Session: 01-12 Electrified Propulsion and Novel Cycles II

Paper Number: 151447

A Comprehensive Simulation Approach for Maintenance Costs of Future Aircraft Engines Using the Example of Hybrid-Electric Propulsion 

Operating costs are an important aspect for flight operators and consist largely of engine maintenance costs. These will change and most probably form new patterns within the introduction of new disruptive engine architectures. For example, parallel hybrid-electric operation of turbofan engines, where the low-pressure spool is assisted by an electric motor, allows for electrically assisted take-off and climb procedures. While the required electric energy during these flight phases is comparably low, this allows for considerable peak shaving of the load profile. With typically highest loads occurring at take-off and being accountable for most degradation patterns propagating on a flight cycle basis for conventional turbofan engines, such altered aspects must be considered regarding future propulsion systems. In this paper a comprehensive approach for modelling maintenance costs of aircraft engines is presented. A holistic model environment is aimed for and consists of four submodels: Flight profile simulation, thermodynamic engine performance model, modular degradation model and maintenance costs model. This allows to capture aspects from both operational influences throughout the mission profile, e.g. take-off thrust reduction and climb rate, as well as technical variations in aircraft engine technology, with special emphasis on hybrid-electric applications. The model is set up on for the Airbus A320 aircraft propelled by IAE V2500 high bypass turbofan engines, which has a high impact within global flight movements and much data available in open literature for model calibration. With a high degree of variability this approach enables adaption to other airframes and more disruptive propulsion systems, with adequate data available.

An aircraft operation model is used to model flight trajectories that capture an average fleet behavior and interact with ambient conditions, aircraft weight and thrust setting. A machine learning model is used for generating kinematic flight trajectories from airport couples and atmospheric conditions retrieved from the Copernicus Atmosphere Monitoring Service (CAMS). The flight movement is then solved for required engine thrust from aircraft performance calculations.

The engine performance model solves the thermodynamic cycle of the turbofan with thrust and ambient conditions as inputs throughout the mission profile and reveals the true operating point of the engine. In this study an in-house performance model calibrated on the IAE V2500-A1 engine is used. Furthermore, the engine model is adapted for hybrid-electric application, where electric power is added to one of the turbomachinery spools from an electric motor. This has an effect on thermal loading of the engine and, depending on the operating strategy, can substantially change the operating profile of a flight mission, where highest loads no longer occur during take-off.

The operating parameters of the engine are used to compute thermal and mechanical loads. Additionally, ingested contamination mass flows are calculated with the air mass flow of the engine and air pollutants from atmospheric data, such as dust, salt and Sulphur. The data is used to compute the operational severity of the flight and its effect on performance degradation of turbomachinery. To do so, a parametric model is set up and calibrated on the performance degradation of real engines extracted by non-linear gas path analysis and complemented by literature data. Until here, the model is capable of generating synthetic operational data for real or any arbitrary flight network, with trends of exhaust gas temperature similar to real engine data.

Eventually, cost data from literature is processed to estimate condition-based maintenance costs on a modular basis. Simulations are performed for flight networks with both conventional and hybrid-electric propulsion systems and are compared so that expected changes in service life, maintenance intervals and maintenance cost are quantified.

Presenting Author: Maximilian Bien Technische Universität Braunschweig

Presenting Author Biography: Maximilian Bien was born in Hannover, Germany in 1993. 2016 he received his B.Sc. degree in Mechanical Engineering at the Gottfried Wilhelm Leibniz Universität Hannover. In 2019, he completed his M.Sc. in Aerospace Engineering at the Technische Universität Braunschweig. Then, he started working on his PhD at the Insitute of Jet Propulsion and Turbomachinery (IFAS). There, his research involves modelling aircraft engine performance, degradation and maintenance with a particular focus on future hybrid-electric propulsion systems.

Authors:

Maximilian Bien Technische Universität Braunschweig
Sebastian Lück Technische Universität Braunschweig
Jens Friedrichs Technische Universität Braunschweig
Jan Göing Technische Universität Braunschweig