A Fuel Quality Sensor for Fuel Cell Vehicles, Natural Gas Vehicles, and Variable Gaseous Fuel Vehicles
Bibliographic record
Abstract
There are many possible applications for the discrete acoustic wave and phase detection (DAWPD) sound speed sensor. The DAWPD sensor is a compact sensor that measures the sound speed of gases. The sound speed of gaseous fuels can be related to various properties of a fuel including composition. A sound speed sensor can be used as a fuel quality sensor in natural gas vehicles (NGVs), variable gaseous fuel (VGF) vehicles (a VGF vehicle uses a mixture of hydrogen and natural gas fuel), and proton exchange membrane (PEM) fuel cell vehicles (FCVs). In a NGV the DAWPD sensor can be used to measure the methane number, Wobbe number, and hydrogen-to-carbon ratio of the natural gas. The DAWPD sensor can also be used to find the composition of the fuel used in VGF vehicles. In PEM fuel cell vehicles small amounts of CO (>25 ppm) can poison the fuel cell. The DAWPD sensor can measure the amount of CO in the hydrogen fuel in order to control the fuel reforming process. For all three applications the DAWPD sensor can be used in conjunction with a feed-forward control system to optimize the performance of the vehicles. This paper presents the theory relating the sound speed of gaseous fuels to the property of interest for each powertrain. Experimental results are presented for measurements of nitrogen/methane mixtures (representative of natural gas) and methane/hydrogen mixtures as used in NGV and VGF vehicles, respectively. The experimental results show that the DAWPD sensor has adequate accuracy for use in NGV and VGF vehicles. Theoretical results show that the DAWPD sensor also has adequate accuracy for PEM FCVs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".