Evidence map›Paper›PMID 36560282›Full record

ArticleSensors (Basel, Switzerland)2022

Low-Power Transit Time-Based Gas Flow Sensor with Accuracy Optimization.

José R García Oya, Alejandro Sainz Rojas, Daniel Narbona Miguel, Ramón González Carvajal, Fernando Muñoz Chavero

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

José R García OyaElectronic Engineering Department, University of Seville, E-41092 Seville, Spain.ORCID 0000-0001-8646-9341
Alejandro Sainz RojasElectronic Engineering Department, University of Seville, E-41092 Seville, Spain.ORCID 0000-0002-9587-8187
Daniel Narbona MiguelElectronic Engineering Department, University of Seville, E-41092 Seville, Spain.
Ramón González CarvajalElectronic Engineering Department, University of Seville, E-41092 Seville, Spain.ORCID 0000-0003-3891-8987
Fernando Muñoz ChaveroElectronic Engineering Department, University of Seville, E-41092 Seville, Spain.

Funding

Regional Government of Andalusia PY20 RE 012 AICIA
6 · The paper itself

Abstract

In this paper, a fully designed ultrasonic transit time-based gas flow sensor is presented. The proposed sensor has been optimized in terms of accuracy, sensitivity, and power consumption at different design stages: mechanical design of the sensor pipe, piezoelectric transducer configuration and validation over temperature, time of flight detection algorithm, and electronics design. From the optimization and integration of each design part, the final designed gas flow sensor is based on the employment of 200 kHz-piezoelectric transducers mounted in a V-configuration and on the implementation of a cross-correlation algorithm based on the Hilbert Transform for time-of-flight detection purposes. The proposed sensor has been experimentally validated at different flow rates and temperatures, and it fully complies with the accuracy specifications required by the European standard EN14236, placing the proposed design into the state of the art of ultrasonic gas flow sensors regarding cost, accuracy, and power consumption, the latter of which is crucial for implementing smart gas meters that are able to autonomously operate as IoT devices by extending their battery life.

Indexed as

TransducersUltrasonicsAlgorithmsElectronicsTemperaturecross-correlationIoT sensors networkspiezoelectric transducerstransit time measurementultrasonic gas flow sensors

Identifiers

PMID36560282
PMCPMC9782514

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.