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Record W2016781413 · doi:10.2118/2004-053

Detection of Combustion in Pipelines Using Flame Noise

2004· article· en· W2016781413 on OpenAlexaffabout
M. D. Morgan, S. A. Mehta, T. J. Al-Himyary, R.G. Moore

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCombustionPipeline transportNoise (video)Environmental scienceAutomotive engineeringComputer scienceAcousticsEngineeringEnvironmental engineeringArtificial intelligenceChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Flammable mixtures are frequently transported within tubing. Examples include flare lines, storage tank vents, air drilling, and improperly designed purging operations. The flow regime is often turbulent, heat transfer rates high, and flow control devices few in number. In these circumstances, combustion of the gas mixtures could be catastrophic. Early flame detection is critical. Many methods of flame detection are available. Unfortunately, few offer remote, non-line of sight, detection. There is often no means by which combustion can be detected while still inside the tubing. To address this problem, combustion noise is being investigated at the University of Calgary as a possible solution. An experimental study has been completed that shows that combustion noise is detectable in high speed pressure data. This noise can be distinguished from other sources of noise by its inverse power law relationship with frequency. This relationship holds true whether the frequencies are calculated using traditional frequency analysis or wavelet analysis. Introduction It has been known for several decades that turbulent burnerflames produce noise. Moreover, turbulent flames, though inefficient producers of noise, are still much more efficient at producing noise than turbulent flows. Thus, a crude calculation of the total noise level can, under some conditions, distinguish combustion noise from background flow noise (1). Unfortunately, there are many sources of noise when dealing with industrial equipment. A practical detection system needs to be able to identify the specific noise produced by combustion. Fortunately, acoustic theory predicts that the mechanisms by which combustion noise is generated are distinct from those that produce noise by turbulence or other pure flows (2–7). There is also strong evidence linking the production of the combustion noise to the changes in the fuel burning rate (6,8,9). This link between the fuel burning rate and the production of noise supports the acoustic theories. Unfortunately, current theories are quite limited. In a comprehensive summary of the current state of knowledge, Lieuwen states the "The development of accurate, predictive combustion response models for realistic, that is, turbulent, configurations has not been achieved, however, and remains a key challenge for future workers." (10) It has been found that the noise produced by combustion is function of many different variables. Researchers studying turbulent burner flames found that the spectrum of the noise is affected by changes in the laminar flame speed of the fuel/air mixture, the burner diameter, and the flow rate (11–13). The peak frequency of this noise was also closely related to the level of macroscopic mixing (11,14). Recent research into highly turbulent combustion has also found a connection between the acoustic frequency spectrum of flames and the kinetic spectrum of turbulence (15–17). Researchers have also found that flame noise can be used to diagnose burner flame instabilities such as the lean blowout limit (18,19). Obviously a wealth of information can be found in combustion "noise". However, there are few acoustic studies of combustion within tubing, especially under flowing conditions. Theory and experiments have normally been restricted to laminar or no-flow conditions (20,21).

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.

How this classification was reachedexpand

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: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes2
Has abstractyes

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