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As part of the Gas Turbine Condition Based Maintenance (CBM) Program, Naval Surface Warfare Center, Carderock Division Code 9334 conducted compressor fouling testing on the General Electric LM2500 and Rolls Royce/Allison 501-K Series gas turbines. The objective of these tests was to determine the feasibility of quantifying compressor performance degradation using existing and/or added engine sensors. The end goal of these tests will be to implement an algorithm in the Navy Fleet that will determine the optimum time to detergent crank wash each gas turbine based upon compressor health, fuel economy and other factors which must be determined. Fouling tests were conducted at the Land Based Engineering Site (LBES). For each gas turbine, the test plan that was utilized consisted of injecting a salt solution into the gas turbine inlet, gathering compressor performance and fuel economy data, analyzing the data to verify sensor trends, and assessing the usefulness of each parameter in determining compressor and overall gas turbine health. Based upon data collected during these fouling tests, it seems feasible to accomplish the end goal. Impact Technologies, who analyzed the data sets for both of these fouling tests, has developed a prognostic modeling approach for each of these gas turbines using a combination of the data and probabilistic analysis.

In June 2005 1,2 , Naval Surface Warfare Center (NSWC) Gas Turbine Emerging Technologies conducted testing on a General Electric LM2500 gas turbine engine. This engine is the main propulsor for DDG-51 and CG-47 class United States Navy surface ships. The purpose of this testing was to induce compressor stall in order to evaluate existing algorithms for stall prediction and gather data for further algorithm development. In addition to existing sensor data, dynamic pressure sensors, with data rates ranging from 20-1000 KHz, were installed in various compressor stages for additional capability. Utilizing the data collected, in conjunction with a MATLAB-based neural network approach, NSWC has developed algorithms to detect and trend stall margin and related quantities that can eventually be used in an early stall warning system onboard ship. Algorithms can be incorporated into the recently installed Full Authority Digital Control, allowing real-time stall detection and prevention. This paper discusses the feasibility of employing a neural network approach to detect and output a compressor stall margin value and associated risk of compressor stall for U.S. Navy LM2500 gas turbine engines TABLE OF CONTENTS 1. INTRODUCTION.

In June 1997, the U.S. Navy purchased the Soviet military cargo ship “Vladimir Vaslyaev” for conversion to the USNS LCPL Roy M. Wheat for use in the Maritime Prepositioning Force. This paper documents the efforts of NSWCCD and dB Associates in supporting the installation, startup, and integration of the ship’s controls with the two Zorya DT-59 main propulsion gas turbine engines (GTE’s). The installation documentation developed included a video record of the port and starboard gas turbine installations, as well as information that aided in the development of the Engineering Operational Procedures (EOP). The integration for the DT-59s focused on providing engine speed sensors, an engine vibration monitoring system and engine reversing protection circuits.