitor (RFM). The development and verification phases of research projects have often been conducted at customer sites. Many aspects of these technologies are new and will be of interest to gas turbine engineers as they are not widely applied yet. It is hoped that the reader will gain a new appreciation for the scope of modern diagnostic methods for power generation systems.

New Model Based Gas Turbine Fault Diagnostics Using 1D Engine Model and Nonlinear Identification Algorithms

Gas turbine fault identification has been used worldwide in many aero and land engines. Model based techniques have improved isolation of faults in components and stages’ fault trend monitoring. In this paper a powerful nonlinear fault identification system is developed in order to predict the location and trend of faults in two major components: compressor and turbine. For this purpose Siemens V94.2 gas turbine engine is modeled one dimensionally. The compressor is simulated using stage stacking technique, while a stage by stage blade cooling model has been used in simulation of the turbine. New fault model has been used for turbine, in which a degradation distribution has been considered for turbine stages’ performance. In order to validate the identification system with a real case, a combined fault model (a combination of existing faults models) for compressor is used. Also the first stage of the turbine is degraded alone while keeping the other stages healthy. The target was to identify the faulty stages not faulty components. The imposed faults are one of the most common faults in a gas turbine engine and the problem is one of the most difficult cases. Results show that the fault diagnostic system could isolate faults between compressor and turbine. It also predicts the location of faulty stages of each component. The most interesting result is that the fault is predicted only in the first stage (faulty stage) of the turbine while other stages are identified as healthy. Also combined fault of compressor is well identified. However, the magnitude of degradation could not be well predicted but, using more detailed models as well as better data from gas turbine exhaust temperature, will enhance diagnostic results.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

Requirements for Gas Turbine Inlet Systems in Russia

Russian power generation, oil and gas businesses are rapidly growing. Installation of new industrial gas turbines is booming to fulfill the demand from economic growth. Russia is a unique country from the annual temperature variation point of view. Some regions may reach up to 100C. One of the biggest challenges for world producers of gas turbines in Russia is the ability to operate products at power plants during cold winters, when ambient temperature might be −60C for a couple of weeks in a row. The reliability and availability of the equipment during the cold season is very critical. Design of inlet systems and filter houses for the Russian market, specifically for northern regions, has a lot of specifics and engineering challenges. Joint Stock Company CKTI is the biggest Russian supplier of air intake systems for industrial gas turbines and axial-flow compressors. In 1969 this enterprise designed and installed the first inlet for the power plant Dagskaya GRES (State Regional Electric Power Plant) with the first 100MW gas-turbine which was designed and manufactured by LMZ. Since the late 1960s CKTI has designed and manufactured inlet systems for the world market and been the main supplier for the Russian market. During the last two years CKTI has designed inlet systems for a broad variety of gas turbine engines ranging from 24MW up to 110MW turbines which are used for power generation and as a mechanical drive for the oil and gas industry. CKTI inlet systems with filtering devices or houses are successfully used in different climate zones including the world’s coldest city Yakutsk and hot Nigeria. CKTI has established CTQs (Critical to quality) and requirements for industrial gas turbine inlet systems which will be installed in Russia in different climate zones for all types of energy installations. The last NPI project of the inlet system, including a nonstandard layout, was done for a small gas-turbine engine which is installed on a railway cart. This arrangement is designed to clean railway lines with the exhaust jet in a quarry during the winter. The design of the inlet system with efficient multistage compressor extraction for deicing, dust and snow resistance has an interesting solution. The detailed description of challenges, weather requirements, calculations, losses, and design methodologies to qualify the system for tough requirements, are described in the paper.

Blended Education in Turbomachinery and Fluid Dynamics

The paper deals with blended education in turbomachinery and fluid dynamics courses in Finland. The teaching methodology of these courses has been developed to comply with the new challenges of the education in technology. Presently six of the courses in the curriculum are following the schemes explained in this presentation. The courses are studied in the last year of the Bachelor level and in the Masters level quantifying from 2 to 4 ECTS credits. Students get all the material from the teaching platform in the web, which can be accessed freely anytime and practically from anywhere. Before attending the teaching events, the students go through the study material, perform several exercises and take a quiz, which can give them extra points for the exam tally. The contact teaching of the course is divided into four Learning Sessions of four hours. The first half is reserved to deepen the acquired knowledge, and performed in a way to attract a more interactive atmosphere. In the second half the students are divided to groups, where they solve more difficult study exercises compared to those they already have trained on. As the students are expected to study with the material of blended learning in advance, the percentage of the contact hours has to be lowered in comparison to the traditional teaching in order to maintain a balance with the credits in the course. However, with the acquired knowledge, the students are more interactive with the teachers and the teaching becomes more efficient. The students have given positive feedback on the courses. The instructors have found that the blended learning is not necessary an easier task, although there are less contact hours with the student. The efficiency of teaching has increased and the teaching is more rewarding. Both the students and the teachers found that blended education suited best for student groups around 10 students.

