Application of Low-Temperature Phase Change Materials to Enable the Cold Weather Operability of B100 Biodiesel in Diesel Trucks
Bibliographic record
Abstract
The use of B100 biodiesel for compression ignition engines during the winter poses a challenge due to gelling and plugging of engine filters and fuel lines. The most common method to prevent this issue is blending it with petroleum diesel and many engine manufacturers limit the biodiesel in blends to 20% or less for warrantee purposes; as low as 5% may be set for winter months. In this research, an experimental analysis is performed using a scaled model of the fuel tank with canola oil as a test fluid in the tank. An insulated tank is subjected to an ambient temperature of −20 °C in an icing tunnel facility with air velocity at 10 m/s. The results show that the time for the oil to drop from 20 °C to 5 °C was increased from 18.6 h to 22.5 and 33 h, respectively, when 4 and 12 tubes containing phase change materials (PCM) were inserted in the tank containing 33 l of canola oil. A numerical model was further formulated to predict the transient temperature of the oil and comparison with experimental results showed excellent agreement. Finally, the developed numerical model was used to simulate different designs to investigate the effect of tank filling level, overall heat transfer coefficient, number of PCM modules, and diameter of PCM modules on the tank performance. The results show that B100 can be implemented in diesel engines in cold climates using a passive approach using engine coolant.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".