Determining temperature and drivers of heat in mechanical face seals
Bibliographic record
Abstract
High heat in mechanical seals is a long-recognized main failure cause, disrupting the seal's vital lubricating film and heating temperature-sensitive process media. A way to accommodate the seal's heat generation is by choosing materials with high thermal conductivity, such as silicon carbide (SiC). In single-use and short operating time applications, the usage of SiC may be greatly over-dimensioned, unnecessarily environmentally intense, and expensive. There is a desire to apply other materials for these cases, but the heat generation of the mechanical seal poses great limitations in material selections. The purpose of this study is to reduce the operating temperature of mechanical seals to enable the application of cheaper and more sustainable materials. The seals used for testing are presently used in Alfa Laval’s single-use separators, CultureOne, and accompanying this work is the designing of a temperature measuring rig for the specific mechanical seal to be tested in its applied environment. The following research questions have been formulated to concretize the presented problematization: RQ1: Which parameters have the highest significance for generated heat in the single-use mechanical seal? RQ2: How can the heat of the mechanical seal’s wear face be measured while operating in the CultureOne machine? This study is deductive, intending to gather quantitative data through thermal measuring. The temperature measuring rig was designed with inspiration from previous similar studies and was then validated through repeated testing and comparisons with FEM-simulations of the tested case. This resulted in a detected temperature loss of 1°C, which has been accounted for in the conclusions. During validation tests, a standard original seal temperature to use as a comparison was found to be 41°C. Heat-affecting parameters have been gathered and tested through the method of Design of Experiments (DoE), where a Placket-Burman design was chosen to enable testing 9 parameters with 12 tests. The results of the study indicate that surface roughness and sealing liquid temperature have significant effects on the seal temperature. Roughening the surface of the mechanical seal’s polished, static carbon face, as achieved with a 1000-grit abrasive paper, provided a heat mitigating effect of -9.3 °C, which is a 23% heat reduction. Similarly, introducing more cooling power to the system by cooling the sealing liquid with external methods such as an ice bath provided a heat-reducing effect of -5.4 °C at the end of a one-hour test, reducing the temperature with 13%. Combining the parameters provided a 36% reduction in heat for a one-hour run with the mechanical seal in the CultureOne Primo machine. Thus, the temperature-reducing strategies discovered in this study can be applied to enable more sustainable and cheaper material selections for mechanical face seals.
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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 | 0.000 |
| 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) | 0.009 | 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".