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Record W3199361467 · doi:10.32393/csme.2021.149

Optimization Of A Multi-Jet Water Flow Meter

2021· article· en· W3199361467 on OpenAlex
Mitchell L Boddy, Eric Savory

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 4 · 2021
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsMetreFlow measurementJet (fluid)Flow (mathematics)Water jetComputer scienceEnvironmental scienceMarine engineeringMechanicsAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.199
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it