1 Copyright© 2003 by Roth et al. Published by the American Institute of Aeronautics and Astronautics Inc., with permission. Estimation of Turbofan Engine Performance Model Accuracy and Confidence Bounds Bryce A. Roth, Dimitri N. Mavris School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA 30332-0150 and David L. Doel GE Aircraft Engines Cincinnati, OH 45215 Abstract This paper explores the application of Inference and Bayesian Updating principles as a means to efficiently incorporate probabilistic data into the turbine engine status model matching process. This approach allows efficient estimation of nominal model match parameters from test data and also enables quantification of model accuracy and confidence bounds. The basic concepts are developed in detail and formulated into a status matching approach. This method is then applied to a simple surrogate matching problem using a cantilever beam matching exercise to illustrate the methods in a clear and easy-to-understand way. Typical results are presented and are directly analogous to status matching of a gas turbine engine cycle model. Introduction This paper focuses on developing improved methods for matching high-accuracy engine performance models to test data. The chief motivation for this work is the need to make model predictions match test data as closely as possible and to do so in the minimum possible time and cost. The basic engine performance matching problem is complicated by the fact that the test data has inherent uncertainty which must be accounted for in the matching process. Furthermore, the data typically comes from many sources, each with a varying degree of relevance (legacy test data versus new test data, for instance). Appropriate weight must be apportioned to each piece of data according to its relevance to the current engine. Finally, some parameters must be matched with greater precision than others. Challenges are also presented by continued increases in engine complexity (particularly with regard to control laws), and the need to model an ever-increasing number of phenomena that impact engine performance. The confluence of these factors is a clear and present need to develop rigorous, efficient, and methodical approaches to matching model predictions to test data. While it is important that the data match be as accurate as possible, it is equally important to be able to quantify the accuracy of the match. Engine performance models are used throughout the design process and impact significant decisions regarding the design and specific commitments to customers. A failure to appreciate the accuracy of the performance model can cause customer commitments to be unattainable or, conversely, to be overly conservative. Similarly, the same issues can lead to overly conservative designs or to field problems resulting from optimistic assumptions about the conditions an engine component will experience in service. Thus, a second ingredient of a useful engine performance model is the ability to specify its uncertainty. A Typical Status Matching Approach A general feature of status matching is that the problem becomes more complex and the flexibility greater as more data is available to be considered. The easiest case is a production status match where there are many engines, but few parameters to be matched. Another thing that makes the production status match simpler is that all the data is at sea level static conditions. The goal of a production status match is to simultaneously match thrust at fan speed, core speed at fan speed, specific fuel consumption (SFC) at thrust and exhaust gas temperature (EGT) at fan speed. Although internal pressures and temperatures are available, they are normally single element probes (control sensors) and are not considered in the matching process. Typically, the first step is to match thrust at fan speed. This is done by varying the fan pumping (air flow at fan speed) until a thrust match is achieved. In this, or any subsequent step, a reasonableness check is performed to make certain that the resulting map makes sense. This might involve an observation that very little change from the previous representation was required to achieve the match or it could involve a review with the appropriate aero designer or other expert(s). Step two is to match core speed at fan speed. This will usually be done by adjusting the high pressure compressor flow at speed characteristic. If either high pressure or low pressure turbine flow functions have changed (because of a deliberate design decision), they ISABE 2003-1208