Front Underride Protection Devices (FUPDs): Multi-Objective Optimization
Bibliographic record
Abstract
This work investigates a multi-objective optimization approach for minimizing design parameters for Front Underride Protection Devices (FUPDs). FUPDs are a structural element for heavy vehicles to improve crashworthiness and prevent underride in head-on collision with another vehicle. The developed dsFUPD F9 design for a Volvo VNL was subjected to modified ECE R93 testing with results utilized in the optimization process. The optimization function utilized varying member thickness to minimize deformation and system mass. Enhancements to the function were investigated by introducing variable materials and objectifying material cost. Alternative approaches for optimization was also needed to be explored. Metamodel-based and Direct simulation optimization strategies were compared to observe there performance and optimal designs. NSGA-II, SPEA-II Genetic Algorithms and Adaptive Simulated Annealing algorithms were under investigation in combination with three meta-modeling techniques. Leapfrog LFOPC algorithm hybridized forms of Genetic Algorithms and Adaptive Simulated annealing was also investigated. Crash worthiness of the optimal design was analyzed through varying collision scenarios using FEA models of a Toyota Yaris and Ford Taurus in LS-DYNA with the aid of occupant compartment intrusion evaluations and collision compatibility profiles. Direct simulation optimization with a NSGA-II approach improved the dsFUPD F9 design. This approach reduced the system mass and cost, while maintaining the modified ECE R93 requirements and crashworthiness. In final, the addition of various materials and a cost objective to the multi-objective optimization function improved the minimization of search space and progression for optimal design. Future works should investigate material selection for crashworthiness and cost effectiveness for FUPDs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".