Gas Turbine Engine Performance Estimation Using Model-Based System Engineering

Abstract One of the key requirements for aerospace gas turbine engines is to perform efficiently across the dynamic thrust requirements during a given flight and changing atmospheric conditions. While the most accurate understanding of the dynamic behavior of the engine can only be developed when it is already built, systematic estimation of the dynamic performance of the engine at the concept design stage would lead to significant savings in the cost and time of the design. Given the recent focus of the gas turbine industry on the digitalization of the design process to connect the design evolution to the main requirements, perform trade studies, and optimize the design choices, the digital representation of the gas turbine engine has become essential. In the present work, a system architecture model (SAM) for a high bypass turbofan aero engine is developed. It includes the requirements for the engine, including both performance and emission requirements. A functional decomposition of the model is then carried out and the functions are allocated to different physical forms of the turbofan engine that include an inlet diffuser, low-pressure compressor, high-pressure compressor, burner, high-pressure turbine, low-pressure turbine, fan, core nozzle, and the fan nozzle. Behavior and parametric models are created to analyze the system configuration performance in terms of the specific trust (Ts), thrust-specific fuel consumption (TSFC), and emission characteristics, mainly the NOx emissions for different sets of operating parameters based on flight conditions like take-off, cruise, approach, landing, and taxi. The overall model consists of two main representations, (i) the SysML model capturing the design requirements, the block definition diagram of the turbofan structure, and the parametric diagrams, and (ii) Analytical models for parametric and performance analysis. SysML model and analytical models are connected through a model-based system engineering (MBSE) tool that enables the understanding of the effect of change in any parameter on the overall performance of the engine and ensures the design requirements are always met. The model is validated using a landing-take-off (LTO) cycle for General Electric’s GE90-94B engine. The predictions from the model compare very well with the test data available in the open literature.