An inverse analysis method for determining abradable constitutive properties

Lye, Ryan; Bennett, Chris; Rouse, James; Zumpano, Giuseppe

Abstract

Abradable coatings enable small tip clearances within gas turbine engines to be achieved. These coatings allow blades to cut their ideal paths during engine running-in and act as a sacrificial layer during unforeseen blade-casing interactions, minimising any damage to the blades. Abradables are often plasma sprayed and as a result, a given abradable can have a wide range of properties which are defined by its composition and the spray process parameters. These properties are also known to evolve during blade-casing interactions as a result of heating and compaction. In industry, abradables are often characterised by a superficial Rockwell hardness value; however, it is not clear how the Rockwell hardness relates to the mechanical properties of the abradable or whether this relationship is unique. An inverse methodology is presented for obtaining these properties via simulated Rockwell hardness testing. Firstly, a neural network (NN) is trained using the simulated Rockwell tests, which is then used in conjunction with a particle swarm optimisation (PSO) to estimate abradable properties for a given hardness value. These properties, determined from the optimisation process, are then used to conduct a series of blade-casing interaction simulations, demonstrating how the contact forces and dominant frequencies differ during rub events. This work provides a methodology to rapidly estimate abradable properties over their full range of acceptable hardnesses, which can in turn be used to optimise specific blade geometries and abradable hardnesses to produce optimal compressor performance and blade life.

Citation

Lye, R., Bennett, C., Rouse, J., & Zumpano, G. (2022). An inverse analysis method for determining abradable constitutive properties. Materials Today Communications, 33, Article 104571. https://doi.org/10.1016/j.mtcomm.2022.104571

• Journal Article Type: Article
• Acceptance Date: Sep 27, 2022
• Online Publication Date: Sep 29, 2022
• Publication Date: Dec 1, 2022
• Deposit Date: Oct 18, 2022
• Publicly Available Date: Oct 24, 2022
• Journal: Materials Today Communications
• Electronic ISSN: 2352-4928
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Article Number: 104571
• DOI: https://doi.org/10.1016/j.mtcomm.2022.104571
• Keywords: Materials Chemistry; Mechanics of Materials; General Materials Science
• Public URL: https://nottingham-repository.worktribe.com/output/12614432
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S235249282201412X