Bibliographic record
Abstract
Multi-unit residential buildings (MURBs) pose numerous challenges for the accurate measurement of utilities usage. Water flow meters often must be installed against vendor recommendations as space can be quite limited. Meters are forced to be installed adjacent to 90 pipe bends, expansions, or contractions which create unsteady flow profiles. Meters operate best under fully developed conditions so this can result in increased inaccuracies in meter readings, and consequently, improper consumer billing. This research project focusses specifically on the optimization of the multi-jet style meter. Multi-jet meters are mechanical devices which contain an impeller enclosed by a concentric ring of guide vanes, which cause the water to form multiple jets that impact the impeller from multiple angles. These meters can provide a high accuracy for their low price point but can be heavier, bulkier, and less accurate than some other meter designs. Currently, no quantitative research has been performed on the effects of pipe bends on multi-jet meter performance. There is also a lack of information available in the literature discussing the performance of these meters when the size or design of their internals is altered. Thus, the goal of this project is to reduce the overall size of the meter to allow it to fit easier in these tight installation conditions and mitigate or potentially eliminate the impact to accuracy caused by awkward installation conditions. To determine the effectiveness of the improved design the accuracy of current models must first be established. The testing apparatus used is a closed-loop pipe network consisting of a water reservoir, pump, venturi meter, and the multi-jet flow meter. The venturi meter with high resolution pressure transducers will allow for comparison between registered flowrates of the tested meter versus the actual flowrate. This pipe network will also be modified to simulate the special restrictions of MURBs to establish a baseline for meter performance under these circumstances. The main parameters that are to be examined are: the overall size of the meter body, number of jets/guide vanes, as well as the size and design of the inlet diffuser which is meant to improve the flow profile to make it more suitable for measurement prior to entering the meter. Some general testing has already been completed but due to the pandemic, delays in equipment deliveries have slowed progress on the testing of the multi-jet meters specifically.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".