A novel system of parametric analytical modeling techniques for transversely loaded fibre reinforced polymer structural members of variable material properties and cross-sectional geometry
Bibliographic record
Abstract
A broad class of structural problems has been recognized as being non-conducive to the use of conventional state-of-the-art structural analysis techniques involving the finite element method. This class of problems includes long fibre reinforced polymer structural members having non-uniform, continuously variable material properties and cross-sectional geometry. A novel composite leaf spring is being developed (Thunder Composite Technologies, Ltd.), which will likely prove to be superior to the conventional leaf springs that it aims to replace. However, this new spring belongs to the aforementioned class of problems and will therefore necessitate unconventional and highly complex design and analysis techniques due to the anisotropy and non-homogeneity of its constituents. As such, a design and analysis software tool was developed to be capable of collecting user stipulated parameters and performance specifications, generating a design that is capable of meeting these requirements and performing a high-fidelity structural analysis on the resulting design in order to verify its performance. The following paper summarizes the engineering science and programming methodologies employed by this design tool and discusses how similar methodologies could be employed in the design and analysis of other structural members that reside within this class of problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".