Performance Evaluation of the IPRP Technology When Fueled With Biomass Residuals and Waste Feedstocks

The Integrated Pyrolysis Regenerated Plant (IPRP) concept is based on a rotary kiln pyrolyzer that converts biomass or wastes (B&W) in a rich gas used to fuel a gas turbine (GT); the combustion of pyrolysis by-products (char or tar), is used to provide heat to the pyrolyzer together with the GT exhaust gases. The IPRP concept was modelled through an homemade software, that utilizes thermodynamic relations, energy balances and data available in the Literature for BW pyrolysis products. The analysis was carried out investigating the influence on the plant performances of main thermodynamic parameters like the Turbine Inlet Temperature (TIT), the Regeneration Ratio (RR) and the manometric compression ratio (β) of the gas turbine; when data on the pyrolysis process where available for different pyrolysis temperature, also the different pyrolysis temperature (TP) was considered. Finally, data obtained from the analysis where collected for the typical parameters of different GT sizes, namely the manometric compression ratio and the turbine inlet temperature. For the other parameters, where considered the ones that give the highest efficiencies. The paper shows the IPRP efficiency, when fuelled with different biomass or wastes materials and for different GT (plant) size.

Impact of Mechanical Chevrons on Supersonic Jet Flow and Noise

In this paper, we present observations on the impact of mechanical chevrons on modifying the flow field and noise emanated by supersonic jet flows. These observations are derived from both a monotonically integrated large-eddy simulation (MILES) approach to simulate the near fields of supersonic jet flows and laboratory experiments. The nozzle geometries used in this research are representative of practical engine nozzles. A finite-element flow solver using unstructured grids allows us to model the nozzle geometry accurately and the MILES approach directly computes the large-scale turbulent flow structures. The emphasis of the work is on “off-design” or non-ideally expanded flow conditions. LES for several total pressure ratios under non-ideally expanded flow conditions were simulated and compared to experimental data. The agreement between the predictions and the measurements on the flow field and near-field acoustics is good. After this initial step on validating the computational methodology, the impact of mechanical chevrons on modifying the flow field and hence the near-field acoustics is being investigated. This paper presents the results to date and further details will be presented at the meeting.

Investigations of Pyrolysis Syngas Swirl Flames in a Combustor Model

An atmospheric combustor model with optical access for confined, non-premixed swirl-stabilized flames was developed in order to investigate the combustion behaviour of gas turbine flames fired with low-caloric syngases. The applied measuring techniques are Particle Image Velocimetry (PIV) for cold aerodynamic analysis, IR thermometry in open flame conditions, thermocouple traverses and global emissions analyzer in confined flame conditions. Two different fuels were chosen: propane and a synthetic mixture of CH4, CO, CO2, H2 having a composition typical of a gas from wood pyrolysis. Thermal powers between ∼5 kW and ∼20 kW were obtained with two different air flow rates and equivalence ratio varied in the range φ = 0.2–1.0. The experimental results constitute a database for the validation of numerical combustion models. Preliminary numerical analysis was carried out with STAR-CD software package.

Development and Testing of Harsh Environment, Wireless Sensor Systems for Industrial Gas Turbines

The potential for savings provided to worldwide operators of industrial gas turbines, by transitioning from the current standard of interval-based maintenance to condition-based maintenance may be in the hundreds of millions of dollars. In addition, the operational flexibility that may be obtained by knowing the historical and current condition of life-limiting components will enable more efficient use of industrial gas turbine resources, with less risk of unplanned outages as a result of off-parameter operations. To date, it has been impossible to apply true condition-based maintenance to industrial gas turbines because the extremely harsh operating conditions in the heart of a gas turbine preclude using the necessary advanced sensor systems to monitor the machine’s condition continuously. Siemens, Rove Technical Services, and Arkansas Power Electronics International are working together to develop a potentially industry-changing technology to build smart, self-aware engine components that incorporate embedded, harsh-environment-capable sensors and high temperature capable wireless telemetry systems for continuously monitoring component condition in the hot gas path turbine sections. The approach involves embedding sensors on complex shapes, such as turbine blades, embedding wireless telemetry systems in regions with temperatures that preclude the use of conventional silicon-based electronics, and successfully transmitting the sensor information from an environment very hostile to wireless signals. The results presented will include those from advanced, harsh environment sensor and wireless telemetry component development activities. In addition, results from laboratory and high temperature rig and spin testing will be discussed.