A Statistical Method Useful in the Development of Jet Engine Fuel Controls

1965 ◽  
Author(s):  
H. J. Jazwinski ◽  
C. D. Mccarthy
Keyword(s):  
Statistical Method ◽  
Engine Fuel ◽  
Jet Engine

scholarly journals Droplet Size and Spray Dispersion Characteristics of an Impingement-Jet Type Jet Engine Fuel Injector

2008 ◽  
Vol 74 (746) ◽  
pp. 2213-2220
Author(s):  
Takahiro TAMURA ◽  
Mikiya ARAKI ◽  
Hideshi YAMADA ◽  
Seiichi SHIGA ◽  
Shigeru HAYASHI ◽  
...  
Keyword(s):  
Droplet Size ◽  
Engine Fuel ◽  
Dispersion Characteristics ◽  
Fuel Injector ◽  
Jet Engine ◽  
Impingement Jet

Failure of Jet Engine Fuel Control Unit Originated by Corrosion of Cadmium Coating

2020 ◽  
Vol 20 (5) ◽  
pp. 1470-1478
Author(s):  
Mirko Sgambetterra ◽  
Guido Zucca ◽  
Vincenzo di Francesco ◽  
Fabrizio De Paolis ◽  
Manuele Bernabei
Keyword(s):  
Control Unit ◽  
Engine Fuel ◽  
Jet Engine

Real-time multi-rate HIL simulation platform for evaluation of a jet engine fuel controller

2011 ◽  
Vol 19 (3) ◽  
pp. 996-1006 ◽  
Author(s):  
Morteza Montazeri-Gh ◽  
Mostafa Nasiri ◽  
Soheil Jafari
Keyword(s):  
Real Time ◽  
Simulation Platform ◽  
Engine Fuel ◽  
Jet Engine ◽  
Hil Simulation

Synthesis of a component of the jet engine fuel and an accelerator of oil tar oxidation by catalytic processing of heavy pyrolysis tar

2010 ◽  
Vol 83 (7) ◽  
pp. 1265-1269
Author(s):  
Kh. D. Ibragimov ◽  
E. G. Ismailov ◽  
G. S. Martynova ◽  
N. R. Bektashi ◽  
Z. M. Ibragimova ◽  
...  
Keyword(s):  
Engine Fuel ◽  
Jet Engine ◽  
Pyrolysis Tar ◽  
Catalytic Processing

scholarly journals Towards Numerical Simulation of Snow Showers in Jet Engine Fuel Systems

2015 ◽  
pp. 613-624 ◽  
Author(s):  
Ewen Marechal ◽  
Sofiane Khelladi ◽  
Florent Ravelet ◽  
Farid Bakir ◽  
Olivier Delepierre-Massue Snecma
Keyword(s):  
Numerical Simulation ◽  
Engine Fuel ◽  
Jet Engine

Emulsified Jet Engine Fuel

1967 ◽  
Author(s):  
Jay C. Harris ◽  
E. A. Steinmetz
Keyword(s):  
Engine Fuel ◽  
Jet Engine

Ultrahigh-Pressure Waterjets for Coke Removal in Jet Engine Fuel Tubes, with Laser-Optic Inspection

1996 ◽  
Author(s):  
D. E. Steele ◽  
J. L. Doyle ◽  
D. O. Monserud ◽  
P. J. Miles
Keyword(s):  
Ultrahigh Pressure ◽  
Engine Fuel ◽  
Jet Engine ◽  
Coke Removal ◽  
Laser Optic

G0701-1-3 Effect of Atomization on Combustion Characteristics in a Jet Engine Fuel Injector with Wall Impingement

2009 ◽  
Vol 2009.3 (0) ◽  
pp. 115-116
Author(s):  
Nobuaki MOTEGI ◽  
Mikiya ARAKI ◽  
Hideshi YAMADA ◽  
Hisao NAKAMURA ◽  
Seiichi SHIGA
Keyword(s):  
Combustion Characteristics ◽  
Engine Fuel ◽  
Fuel Injector ◽  
Jet Engine ◽  
Wall Impingement

A Method for Forecasting the Condition of HPT NGVs by Using Bayesian Belief Networks and a Statistical Approach

2014 ◽  
Author(s):  
Daniel Giesecke ◽  
Jens Friedrichs ◽  
Thomas Kenull ◽  
Matthias Binner ◽  
Martin Siegert
Keyword(s):  
Statistical Method ◽  
Expert Knowledge ◽  
Guide Vane ◽  
Hypothesis Test ◽  
Bayesian Belief Networks ◽  
Statistical Hypothesis ◽  
Linear Regression Method ◽  
Arithmetic Means ◽  
Jet Engine ◽  
Maintenance Process

Jet engine maintenance is a very competitive field in terms of time and costs. To increase planning security and reduce turnaround time (TAT) of the maintenance process it is important to get as much engine data as possible before disassembly. Aero engines are especially subjected to environmental and operational influences. For the high pressure turbine (HPT), the following parameters have been identified to describe the deterioration of its nozzle guide vane (NGV): On-wing cycles, NGV material, airport region, engine wing position, thrust rating, vane repair history and customer business segment. The combined influences of the parameters are non-trivial and it is not possible to acquire them analytically. There are no known mathematical laws connecting the above-mentioned parameters. The linear regression method set limits for processing data in an adequate manner. This is confirmed by the analysis of the arithmetic means and standard deviations. Especially the standard deviation values fit in a broad spectrum due to various reasons. Thus, it is not feasible to make an appropriate forecast with a simple statistical method due to the multidimensional character of the parameters influencing the accuracy. For this reason, advanced methods need to be developed to derive a feasible forecast method. By applying a statistical hypothesis test, a bayesian belief network (BBN) has been designed. It allows the use of imprecise data without suffering a significant loss in forecast accuracy and additionally, the implementation of expert knowledge. The objective of this study is to develop an effective BBN in order to adequately predict the next repair of the first stage HPT NGV of the General Electric CF6-80C2 engine. The reason for selecting the NGV is due to its high susceptibility to different influences, combined with the significant costs and TAT during the maintenance process. Having poor forecasting quality by using a simple statistical method, the evaluation of the BBN provides very satisfactory accuracy of above 80 percent which is equivalent to 19 out of 23 vane segments. Furthermore, the developed BBN emphasises robustness when detecting the expected tendencies while having only a limited amount of input parameters. Further work includes application of this method on other engine components as well as establishing the business value of the developed method. In conclusion, BBN have tremendous potential for forecasting the repair of the entire jet engine.

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