The main objective of this analysis is to use probabilistic methods to extract maximum performance out of existing technology. The designer can do this through a series of well-informed trades that consider all factors impacting final performance of the design. In fact, this is the way engines are designed today. However, lack of knowledge about uncertainty makes it difficult to take full advantage of available design margins because their limits are known.
With the probabilistic approach, these trades can be made through probability contours. The best fuel burn solution is limited by the upper limit on fan diameter, imposed to ensure that the engine nacelle has sufficient ground clearance. Solutions in the shaded region of the design space have fan diameters larger than the maximum allowable for this aircraft. For clarity, maximum turbine inlet temperature is fixed at the value yielding best design range. The region for best design range has a higher fan pressure ratio and extraction ratio than that for the best fuel burn (lowest specific fuel consumption). This is expected, but the design range is expressed here as the probability of meeting a target rather than an absolute range. The best fuel burn design is estimated to have only a 30% chance of meeting or exceeding the design range target, while the best design range cycle has a greater than 60% probability of success.
The spacing of the contours shows the sensitivities in the calculations. Near the best design range cycle, sensitivities are roughly 5% probability of success per 8.8 n mi of range, and 5% probability of success per 350 lb of 3000-n mi mission fuel burn.
Similarly, the impact of engine weight on probability of success can be estimated from the contour sensitivities. A change of 5% probability of success is worth about 200 lb of engine weight near the best design range cycle. At first glance, the designer might think it worthwhile to trade several probability-of-success percentage points for a 200-lb reduction in engine weight. Such a trade is additionally appealing because there is a related decrease in engine manufacturing cost when weight is reduced. However, the designer must consider acoustic noise because the lower engine weight also has a higher fan pressure ratio that implies more noise.
Since cost is closely allied with weight, contours of constant manufacturing (shop) cost likely would look very much like contours of constant weight. In addition, acoustic noise is driven primarily by fan pressure ratio, but it also is linked to extraction ratio. Including acoustic noise places an upper limit on fan pressure ratio, and prevents the designer from reducing cost and weight as much as theory indicates. Both acoustic noise and manufacturing cost are essential to making an informed design decision for the best engine cycle compromise.
The overall design process is a well-balanced solution with compromises between all opposing requirements. If engine weight and cost receive too much emphasis, specific fuel consumption and acoustic noise margin suffer. Should too much attention be paid to reducing fuel burn, a heavy and expensive design results? Discovering these relationships is not peculiar to using probabilistic methods; experienced designers have seen these trends many times. Instead, probabilistic methods help designers visualize and make direct trades of design margins. Both capabilities are seriously lacking in today's methods and tools.
Component uncertainty does have a significant impact on aircraft performance. It is imperative for designers to consider the impact of uncertainty if they want to refine current designs by trading design margin for increased performance.
Probabilistic design methods show promise in preliminary design applications, particularly in helping to quantify trades of design margin against performance. The probabilistic sensitivity methods explored in this study only begin to exploit the potential for this technique.
