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Record W2015795659 · doi:10.1260/2040-2295.3.3.455

Development of a Laminar Flow Bioreactor by Computational Fluid Dynamics

2012· article· en· W2015795659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Healthcare Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid dynamics and aerodynamics studies
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersLeibniz-GemeinschaftGottfried Wilhelm Leibniz Universität HannoverCarnegie Mellon University
KeywordsLaminar flowComputational fluid dynamicsBioreactorFluid dynamicsDynamics (music)Flow (mathematics)MechanicsComputer scienceChemistryPhysics

Abstract

fetched live from OpenAlex

The purpose of this study is to improve the design of a bioreactor for growing bone and other three‐dimensional tissues using a computational fluid dynamics (CFD) software to simulate flow through a porous scaffold, and to recommend design changes based on the results. Basic requirements for CFD modeling were that the flow in the reactor should be laminar and any flow stagnation should be avoided in order to support cellular growth within the scaffold. We simulated three different designs with different permeability values of the scaffold and tissue. Model simulation addressed flow patterns in combination with pressure distribution within the bioreactor. Pressure build‐up and turbulent flow within the reactor was solved by introduction of an integrated bypass system for pressure release. The use of CFD afforded direct feedback to optimize the bioreactor design.

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.

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 score0.126
Threshold uncertainty score0.697

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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