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–c * = ln(x) / ln(2) or x = e^[–c * ln(2)]. Statistical Analysis 81 (4) (5) Based on the production cost regression in Table 6.9, the unit num- ber for x is about 375. It is interesting to speculate why the regression would converge to this unit number. Some of the military engine contractors/producers we interviewed for this study said that cost improvement slopes increase (or flatten) around unit numbers of 250 to 300. However, our production data do not show a consistent level- ing off at that point. The similarity of the two values (250 and 300) might be a coincidence. Nonetheless, the unit value falls between those two values in previous RAND military engine studies. The re- gression analysis in Table 6.9 was redetermined to illustrate the cost estimate at a different point in the production using computed T375 values. The results of redetermining the regression are shown in Equation (6): lnT375 = -10.4 + 1.162 lnritf + 0.482 ab + 0.262 lndrywt (6) (3.745) (4.894) (2.55) where R-squared = 0.9158, adjusted R-squared = 0.9032, RMSE = 0.13356, and where lnT375 is the natural log of the production price for unit number 375 in millions of 2001 dollars. APPLYING THE RESULTS: A NOTIONAL EXAMPLE We now discuss how the results of this study, summarized in Table 6.10, can be applied to a notional future engine. To illustrate the use of the cost and time estimating relationships presented in this chapter, we now consider two preliminary aircraft designs, each of which employ a single afterburning engine for a single-engine fighter/attack aircraft. The first engine is an advanced derivative of an existing engine; the other engine is also derivative, but it employs more evolutionary technological advances. Table 6.11 describes the parameters of each engine.PDF Image | Military Jet Engine Acquisition Technology Basics and Cost-Estimating Methodology
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