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Record W1978818041 · doi:10.1115/fedsm-icnmm2010-30192

On the Feasibility of Modeling Two-Phase Flow-Induced Fluidelastic Instability in Tube Bundles

2010· article· en· W1978818041 on OpenAlexaff
Njuki Mureithi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInstabilityFlow (mathematics)Work (physics)MechanicsStability (learning theory)Computer sciencePhase (matter)Statistical physicsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

In the 80’s a number of theoretical models were developed to model fluidelastic instability, primarily for single phase flows. The models ranged from purely analytical models to semi-empirical models requiring considerable experimental data as input. While these models were very successful in uncovering the nature of fluidelastic instability and the underlying mechanisms in single phase flow, this work seemed to stop short of getting to the next step of practical application to two-phase flows. During the same period, Connors formula became ‘entrenched’ in industry to the extent that the formula now forms part of the design norms against fluidelastic instability. In an ongoing research program the quasi-steady model has been chosen as a possible candidate for modeling fluidelastic instability in two-phase flows. This paper discusses the challenges associated with accurate modeling of fluidelastic instability in two phase flows using this and other models. The unsteady model is shown to have limitations when it comes to measuring accurately the necessary unsteady fluid force coefficients. A comparison of the stability analysis results with experimental measurements shows that the quasi-steady model can give a reasonable estimate of the instability velocity as well as the inter-tube dynamics. Finally, the remaining challenges, before the quasi-steady model and possibly other models can be fully implemented for prototypical conditions are discussed. In particular the need for more work to understand the flow itself is highlighted.

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)0.001
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 score0.418
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.277
Teacher spread0.246 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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