Maritime Patrol Aircraft Engine Study, General Electric Derivative Engines. Volume III. Appendix B - Performance Data - TF34/T7 Study A1 Turboprop.

1979 ◽  
Author(s):  
R. Hirschkron ◽  
R. H. Davis ◽  
R. E. Warren
Author(s):  
D. G. Zimmerman ◽  
W. H. Kern

At the 1962 ASME meeting in Houston, Allison submitted a paper describing the possibilities of industrializing the T56 aircraft engine. Since that time, Allison has modified the engine for gas burning and is operating one as a prime mover for a 2000-kw generator at the Indianapolis Plant. In addition, two additional units are to be installed in the gas-transmisson field driving centrifugal gas compressors. This paper gives a description of the units and a status report on the installation and operation. Photographs of the installation and performance data, as necessary to complete the report, are included.


Author(s):  
Robert C. Stancliff

The General Electric LM5000 Marine Gas Turbine (see figure 1) intended for application to commercial and naval ships requiring high power (50,000 BHP nominal), high thermal efficiency (38 percent), and compact, marinized and relatively light weight prime movers is described. Ship candidates include Fast Support Ships, Aircraft Carriers [in a Combined Nuclear and Gas Turbine (CONAG) propulsion system], Battleships and large surface effect ships. The LM5000 marine gas turbine is a marinized version of the LM5000 industrial gas turbine which was derived in 1977 from the CF6-50 aircraft engine. The CF6-6 model of this family of aircraft engines was the parent of the over 648 GE LM2500 marine gas turbine now used on the ships of 18 navies, 32 ship programs and 247 ships of the world. Over 2100 of the CF6-50 mode] engines are used on over 600 of the McDonald Douglas DC-10, the Airbus A300 and the Boeing 747 aircraft. Since reliability and durability are dependent upon engine family experience, the hardware commonality with the CF6-50 aircraft engine is described as well as the associated experience, performance, installation and maintainability features.


Author(s):  
D. E. Saunders

Spare parts shortages for engine programs in the late 1970’s, together with significant tightening of military spare parts budgets, prompted the development of a sophisticated forecasting system at General Electric Company, Aircraft Engine Business Group, Lynn, Massachusetts. GE’s system revolves around a generic simulation program containing the reliability, durability, maintainability and availability characteristics of an engine and its component parts in the data set. Subroutines of the program include the logic associated with particular types of maintenance policy such as on-condition maintenance, opportunistic maintenance, or scheduled part replacement.


Author(s):  
Stefan Spieler ◽  
Stephan Staudacher ◽  
Roland Fiola ◽  
Peter Sahm ◽  
Matthias Weißschuh

The change of performance parameters over time due to engine deterioration and production scatter plays an important role to ensure safe and economical engine operation. A tool has been developed which is able to model production scatter and engine deterioration on the basis of elementary changes of numerous construction features. In order to consider the characteristics of an engine fleet as well as random environmental influences, a probabilistic approach using Monte Carlo simulation (MCS) was chosen. To quantify the impact of feature deviations on performance relevant metrics, nonlinear sensitivity functions are used to obtain scalars and offsets on turbomachinery maps, which reflect module behavior during operation. Probability density functions (PDFs) of user-defined performance parameters of an engine fleet are then calculated by performing a MCS in a performance synthesis program. For the validation of the developed methodology pass-off test data, endurance engine test data, as well as data from engine maintenance, incoming tests have been used. For this purpose, measured engine fleet performance data have been corrected by statistically eliminating the influence of measuring errors. The validation process showed the model’s ability to predict more than 90% of the measured performance variance. Furthermore, predicted performance trends correspond well to performance data from engines in operation. Two model enhancements are presented, the first of which is intended for maintenance cost prediction. It is able to generate PDFs of failure times for different features. The second enhancement correlates feature change and operating conditions and thus connects airline operation and maintenance costs. Subsequently, it is shown that the model developed is a powerful tool to assist in aircraft engine design and production processes, thanks to its ability to identify and quantitatively assess main drivers for performance variance and trends.


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