Title

Gas Turbine Exhaust Temperature Measurement Approach Using Time-Frequency Controlled Sources

Abstract

Siemens has developed a novel approach for measuring the process gas temperature leaving the power turbine in their heavy industrial gas turbine engines using active acoustic tomography. Siemens has deployed this measurement technique on two test engines of different power ranges and different combustion and exhaust duct configurations. These engine tests have demonstrated that this technology is effective and robust. All working parts are outside the heat effective zone so, unlike the traditional intrusive point temperature measurement method, sensors are easily replaceable during engine operation. Bulk exhaust temperature is used in performance testing of industrial gas turbine engines and is a critical measurement for power production. Temperature distribution information in the exhaust plane is valuable for safe engine operation and can be used to prevent lifetime reduction due to hotspots or to monitor the burner flames. Siemens used broadband sound sources for the previously reported acoustic pyrometer experiments. This paper extends this work utilizing sparse time-frequency encoded sources to improve the robustness of time of flight estimation in the high noise area of the turbine exhaust. The goal is to achieve a higher signal to noise ratio between the emitted and received signals by focusing the acoustic energy into narrow time-frequency bins that are little affected by turbine noise. Different acoustic patterns are tested and compared to the previously used broadband source both in laboratory experiments and a turbine test bed. The patterns are evaluated regarding their noise robustness, sound pressure levels and narrow autocorrelation which are important for accurate time of flight estimation in high noise environments.

Publication Date

1-1-2015

Publication Title

Proceedings of the ASME Turbo Expo

Volume

6

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/GT2015-42139

Socpus ID

84954289456 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84954289456

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