Design-purpose numerical simulations of the NRC Heavy Impact Test Theater (HITT)
Bibliographic record
Abstract
The National Research Council of Canada, with its collaborating partners (Defence Research and Development Canada and the US Navy), are developing a new facility, known as the Heavy Impact Test Theater (HITT), that will be part of the Ice Tank facility at the NRC St. John’s location. This facility will enable full-scale, or near full-scale, ice / ship-grillage impact experiments in water, where damage would be imposed on the grillage. Here we present results from sufficiently elaborate numerical simulations intended to assist with the design of HITT. In general, the simulations include representations of the moving Ice Tank carriage and the large flooded impacting device (known as the ‘Hammer’), which hangs from the carriage on four chains/cables. When impact is involved, the simulations also include the hybrid ice / flooded-structure component (known as the Anvil). The simulations are useful for predicting starting and stopping loads on the carriage, lateral side loads on the carriage during the ice impact events, and formulating load-mitigating strategies where necessary. An underlying purpose of the simulations was to investigate the reliability of the simulation results when compared to actual half-scale HITT experiments (not involving impact) that were performed in NRC’s Large Towing Tank. Additionally, full-scale HITT simulations were conducted showing anticipated displacement and load behaviors associated with the Hammer impacting the Anvil. The simulations involving impact incorporate NRC’s validated crushable-foam ice model.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".