Incremental harmonic balance method for analysis of standard/smart vehicles-to-rigid barrier frontal collision
Bibliographic record
Abstract
The aim of this paper is to investigate and to enhance crashworthiness of frontal barrier impact using a new idea of crash improvement. Two different types of smart front-end structure are proposed to support the function of the existing vehicle. The work described includes developing and analysing mathematical models of vehicle-to-rigid barrier full and offset frontal collision events for the two types of smart front-end structure. In these models, vehicle components are modelled by lumped masses and nonlinear springs. Moreover, the hydraulic cylinders are represented by non-linear damper elements. In this paper, the dynamic responses of the crash events are obtained with the aid of analytical approach using the Incremental Harmonic Balance Method (IHBM). In addition, the intrusion injury and occupant deceleration are used for interpreting the results. It is demonstrated from simulation results that significant improvements to both intrusion and deceleration injuries are obtained using the smart front-end structures.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".