Dynamical Capillary Rise Photonic Sensor for Testing of Diesel and Biodiesel Fuel
Bibliographic record
Abstract
There are many fuel quality standards introduced by national organizations and fuel producers. Usual techniques for measuring fuel parameters like cetane number, cetane index, fraction composition, viscosity, density, and flash point, require relatively complex and expensive laboratory equipment. On the fuel user side, fast and low cost sensing of useful state of biodiesel fuel is important. The main parameters of diesel fuel compatibility are: density, viscosity and surface tension. These three parameters define indirectly the quality of the fuel atomization process and the injected portion of energy that affect the quality of the fuel. In the presented paper the purposefulness of fuel testing using measurements of separable parameters is discussed. On this base, a sensor which enables the examination of relation of the mentioned parameters in one arrangement is proposed, analyzed and tested. The sensor uses the dynamic capillary rise method with photonic multichannel data reading in an inclined capillary. The principle of the sensor’s operation, the construction of the sensor head, and the experimental results are presented. The capillary is a disposable element. The sensor testing was performed with freshly prepared biodiesel fuels, and fuels stored for 2 years. We conclude that the proposed construction may be in future the base of low cost commercially marketable instruments for basic fuel classification: fit for use or not. That classification includes initial fuel composition and fuel parameters change during storage. Therefore, the proposed sensor is intended to use in fuel buying/selling point rather than used as part of a diesel engine automated system.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".