Connecting Microscale And Macroscale Damage Models In A Bayesian Framework for Fatigue Damage Prognostics Of CFRP Composites

Metadata Updated: February 28, 2019

Composites offer unique advantages for aerospace structures and are increasingly being adopted into newer designs. However, it is also acknowledged that given current understanding of damage mechanisms in composites there is a significant risk with the extensive use of composites materials in aerospace applications. On one hand the uncertainty in damage evolution along lifetime is extremely large, and on the other hand there is a lack of knowledge about the mechanics of the onset, posterior growth, and interactions between several micro-scale damage modes. All these factors lead to the adoption of high safety margins in the design and costly inspection schedules along the service to mitigate the risks. Structural health monitoring for onboard damage diagnosis and prognosis of structural failures has the potential to reduce maintenance costs and improve the safety of the structure through a condition based maintenance scheduling. In this scheme the current damage state of a specific structural element is estimated and further used as the input for a prognostic algorithm that predicts the propagation of damage through time using updated models and based on some knowledge of the future load conditions.

A novel damage prognostics framework for composites FRP under fatigue loadings is proposed in this work. The proposed methodology is grounded on physics-based models for evolution of damage at (1) micro-scale, i.e. micro-cracks and delamination, and (2) macro-scale such as stiffness reduction induced by micro-scale damage modes. Through stochastic embedding, these apriori deterministic models are converted to probabilistic models by introducing a modeling error term. This error term is controlled by a probability density function whose parameters are estimated in addition to the rest of "physical" parameters. The probabilistic damage models are then incorporated in a Bayesian filtering algorithm that sequentially updates both, a damage state variable and the set of model parameters, as fresh damage data become available along the fatigue cycling process. Next, these damage models are used to simulate fault propagation with this updated state information to generate a prognostic estimate of the remaining useful life of the structure in a probabilistic sense. The proposed methodology is demonstrated using experimental NDE damage data for micro-crack density, delamination area, and stiffness reduction from an extensive post-impact tension-tension fatigue test performed over several CFRP [0,90]4s laminates.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Data Update Frequency irregular

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Harvested from NASA Data.json

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Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Dashlink
Unique Identifier DASHLINK_854
Abhinav Saxena
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
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Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 353d7070-35de-48a1-a85d-1382ad7c200b
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2013-12-12
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash 3aeb29fb253d292a70339e0d5cfa88944ae7c68d
Source Schema Version 1.1

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