Page 1 985510 A Probabilistic Design Methodology For Commercial Aircraft Engine Cycle Selection Dr. Dimitri N. Mavris Assistant Professor and Manager, Aerospace Systems Design Laboratory (ASDL) Mr. Noel I. Macsotai and Mr. Bryce Roth NASA Multidisciplinary Analysis Fellows, ASDL Georgia Institute of Technology Copyright ' 1998 SAE, International, and the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. ABSTRACT The objective of this paper is to examine ways in which to implement probabilistic design methods in the aircraft engine preliminary design process. Specifically, the focus is on analytically determining the impact of uncertainty in engine component performance on the overall performance of a notional large commercial transport, particularly the impact on design range, fuel burn, and engine weight. The emphasis is twofold: first is to find ways to reduce the impact of this uncertainty through appropriate engine cycle selections, and second is on finding ways to leverage existing design margin to squeeze more performance out of current technology. One of the fundamental results shown herein is that uncertainty in component performance has a significant impact on the overall aircraft performance (it is on the same order of magnitude as the impact of the cycle itself). However, this paper shows that uncertainties in component efficiencies, pressure losses, and cooling flow losses do not have a significant influence on the variance of aircraft performance. This paper also shows that the probabilistic method is very useful for formulating direct trades of design margin against performance or other figures of merit such as engine weight, thus enabling the existing design margin to be capitalized upon in the interest of obtaining better system performance. In terms of a comparison between techniques, one can conclude that the probabilistic approach is inherently more computationally intensive that the deterministic approach. It therefore behooves the designer to choose wisely when setting up the problem in order to avoid unnecessary work. However, a properly formulated probabilistic method provides a much clearer picture of how the various system trades stack up against one another and enables the ultimate cycle selection to be analytically determined based on the level of risk that is consistent with program objectives. NOMENCLATURE AMV Advanced Mean Value CDF Cumulative Distribution Function CDP Compressor Discharge Pressure DoE Design of Experiments EPNLdB Equiv. Perceived Noise Level (decibels) FoM Figure of Merit FPI Fast Probability Integration FPR Fan Pressure Ratio HPT High Pressure Turbine KCP Key Control Parameters KNP Key Noise Parameters LP Low Pressure LPT Low Pressure Turbine OEW Operating Weight Empty, lb OPR Overall Pressure Ratio PQEXT Extraction Ratio (P16/P56, per SAE ARP755B) P success Probability of Success RSE Response Surface Equation RSM Response Surface Method SFC Specific Fuel Consumption, 1/hr TH41 Max Turbine Inlet Temp, o F (per SAE ARP755B) TOGW Takeoff Gross Weight, lb DP/P Pressure Loss, % m Mean Value s Standard Deviation INTRODUCTION The focus of this paper is to explore ways in which probabilistic design methods can be applied to the aircraft engine cycle design process in order to account for the uncertainty inherent in preliminary-level component performance estimates. The idea is that benefits can be garnered in two ways: first, probabilistic design techniques can be used to estimate uncertainty in performance of a particular design. Second, probabilistic methods can be used to leverage the design margin available in order to achieve better design performance with the same technology level. This paper will examine each of these aspects in detail as applied to a large commercial engine suitable to power a large (~800,000 lb) commercial transport. The focus of this text is on the development of probabilistic methods suitable for engine cycle selection, and these methods