Flow velocity profiling using acoustic time of flight flow metering based on wide band signals and adaptive beam-forming techniques
Bibliographic record
Abstract
In this paper, we present a novel approach to non-intrusive flow velocity profiling technique using multi-element sensor array and wide-band signal's processing methods. Conventional techniques for the measurements of the flow velocity profiles are usually based on intrusive instruments (current meters, acoustic Doppler profilers, Pitot tubes, etc.) that take punctual velocity readings. Although very efficient, these choices are limited in terms of practical cases of applications especially when non-intrusive measurements techniques are required and/or a spatial accuracy of the velocity profiling is required This is due to factors related to hydraulic machinery down time, the often long time duration needed to explore the entire section area, the frequent cumbersome number of devices that needs to be handled simultaneously, or the impossibility to perform intrusive tests. In the case of non-intrusive flow profiling methods based on acoustic techniques, previous methods concentrated on using a large number of acoustic transducers placed around the measured section. Although feasible, this approach presents several major drawbacks such as a complicated signal timing, transmission, acquisition and recording system, resulting in a relative high cost of operation. In addition, because of the geometrical constraints, a desired number of sensors may not be installed. Recent results in acoustic flow metering based on wide band signals and adaptive beamforming proved that it is possible to achieve flow velocity profiles using less acoustic transducers. In a normal acoustic time of flight path the transducers are both emitters and receivers, sequentially changing their roles. In the new configuration, proposed in this paper, two new receivers are added on each side. Since the beam angles of each acoustic transducer are wide enough the newly added transducers can receive the transmitted signals and additional time of flight estimation can be done. Thus, several flow velocities are possible to be computed. Analytically defined emitted wide band signals makes possible the identification of signals coming from each transducer. Using the adaptive beam-forming algorithm the receiving transducers can record different signals from the receiver, equivalent to different propagation paths. Therefore, different measurements of time of flight are possible, leading to additional flow velocity measurements. Results carried out in an experiment facility belonging to ICPE-CA, Bucharest - Romania allowed to the validation of the flow velocities computed using this new technique, in symmetric, asymmetric and uneven flow conditions. The acoustic derived values were referenced with those provided from a Pitot tube probe installed in the test channel and the results obtained by the method proposed in this paper are relatively close to this reference.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".