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Title: Severity estimation and shop visit prediction of civil aircraft engines
Author: Hanumanthan, Hariharan
ISNI:       0000 0004 2729 9364
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2009
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To sustain in the vibrant field of civil aviation, the aircraft and engine manufacturers are in the pursuit of delivering efficient systems with the best economics. In umpteen scenarios of growing interest, engine maintenance cost due to scheduled maintenance is of importance. The current research is focused on estimation of the maintenance factors, such as severity and shop visit rate to study the operational scenarios and concurrent technologies. The severity, defined as relative engine damage is estimated by blending the aircraft performance, gas turbine performance, gas turbine design and life estimation methods towards transforming the thrust variation into life estimates, reflecting the severity on critical Life Limited Part (LLP) of an aircraft engine. The Shop Visit Rate (SVR) is predicted based on Exhaust Gas Temperature (EGT) margin consumption due to gas turbine performance degradation. The severity studies reveal that Hight Pressure Turbine (HPT) blade and disc are critical, depicting engine severity. Lower thrust engine severity is dominated by cyclic damage (low cycle fatigue) and large thrust engines by steady state damage (creep). The operational factors, take-off derate and Outside Air Temperature (OAT) have more sensitivity on severity of aircraft engines. The use of climb derate, reduces the damage on large thrust engines considerably, especially for three shaft engines. Cooling effectiveness and thermal barrier coating are important technological factors for reducing the severity level. The SVR prediction on lower and large thrust engines, depict the take-off EGT as a source for shop visits, governed by operational parameters such as takeoff derate, OAT, trip length and engine wash. The engine aging curves are represented as Weibull distribution based on severity and SVR. Severity estimation and shop visit prediction methodology, demonstrated through an integrated tool will serve as a decision making element for comparing competitive engines, operational strategies and engine technologies.
Supervisor: Singh, R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available