Progress on Combined Optic-Acoustic Monitoring of Combustion in a Gas Turbine

The need for better combustion monitoring in gas turbines has become more acute with the latest technical requirements, standards, and policies in terms of safety, environment, efficiency, operation flexibility and operation costs. This paper reports on a concept for gas turbine combustion monitoring using multiple probes that combine optical and acoustical measurements. The motivation of the project is twofold. On the one side, one wants to exploit the radiative feature of the flame and transform it into a piece of reliable information about the combustion status. On the other side, this information can be useful in terms of data interpretation or data reconciliation with other information coming from further sensors such as temperature probes, fast pressure probes or accelerometers. For this purpose, a set of multiple Rayleigh Criterion Probes (RCPs) combining optical and acoustical sensors is used. Detailed information about the RCP can be found in paper GT2017-63626, [1]. The focus is put on the detection of the flame, on the monitoring of the ignition process, on the quality assessment of combustion based on its spectral contents (including soot formation) and on the detection of possible combustion instabilities. The novel test rig used for validation of this advanced combustion monitoring concept is introduced, and minimal instrumentation including three probes is recommended. The split in red, green and blue (RGB) light components and their further analysis allows mapping the different types of operation. Solutions are proposed to bring the optical interface as near as possible to the flame and make it operational and reliable despite prevailing heat. The paper closes with a description of the ongoing tests on a pressurized combustion facility, and a sketch for a 3-RCPs based compact combustion monitoring system. The advantages of selected chromatic spectral bands are discussed, as well as the remaining challenges towards a full demonstration.

Combined Optic-Acoustic Monitoring of Combustion in a Gas Turbine

The need for better combustion monitoring in gas turbines has become more acute with the latest technical requirements, standards, and policies in terms of safety, environment, efficiency, operation flexibility, and operation costs. Combustion Bay One e.U. and FH JOANNEUM GmbH initiated in 2015 an experimental research program about the feasibility and first assessments of placing optical systems near the combustor. The project’s acronym “emootion” stands for “Engine health MOnitOring and refined combusTION control based on optical diagnostic techniques embedded in the combustor”. The motivation of the project is twofold. On one side, one wants to exploit the radiative feature of the flame and to transform it into a piece of reliable information about the combustion status. On the other side, this information can be useful in terms of data interpretation or data reconciliation with other information coming from other sensors such as temperature probes, fast pressure probes, or accelerometers. The focus is put on several aspects of combustor operations: on detection of the flame, on monitoring of the ignition process, on a quality assessment of combustion based on its spectral contents (including soot formation), and on the detection of possible combustion instabilities. Promising results were obtained using photodiodes that offer an adequate trade-off between narrow-band sensitivity and signal time response. It is shown that it is convenient to combine a fast-pressure sensor with an optical sensor in a compact form; this combination has led to the so-called Rayleigh Criterion Probe (RCP). The split in red, green, and blue (RGB) light components and their further analysis allows for mapping the different types of operation. Regarding the probe packaging aspect, it is discussed that the level of light collection needed to keep an acceptable signal-to-noise ratio has been so far a restraint for the use of optical fibres. Solutions are proposed to bring the optical sensor as close as possible to the optical interface and to make it operational and reliable in prevailing heat. This contribution closes with a description of the pressure tests in a new combustion facility built for this purpose. A compact and portable combustion monitoring system including at least 3 RCPs can become an instrumentation standard within the next decade.

Turbocharger Speed Monitoring Based on Vibration Measurements for Diesel Engine Management

Demanding legislation on exhaust emissions and fuel consumption has led great attention to on board control algorithms able to optimize the combustion process in terms of efficiency and pollutants emissions production. Dealing with turbocharged engines, the thermo and fluid dynamic conditions of the exhaust gas are responsible for the turbine rotation; its speed has demonstrated to be related to the combustion process and can be used for the combustion monitoring. This work presents a methodology in which the instantaneous turbocharger speed is obtained by the processing of the signal from an accelerometer mounted on the compressor housing. Experimental tests have been carried out on a small water-cooled, two cylinder, common rail diesel engine installed in the Laboratory of the Engineering Department at ‘ROMA TRE’ University. The methodology has been applied to the signals acquired during steady state and transient tests. The comparison between the estimations provided by the accelerometer and the values obtained by direct measurements highlighted the accuracy of the predictions thus demonstrating the suitability of the accelerometer to be used as feedback signal in algorithms for the engine management in order to maintain the combustion effectiveness in spite of aging and degradation of components, variations of fuel properties.

Smart Sensor Technologies for Performance Optimization of Power Generating Assets

Significant research is ongoing on several fronts in smart sensor technologies for optimizing the performance of power generating assets. The initiatives include: 1. Real-time models with advanced computational algorithms, embedded intelligence at sensor and component level for reducing operating costs, improving efficiencies, and lowering emissions. 2. Optical sapphire sensors for monitoring operation and performance of critical components in harsh environments, for improving accuracy of measurements in combustion monitoring, and lowering operating costs. 3. Wireless technologies using (a) microwave acoustic sensors for real-time monitoring of equipment in high temperature/pressure environments (b) integrated gas/temperature acoustic sensors for combustion monitoring in diverse harsh environment locations to improve combustion efficiency, reduce emissions, and lower maintenance costs (c) sensors for sensing temperature, strain and soot accumulation inside coal-fired boilers for detailed condition monitoring, better understanding of combustion and heat exchange processes, improved designs, more efficient operation. 4. Distributed optical fiber sensing system for real-time monitoring and optimization of high temperature profiles for improving efficiency and lowering emissions. 5. Smart parts with embedded sensors for in situ monitoring of multiple parameters in existing and new facilities. 6. Optimizing advanced 3D manufacturing processes for embedded sensors in components for harsh environments to reduce costs and improve efficiency of power generation facilities with carbon capture capabilities. 7. New energy-harvesting materials for powering wireless sensors in harsh environments, improving reliability of wireless sensors in demanding environments, and in-situ monitoring and performance of devices and systems. 8. Real-time, accurate and reliable monitoring of temperature at distributed locations of sensors in harsh environments for improving operations and reducing operating costs. 9. Algorithms and methodologies for designing control systems utilizing distributed intelligence for optimal control of power generation facilities. 10. Gas sensors for monitoring high temperatures in harsh environments for lowering operating costs and better control of operations. 11. Optimizing placement of smart sensors in networks for cognitive behavior and self-learning. This paper provides an overview of the initiatives in smart sensor technologies and their applications in optimizing the performance of power generating facilities.