Abstract:
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.