Efficiently assessing fan blade-off structural response in early-stage aircraft design

Abstract

The development of future aircraft concepts will require a decisive departure from conventional designs to meet ambitious environmental and performance goals. Catastrophic events, such as a Fan Blade-Off (FBO), can have a major influence on overall aircraft design. However, detailed simulations of FBO events are computationally expensive, and high-fidelity engine models often depend on detailed design data that may not be available during early design stages. This creates a barrier to efficiently evaluating new aircraft concepts.

A computationally efficient mid-fidelity model has been developed to capture the key physics during the initial phase of an FBO event. The objective is to demonstrate a methodology capable of rapidly assessing multiple early-stage design concepts of aircraft powerplants with sufficient accuracy while maintaining manageable computational cost.

At early design stages, highly accurate predictions are neither strictly necessary nor realistically achievable, given the limited availability of detailed design data that will only emerge later in the design cycle. Instead, the primary objective of analysis at this stage is to provide credible indications of engine behaviour that are sufficiently informative to guide future design decisions. A model that can reliably capture the dominant physical responses and predict key performance metrics within the expected order of magnitude is therefore appropriate for early-design stages. The purpose of the model developed in this work is not to deliver precise predictions, but to provide meaningful insight into system behaviour at a stage where such information would otherwise be unavailable.

To investigate suitable methodologies, an engine model was developed in LS-DYNA that represents a large commercial aircraft powerplant. The model was benchmarked against theoretical estimates and published literature results. Key performance metrics, including bearing loads, displacements, and mount reactions, were all within the expected order of magnitude when compared to the literature,
confirming that the modelling approach is appropriate.

Date of Award	Dec 2026
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Rolls Royce PLC
Supervisor	Declan Nolan (Supervisor) & Damian Quinn (Supervisor)