Multidisciplinary design optimization of a zero-emission vehicle chassis considering crashworthiness and hydroformability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research used multidisciplinary design optimization to optimize the ladder frame chassis of a zero-emission vehicle by simultaneously considering three objective functions: (a) chassis mass, (b) deceleration during collision, and (c) manufacturability of a part in hydroforming. Additionally, design constraints were placed on torsional and bending stiffness, maximum von-Mises stress, and the natural frequency in torsion and bending. Optimization was completed in a three-phase approach: phase one used a simplified chassis model to conduct topology optimization with genetic algorithms; phase two was conducted to determine an optimum cross-sectional type and shape; and phase three incorporated results from phases one and two, into a high-fidelity, three-dimensional chassis model, for gradient-based optimization. Results from all phases of the design optimization indicated that improvements could be made over the baseline configuration. Through examination of Pareto frontiers in phase three, distinct trade-offs were identified between all objective functions: a 5 per cent reduction in chassis mass was required to maximize hydroformability; to minimize mass required a 90 per cent increase in deceleration; and minimization of deceleration required an 18 per cent decrease in hydroformability. Tri-objective optimization was used to generate a three-dimensional Pareto frontier ‘surface’ to show the impact of one objective function on all others simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it