Analysis of thermodynamic behavior of high-speed mechanical devices by finite difference method
Bibliographic record
Abstract
This paper firstly starts from the thermodynamic theory, based on the classical heat transfer theory, and adopts the finite difference dichotomy method for mathematical modeling, and uses the secondorder center difference format to discretize the space, and solves the non-Fourier heat conduction equation.After completing the algorithmic solution of thermodynamic theory and finite difference method, the two are combined to deeply analyze and discuss the thermodynamic behavior of highspeed mechanical devices represented by high-speed rotating bearings.When the bearings operate at high speed, with the increase of stiffness, the pressure change in the middle and rear part of the bearings gradually flattens out, the temperature gradually rises, and the relative bearing capacity of the bearings decreases.The increase in the number of bearings also brings about an increase in the pressure at the centerline of the bearings, and the temperature of the air film corresponds to the increase in the average pressure, and there is a risk of over-temperature.In the thermodynamic characterization, the work done by the air film under compression and the heat generation due to viscous shear will lead to an increase in the temperature of the air film, which will lead to the temperature rise of the bearings, and will have a very great impact on the bearing performance.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".