Development of the HyStEP Device
Bibliographic record
Abstract
With the introduction of more fuel cell electric vehicles (FCEVs) on U.S. roadways, especially in California, the need for available hydrogen refueling stations is growing. While funding from the California Energy Commission is helping to solve this problem, solutions need to be developed and implemented to help reduce the time to commission a hydrogen station. The current practice of hydrogen station acceptance can take months because each vehicle manufacturer conducts their own testing and evaluation. This process is not practical or sufficient to support the timely development of a hydrogen fueling station network. To address this issue, as part of the Hydrogen Fueling Infrastructure Research and Station Technology (H2FIRST) Project Sandia National Laboratories and the National Renewable Energy Laboratory along with a team of stakeholders and contractor Powertech Labs has developed the Hydrogen Station Equipment Performance (HyStEP) Device. The HyStEP Device is intended to be a surrogate for FCEVs that can be used to collect data on hydrogen station fueling performance. The device includes three Type IV 70 MPa tanks capable of storing a total of 9 kg H2 that are instrumented with pressure and temperature sensors. The tanks can be used individually or in parallel to simulate small, medium, and large fuel systems. The tanks are connected to a 70 MPa receptacle equipped with pressure and temperature sensor as well as infrared communications integrated with a data acquisition, analysis, and control system. The HyStEP Device is capable of performing tests defined in the test method standard CSA HGV 4.3 and providing the data needed to ensure that hydrogen stations meet the fueling protocol standard SAE J2601-2014. These include IrDA communication tests, fault detection tests, and communication and non-communication fueling.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.021 | 0.013 |
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".