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Record W4306989184 · doi:10.1002/cjce.24730

An improved correlation for dry pressure losses across static mixers at high Reynolds numbers

2022· article· en· W4306989184 on OpenAlexvenueno aff
Evan Lowry, Gautham Krishnamoorthy

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsPressure dropTortuosityMechanicsReynolds numberSurface finishScalingPorosityComputational fluid dynamicsMass transferSurface roughnessMaterials scienceTurbulenceGeometryComposite materialMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract Corrugated (SMV‐style) static mixers are industrially important for process intensification and can promote gas–liquid mass transfer in processes such as sour gas sweetening. Current correlations for pressure loss are limited to Reynolds numbers (Re) below 40 000, far below the ranges encountered in natural gas systems (10 5 < Re < 10 7 ). An experimental and numerical study of pressure drop across multiple corrugated mixers in the range of 10 4 < Re <2 × 10 5 encompassing different configurations (aligned, rotated), pipe diameters (1–4 in.), and sand grain surface roughness values (10–5000 μm) is reported here. Our previous correlation for pressure loss across a single corrugated element is shown to be extendable to multiple corrugated mixing elements. Through the inclusion of mixer tortuosity ( τ ), porosity ( ε ), and macro‐scale (geometric) wall roughness ( e ), the correlation also matches historical pressure drop data (at different τ , ε ) reported in literature, thereby demonstrating the utility of these variables as parameters that can help optimize mixer performance. Experiments and computational fluid dynamics (CFD) modelling revealed that the rotated configuration increased the residence time by up to 13% in comparison to the aligned configuration. This may have implications on the selective absorption of sour gas components that are based on fast kinetics. In addition, the role of wall roughness (both pipe housing and mixer) was demonstrated to be significant in this study (accounting for 55% of the pressure losses) and must be accurately accounted for when scaling laboratory measurements.

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 score0.071
Threshold uncertainty score0.493

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)0.0000.000

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 head0.004
GPT teacher head0.186
Teacher spread0.183 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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