At first glance, preliminary aircraft engine design may seem well-defined and, therefore, in little need of probabilistic methods. After all, accurate predictions for the performance of engine components as well as of the overall system are possible using existing analysis techniques. However, the cumulative effect of the many uncertainties in engine component performance does impact overall system performance.
For a theoretical large commercial aircraft, it is not unusual to have a cumulative design uncertainty of 5%. While the likelihood of worst-case extremes is small, variation of 100 n mi on either side of the mean design flight range is possible. In today's highly competitive marketplace, this is significant enough to warrant further consideration.
Typically, design margins based on hard-won experience compensate for uncertainties in engine performance estimates. However, much interest within the aircraft engine industry is centered on robust and probabilistic methods. This interest is driven by increased competitive pressures, demand for greater safety, and longer mean time between failures, environmental consciousness, and maturation of the engine and associated technology.
The first three points make engine design more difficult, with design freedom increasingly limited as time goes on. Technology maturation has slowed the pace of major developments over the past decade. As progress slows and constraints become more restrictive, engine designers must extract every bit of performance from current technologies while simultaneously satisfying all requirements.
If one accepts this technology maturation argument, designers of future engines will have to find ways of getting superior performance without the benefit of major technological advances. The way to accomplish this is by refining current designs and trimming design margins while staying within safety requirements.
Most critical decisions are made in the early stages of development where available design freedom can best achieve better performance. Probabilistic design provides an analytical framework that allows the designer to improve performance by determining the necessary design margin, the parameters impacting the uncertainty in performance, and ways to reduce the impact of uncertainty.
Probabilistic methods have been applied by researchers at the Georgia Institute of Technology to the preliminary design process for a high-bypass engine as installed on a theoretical, 400-passenger, commercial aircraft. Engine and mission performance figures of merit (FoMs) are tracked to show the impact of changing the cycle parameters of a scaleable, fixed-configuration engine on a fixed-size, four-engine aircraft's performance.
Information was provided by Dimitri N. Mavris, Noel I. Macsotai,
and Bryce Roth of the Georgia Institute of Technology.
