Weight estimation for pylons supporting large aero-engines

Weight estimation for pylons supporting large aero-engines: A KBE approach

Snelders, Jeffrey (TU Delft Aerospace Engineering)

Vos, Roelof (mentor)
Hoogreef, M.F.M. (graduation committee)
Wang, Xuerui (graduation committee)

Delft University of Technology

Aerospace Engineering | Flight Performance and Propulsion

2023-08-31

The aviation industry confronts challenges from increased air traffic and environmental concerns. Enhancing engine efficiency becomes crucial due to persistent greenhouse gas emissions from air travel, particularly in long flights with high CO2 emissions. Higher by-pass ratios (BPR) can address this by boosting engine propulsive efficiency, yet integrating modern large ultra-high bypass ratio (UHBR) engines onto the aircraft introduces complexities affecting structural weight and aerodynamics. To evaluate innovative engine-aircraft integration concepts, precise early-stage design and weight assessment methods are needed. Existing conceptual weight estimation methods for pylons lack the ability and precision to do so, as they mainly rely on statistical methods applied to previous generation engines which do not represent current trends. This thesis proposes a physics-based design approach for pylon structures using Knowledge-Based Engineering (KBE) principles, enhancing the evaluation of diverse engine-aircraft integration designs. The proposed methodology revolves around 4 key ingredients. First, the study introduces a completely new parameterization of the pylon structure involving the mathematical description of 'zero-thickness geometry' and structural elements, which leads to a total of 80 parameters to characterize the pylon structure.
This parameterization is implemented in a ParaPy Python application, featuring the 'PylonDesigner' superclass that controls the geometry generation process, performs a weight evaluation process, and contains dedicated attributes and functions for structural analysis and sizing optimization. Specialized classes are implemented to create the geometry of different pylon types using the ParaPy Geometry library. The generated pylon structural geometry is analyzed using the commercially available finite element code Abaqus. To enable a proper coupling between the ParaPy and Abaqus, an application programming interface (API) has been implemented. Using this API, the meshed pylon geometry is processed part-by-part, after which the full structure is assembled. Boundary conditions are then applied, and the analysis is defined including the loads. During the analysis, the pylon is subject to a total of 20 limit loads cases covering different maneuvers, thrust settings and gusts, and 4 ultimate load cases representing the critical fan-blade off event. The results from the structural analysis in Abaqus and a weight evaluation procedure using the geometry in ParaPy are used as inputs for a sizing optimization procedure making use of the Scipy Optimize Sequential Least Squares Programming (SLSQP) algorithm. The objective of this optimization is to minimize the structural weight of the pylon, while subject to constraints on the maximum allowable stress in each component. Validation utilizes two engine-aircraft integration cases: the pylon supporting LEAP-1B engine on Boeing B737-MAX and LEAP-1A engine on Airbus A320 neo. The method approximates pylon weight effectively when employing a 'FEM weight-to-realistic weight' conversion factor. In conclusion, this methodology holds potential in assessing UHBR turbofan engine design and weight penalties, primarily for wing-mounted engines using box-beam structures. Further development is required to address validation challenges and explore various pylon architectures, extending the model to fuselage-mounted struts, and integrating rotor dynamic simulations. Coupling with an engine model shows promise for evaluating variable engine design and integration parameters.

Weight estimation
Structural design
Engine integration
Knowledge Based Engineering
Preliminary aircraft design
Pylon design
Abaqus
ParaPy
Parametric design

http://resolver.tudelft.nl/uuid:656f89ab-869c-45d5-a6f2-e488d5f4a77b

Student theses

master thesis

© 2023 Jeffrey Snelders