Extension of U* Index Theory to Nonlinear Case of Load Transfer Analysis
Bibliographic record
Abstract
Load transfer analysis has been proved to be an effective approach for designing light weight vehicle structures in last two decades. There are two main procedures for predicting the load path in a vehicle: The stress trajectory method and the U* index theory. The first approach has shown some shortcomings in dealing with geometrical irregularities. As a result, automotive industries have mainly applied the U* index as a design tool to study the load transfer behavior in the vehicle structure. The U* index, is an indicator for the load transfer in the structure, i.e. higher U* index value indicates more significant role in the load transfer process. So, the distribution of the U* index in the structure can be used to predict the main load path in the structure. Nevertheless, this foundation of this theory is based upon the linear elasticity equations and consequently, it has always been limited to linear elastic problems in static or quasi static conditions. Eradicating this limitation and extending the U* Index theory to nonlinear elastic problems is the main objective of this study. An extension to nonlinear criteria for U* index theory is proposed in this paper. It is shown, for the very first time, that the extended nonlinear load transfer index (U*NL) is a true measure for the load transfer in the structure in a nonlinear elastic problem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".