Kineto–static roll plane analysis of articulated tank vehicles with arbitrary tank geometry
Bibliographic record
Abstract
The roll stability of an articulated tank vehicle with partial liquid load is discussed from the viewpoint of fundamental mechanics of vehicle response and quantitative influence of size and weight variables. A kineto–static roll plane model for a partially filled tank vehicle of arbitrary tank geometry is developed incorporating the moments and forces associated with liquid movement within the tank. A roll plane model for a partially filled tank of arbitrary shape is developed and integrated with the static roll plane model of the vehicle. The influence of liquid motion within the tank during a steady turning manoeuvre is investigated. The rollover immunity of the tank vehicle is investigated through computer simulation. The vertical and lateral translation of the fluid bulk during steady turning is computed using an iterative algorithm. The corresponding roll moments and forces arising due to liquid motion are incorporated into the static roll model to study the rollover immunity levels of liquid tank vehicles. The influence of tank geometry and liquid fill level on the rollover immunity of the tank vehicles is presented. The rollover threshold levels of the tank vehicle are compared to that of an equivalent rigid cargo vehicle for various loading conditions. The influence of compartmenting of the tank and the influence of the location of the trailer axles on the rollover immunity levels is studied and an optimal order of unloading the various compartments is determined.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".