BlowView simulation tools for design and optimization of thermoplastic liner in hyperbaric on-board hydrogen storage tanks for mobility
Bibliographic record
Abstract
A key challenge to the widespread commercialization of fuel cell electrical vehicle, is to design compact and cost effective on-board Compressed Gaseous Hydrogen tanks which store sufficient quantities of H2 without sacrificing passenger and cargo space. The first generation of FCEVs use 700 bar Type IV pressure vessels to store hydrogen. These vessels have a cylindrical BMPL, overwrapped by carbon-fiber composite material to maintain the internal pressure, which serves as a hydrogen gas permeation layer. However, due to its small molecular size, H2 permeates through the plastic liner wall. This represents a serious issue that should be addressed early in the design stage in order to minimize H2 emissions from the liner and conform to legal safety requirements and standards. Meanwhile, automotive OEMs and their suppliers are being challenged to design longer and thinner liners with very consistent wall thickness. One way to meet the hydrogen permeation rate requires a judicious choice of liner material. In the thermoplastic forming industry, it is still common practice to rely on trial and error to find the appropriate barrier layer configuration/thickness required to meet the permeation rate limit requirement. A tool offering a more efficient alternative, based on reliable predictive/virtual analysis of the H2 diffusion through the BMPL wall, could significantly shorten the design/development cycle by allowing product prototypes to be analyzed and tested virtually. A finite element based model that could help a designer better understand barrier layer properties was integrated in the latest version of NRC's BlowView software. The mathematical diffusion model adopted is based on Fick's diffusion law to predict H2 diffusion through a polymeric wall. Promising results, in terms of H2 permeation rate on an industrial BMPL, will presented during the presentation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".