An Effective Computational Parametric Appraoch for Optimization of Adhesively Bonded Tubular Joints Subjected to Torsion Loading
Bibliographic record
Abstract
Due to their low manufacturing cost, low stress concentration and ease of maintenance, adhesively bonded joints are now one of the most commonly and widely used joining systems in various industrial applications. As the use of composites gains popularity in oil and gas industry, the use of such joints for joining composite pipes is also gaining demand. The design and analysis methodologies applied to these joints under different loading conditions are however non-standard and rather controversial. The inherently complicated equations governing the behaviour of these joints have also impeded their use among the design engineers. As stated, however, as the use of composite pipes gains more popularity in oil and gas industry, the need for standardization of the methodology used for designing such joints becomes more essential. This paper discusses the details of 2D axis-symmetric and full-3D finite element models developed using the ABAQUS commercially available FEM software [1] for modeling and characterizing a series of adhesively bonded tubular joints used in isotropic and orthotropic pipes. The parametric script module of ABAQUS was used to systematically investigate the influence of several design parameters (such as the adhesive thickness, joint length, joint diameter, pipe material, and loading conditions), which govern the performance of such joints. The influence of various parameters specific to composite pipes (including the effect of laminate stacking sequence) was also investigated. Generated from the investigations was a set of useful design curves that provide the relationships among the parameters governing the behaviour of the joints. An important feature of the approach is its ability to establish the most optimized and effective joint length. The integrity of the optimization procedure was evaluated by comparing the response of the joints designed based on the developed design curves with those analyzed in detail by the finite element method (FEM).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".