Adaptive simulation technology enables the calibration of a performance simulation code to a given in-service gas turbine and provides correct prediction of its performance. This is a fundamental prerequisite for reliable gas-path diagnostics and performance health monitoring. In this paper, a new offdesign performance adaption algorithm is introduced. Cranfield University's consolidated engine performance simulation code PYTHIA is enhanced with the capability of offdesign performance adaptation to model available field data. The software minimizes, via a genetic algorithm, an objective function that measures the error between an initial engine model output and the real engine data by varying some characteristics' scaling factors. In this study, a multiple-point adaptation procedure was applied to a two-shaft aeroengine. This generated an optimized engine model that minimized its deviations from a set of test-bed data. The adapted model was then tested against different real data, resulting in an average error, over 8 measured parameters, of less than 0.35%. Nomenclaturea = weighting factor ETA = isentropic efficiency K = number of measurement N1 = relative low-pressure shaft speed, % N2 = relative high-pressure shaft speed, % n = number of offdesign points OF = objective function P = pressure, atm P = measurable performance-parameter vector PR = pressure ratio SF = scaling factor T = temperature, K u = ambient and operating-condition vector WAC = corrected mass flow rate, kg=s X = component-characteristics vector Subscripts amb = ambient DP = design point ETA = isentropic efficiency N = relative shaft speed OD = offdesign PR = pressure ratio WAC = corrected mass flow rate, flow capacity 0 = design point 6 = low-pressure compressor exit 8 = high-pressure compressor exit 11 = high-pressure turbine exit 15 = outlet fan turbine exit Superscript def = default
paper for one flight Mach number and angle of attack setting. More importantly, this research effort established the necessary methodology and technology required towards a full, two-dimensional engine cycle analysis at an affordable computational resource in the very short term. NOMENCLATURE AbbreviationsAoA angle-of-attack BPR bypass ratio CFD computational fluid dynamics CMF corrected mass flow DLL dynamic link library ETA isentropic efficiency GT gross thrust LES large-eddy simulation LBR low by-pass ratio OPR overall pressure ratio ABSTRACT In commercially available gas turbine performance simulation tools, individual engine components are typically represented with nondimensional maps of experimental or default data. In those cases where actual component characteristics are not available and default characteristics are used instead, conventional tools can deviate substantially at off-design and transient conditions. Similarly, when real component characteristics are available, conventional engine cycle simulation tools can not predict the performance of the engine at other than nominal conditions satisfactorily, or account for the impact of changes in component geometry.This study looked into the full integration of two-dimensional streamline curvature component models with a low fidelity cycle program. Firstly, the obtained engine performance was compared against the one calculated based on default component characteristics. As a second case study, a range of flight Mach numbers and angles of attack were examined together with the effect of three different intake lip geometries on the performance of a notional, two-spool, lowbypass ratio, military engine. Two-dimensional models were used in the engine cycle analysis to provide a more accurate, physics-and geometry-based estimate of intake and fan performances.The analysis carried out by this study demonstrated relative changes in the predicted engine performance larger than 1%. For briefness, representative results are presented and discussed in this THE AERONAUTICAL JOURNAL
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