Investigation of transient multiphase Taylor-Couette flow
Bibliographic record
Abstract
A multiphase, transient, horizontal, Taylor-Couette flow has been investigated numerically. This flow governs thermal performance of self-contained drum motor (SCDM) drive systems. These drive systems are extensively used with conveyor belt systems, employed in many industrial and commercial applications. Thermal performance of the SCDM drive system is governed by heat transfer within multiphase flow of air and oil. This flow is confined within a horizontal annular space between two concentric cylinders. The outer cylinder is the drum. It rotates and rejects heat to the atmosphere. The inner cylinder is the outer surface of the motor. It is stationary and subjected to a constant heat flux. This heat is generated within the electric motor, gearbox, and oil viscous dissipation. Numerical simulations have been carried out using ANSYS-CFX. Transient flow and thermal fields have been investigated, starting from an initial stationary state until a quasi-steady state is reached. The effect of drum rotational speed and oil level on thermal performance of the SCDM drive system has been investigated. The study covers a range of rotational speeds from 0 to 150 rpm, oil level from 0%, (i.e., 100% air-filled) to 100%. Numerical results have been validated using experimental data. The optimum oil level has been determined which gives the lowest possible overall maximum SCDM system temperatures. This optimum oil level depends on the drum rotational speed. Results indicated that the 100% oil-filled case does not provide the best thermal performance. The Nusselt number around the inner cylinder has been correlated as a function of rotational Reynolds number and oil level. The developed correlation can be used as a design tool. This tool is to optimize system thermal performance by determining the optimum oil level at a given rotational speed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".