Efficient multi-scale modelling of viscoelastic composites with different microstructures
Bibliographic record
Abstract
Modern composites such as advanced fibre-reinforced and strand-based wood composites are increasingly being used in the new generation of aerospace and civil engineering structures. The structural analysis of such composites often requires knowledge of their effective (homogenized) properties. Several micromechanical models have been developed and are available in the literature for predicting the effective elastic properties of fibre-reinforced solid composites. However, the underlying assumptions in these models somewhat limit their application in solving some practical problems related to the viscoelastic behaviour of composite materials. Two seemingly different classes of composites, i.e. thermoset fibre-reinforced composites and strand-based wood composites with distinct viscoelastic properties are considered in this work due to their wide application in aerospace and construction industry. For viscoelastic analysis of such materials, aspects which require further investigations at the micro-scale are identified first. Specifically, available analytical micromechanics models are extended to predict the shear properties of thermoset fibre-reinforced composites during cure where the resin evolves from a viscous fluid to a viscoelastic solid. For strand-based composites consisting of high volume fraction of orthotropic wood strands, analytical micromechanics models are developed. These models are employed for predicting the effective elastic and viscoelastic properties of strand-based composites. The validity ranges of these models are then examined using experimental data or numerical reference solutions that employ the computational homogenization technique. To enable viscoelastic analysis of large scale composite structures with generally orthotropic properties, an efficient and easy-to-implement approach in the context of 3-D multi-scale modelling, is presented. A multi-scale modelling framework involving analyses at different scales for composites with two difference microstructures is developed and implemented in a general purpose finite element code, ABAQUS®. The accuracy of the developed multi-scale approach is demonstrated for some practical applications involving MOE (apparent modulus of elasticity in bending) prediction of strand-based wood composites. Using this approach, the effect of microstructural parameters (e.g. fibre geometry, orientation, waviness, volume fraction, etc.) on the time-dependent macroscopic response of orthotropic composite structures can be investigated, quantitatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".