Linear Regression Analysis on Fluid Flow Rate in Tank Level Control
Bibliographic record
Abstract
This research was conducted to study the effect of pump speed on changes in fluid levels in the tank and linear regression analysis of fluid flow. The variation used is a 4 mm solenoid and a variable pump speed range of 30-50%. The results of increasing the level at a pump speed of 32% obtained a level of 82 mm and a pump speed of 50% obtained a level of 149.9 mm, with fluid flow rotation occurring in the first minute. In addition, the determination calculation uses actual fluid level data at time 0 seconds (n=1), time 60.27 seconds (n=30), and time 180 seconds (n=61), with a fluid level value of 1 mm, 118 mm, and 141 mm. So the determination evaluation (R2) obtained is 0.863, which indicates that the model is included in the high tolerance category. So this indicates that the PID controller in this series of piping system equipment is still suitable for use because in the recording of the fluid flow data obtained no significant gaps were found. Apart from that, it can also be seen that the pump speed affects the fluid flow rate due to the change in mechanical energy into kinetic energy which pushes the fluid towards the tank. Process time also influences changes in fluid levels in the tank as a result.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".