Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

Surya N. PatnaikOhio Aerospace InstituteCleveland, Ohio 44142James D. Guptill, Dale A. Hopkins, and Thomas M. LavelleNational Aeronautics and Space AdministrationGlenn Research CenterCleveland, Ohio 44135SUMMARYThe NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbineengine that can be generated through the interconnection of a set of standard physical components. In addition, thecode can optimize engine performance by changing adjustable variables under a set of constraints. However, forengine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in thecurrently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver duringengine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through acascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neuralnetwork and linear regression methods. An approximation-interspersed cascade strategy was used to optimize theengine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved theoptimization segment of the NEPP code. The performance of the linear regression and neural network methods asalternative engine analyzers was found to be satisfactory. This report considers two examples--a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine--to illustrate the results, and it discusses insightsgained from the improved version of the NEPP code.INTRODUCTIONThe NASA Engine Performance Program (NEPP) is a gas-turbine engine-cycle simulation code. This code canconfigure and analyze almost any type of gas turbine engine that can be generated through the interconnection ofa set of standard physical components: propeller, inlet, ducts, combustor, fan, compressors, turbines, shafts, heatexchangers, flow splitters, subsonic mixers and/or supersonic ejectors, nozzles and water injectors or gas generators.The engine can he designed for different types of fuels: standard hydrocarbon jet fuel and cryogenic fuel and slurries.For thermodynamic analysis, built-in curve fits can be generated from empirical data available in NEPP. For theanalysis of jet and rocket fuels, an auxiliary chemical equilibrium composition model is available (ref. 1). The NEPPcode has been successfully used to simulate a wide range of engines from turboshaft and turboprops to airturbo-rockets and supersonic variable-cycle engines. A description of the NEPP program, with typical input files for a setof engine configurations, is given in references 2 and 3. Since its inception (ref. 4), the NEPP program has beencontinuously undergoing improvement to keep pace with the advanced gas turbine engines envisioned for the 21 stcentury. NEPP simulation has decreased engine cycle analysis time and improved engine model fidelity.The NEPP code has a numerical optimization capability to increase engine performance. The program allowsthe maximization or minimization of a cost function for a set of independent variables subjected to a number ofspecified behavior parameters of the engine, which act as the constraints. In the NEPP code, the resulting optimiza-tion problem is solved using Powell's method (ref. 5), which was developed in the early sixties. It has been observedthat for certain engine problems Powell's method can produce an overdesign condition with fewer active constraintsthan the correct optimum solution or can experience convergence difficulties. A project was undertaken to correctthe optimization-related deficiency in the NEPP code by augmenting it with a cascade optimization strategy that wasNASA/TM----2000-209177 1