Nanofluids for Performance Improvement of Heavy Machinery Journal Bearings: A Simulation Study
Bibliographic record
Abstract
Nanofluids have extensive applications in hydrodynamic journal bearings used in heavy industry machinery. Inorganic fullerene-like tungsten disulfide nanoparticles (IF-WS2 NPs) are the most common additive for lubrication purpose due to their excellent mechanical characteristics along with their effect on reducing friction and wear. In this work, a computational simulation approach with discrete phase modeling (DPM) of suspended nanoparticles was used to evaluate the application of the IF-WS2 nanofluid lubricant on load carrying capacity of high-load journal bearings where the normal loads are high, considering the bearing dimensions. For accurate simulation, nanofluid viscosity was calculated considering the aggregation effect of NPs by using scanning electron microscopy (SEM) imaging of the nanofluids. A benchmark study was first performed to assess the model accuracy. Hydrodynamic lubrication was simulated under different nanofluid weigh fractions. The simulated pressure distribution was then employed to determine the load capacity of the bearing. The results show an approximately 20% improvement of load carrying capacity at 5% weight fraction of WS2-oil nanofluid.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".