Development of a modular modeling tool for gas turbine steady state performance predictions

Abstract

Gas turbine engines are fundamental propulsion and power generation systems widely employed in critical fields including aviation, energy production, and marine applications. The high cost of physical testing during the design and operation of these engines has made the use of numerical performance prediction tools an indispensable part of the development process. In particular, modular and flexible simulation tools that enable the rapid and reliable evaluation of different engine architectures are essential during the preliminary design phase. In this thesis, an object-oriented gas turbine steady-state performance simulation tool has been developed in the C++ programming language. The tool represents each engine component as an independent, self-contained class, allowing the user to construct arbitrary engine architectures by connecting components in series or parallel arrangements. The tool can be compiled and executed on any operating system without requiring any commercial software licenses or external runtime dependencies. The thermodynamic calculations are based on a gas model that evaluates gas properties from tabulated data as a function of temperature and fuel-to-air ratio, rather than relying on the constant specific heat ratio assumption. This approach accurately captures the variation of gas properties at the elevated temperatures encountered in the combustion and turbine sections of the engine. The International Standard Atmosphere is employed as the ambient model, and a temperature deviation parameter is included to allow the simulation of non-standard day conditions such as hot or cold day operations. The on design and off design analyses of compressor and turbine components are based on generic performance maps anchored to the user-specified design conditions through scaling factors. At the design point, scaling factors are computed from the user-defined pressure ratio, isentropic efficiency, and spool speed, and stored within the component. During off-design operation, these factors are applied to evaluate the component maps at the current operating conditions. The cycle equations are solved iteratively using a Newton-Raphson based cycle matching solver, in which independent and dependent variable pairs are defined separately for each engine architecture and analysis mode. The developed tool has been validated against GasTurb 11 at the design point for both a single-spool turbojet and a two-spool mixed-flow turbofan engine architecture. Station-by-station comparisons demonstrated that mass flow deviations remain below 0.05% and total pressure deviations below 0.1%. The observed discrepancy in specific fuel consumption of approximately 2% is attributed to differences between the gas models employed by the two tools, specifically the fact that the present tool accounts for dissociation effects in the combustion products whereas GasTurb 11 employs a simplified gas model that neglects dissociation. Following validation, part-power throttling and flight envelope analyses were conducted using the two-spool mixed-flow turbofan model. In the throttling analysis, the movement of component operating points across their respective performance maps was examined as the corrected LP spool speed was reduced from the design value, and the characteristic hook-shaped net thrust versus specific fuel consumption curve was obtained. In the flight envelope analysis, engine performance was evaluated across a grid of altitude and Mach number combinations spanning from sea level to 14,000 m and from Mach 0 to 0.9. The predicted trends in net thrust, fuel flow, and component temperatures were found to be consistent with the expected physical behavior of this class of engine, confirming that the tool provides reliable and physically meaningful off-design performance predictions. The modular architecture of the tool enables straightforward integration of new component models, alternative gas property formulations, and additional engine architectures. Future development directions include the implementation of fan stratification modeling, Reynolds number correction factors for component maps, and transient performance simulation capabilities.