Abstract
This paper proposes a progressive damage model incorporating strain and heating rate effects for the prediction of composite specimen damage resulting from simulated lightning strike test conditions. A mature and robust customised failure model has been developed. The method used a scaling factor approach and non-linear degradation models from published works to modify the material moduli, strength and stiffness properties to reflect the effects of combined strain and thermal loading. Hashin/Puck failure criteria was used prior to progressive damage modelling of the material. Each component of the method was benchmarked against appropriate literature. A three stage modelling framework was demonstrated where an initial plasma model predicts specimen surface loads (electrical, thermal, pressure); a coupled thermal-electric model predicts specimen temperature resulting from the electrical load; and a third, dynamic, coupled temperature-displacement, explicit model predicts the material state due to the thermal load, the resulting thermal-expansion and the lightning plasma applied pressure loading. Unprotected specimen damage results were presented for two SAE lightning test Waveforms (B {\&} A); with the results illustrating how thermal and mechanical damage behaviour varied with waveform duration and peak current.
Original language | English |
---|---|
Journal | Applied Composite Materials |
DOIs | |
Publication status | Published - 11 Nov 2019 |
Keywords
- Progressive damage model
- Lightning strike
- Finite element analysis
- Composite damage
- Strain rate effects
- Heating rate effects
Fingerprint
Dive into the research topics of 'Coupled Thermal-Mechanical Progressive Damage Model with Strain and Heating Rate Effects for Lightning Strike Damage Assessment'. Together they form a unique fingerprint.Datasets
-
Spatial and temporal Waveform A and B loading and material data for lightning strike simulations based on converged FE Meshes
Millen, S. (Creator) & Murphy, A. (Owner), Queen's University Belfast, 08 Jun 2021
DOI: 10.17034/ef3ff864-78d3-4ce4-9c0f-fec7b4c408a0
Dataset
File