Experimental and Numerical Study of Thermal Performance of a Self Contained Drum Motor Drive System (SCDMDS)
Bibliographic record
Abstract
The main focus of this work is to investigate thermal performance of self-contained drum motor drive systems (SCDMDS). All components of a SCDMDS are contained inside a rotating drum including the electric motor, gearbox, and an air/oil multiphase flow. A considerable amount of heat is generated within the SCDMDS from various sources, namely, the electric motor losses, the oil viscous dissipation and the gearbox losses. In meantime, a limited amount of heat is dissipated through the surface of the rotating drum and the side flanges. Therefore, a SCDMDS sometimes encounters a serious overheating problem, which often results in electric motor failure. The different heat generation and dissipation mechanisms as well as the two-phase flow within the SCDMDS have been studied experimentally and numerically under different operating parameters, namely, the oil level (OV), the drum rotational speed (N), the torque (ζ), the number of motor poles (n) and the electric motor dimensions. The effects of rubber lagging material and thickness as well as the use of rubber belts have been investigated as well. The numerical part of the present study has been carried out using Ansys-CFX and was validated using experimental data. Results showed that the optimum oil level (OV) for the best thermal performance is about 65%. The increase in the rotational speed (N) enhanced the heat transfer within the SCDMDS due to the improved oil splashing. Viscous dissipation (VD) between the motor stator and the rotating drive drum was found to be almost negligible. However, oil viscous dissipation within the gap between the motor rotor and stator was found to have an important effect on the thermal performance. An analytical model has been developed and implemented using MATLAB to estimate VD within the motor. The losses from the gearbox were studied experimentally and numerically considering planetary and co-axial gear trains. The numerical work was carried out using the KISSsoft and KISSsys software. Results showed that the increase in the drum rotational speed (N) or the drum torque (ζ) increased the gearbox losses. In the planetary gearbox, any increase in the OV increases the churning losses, however, the increase in OV increased the losses in the co-axial gearbox up to OV = 31% beyond which the losses remained constant. After understanding the complex interplay between all the heat generating and dissipating mechanisms within the SCDMDS, a number of possible modifications have been proposed in order to resolve the overheating problem. The effect of cooling the electric motor by using an axial air flow has been investigated. The effect of adding fins along the inner surface of the outer rotating drum has also been studied. Correlations of the various contributing mechanisms have been developed. Based on a thermal resistance network, a SCDMDS sizing and performance assessment computer software tool in the form of a digital twin (DT) has been developed. A user-friendly interface has been developed using Visual Basics and Excel. The DT estimates temperature distribution and the amount of heat generated and dissipated from each component within the SCDMDS and hence it identifies whether the case is considered safe to operate or overheating is expected. In overheated cases, the DT also suggests several possible modifications the user could consider to resolve the overheating problem. The DT has been validated against several experimental case studies and found to be very reasonably accurate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".