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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 OpenAlexaff
Meir Israelowitz, Birgit Weyand, S.M.K. Rizvi, Peter M. Vogt, Herbert P. von Schroeder

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations16
Published2012
Admission routes1
Has abstractyes

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