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A Heat-Driven Micro Jet Pump Using a Self-Oscillating Fluidic Heat Engine (SOFHE): First Demonstration

2023· article· en· W4391549077 on OpenAlexafffund
Nooshin Karami, Etienne Léveillé, Amrid Amnache, Luc G. Fréchette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsInstitut interdisciplinaire d'innovation technologique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJet (fluid)Heat pumpFluidicsHeat engineMicrofluidicsMechanical engineeringMaterials scienceInjectorMechanicsJet engineFlow (mathematics)Heat transferEngineeringElectrical engineeringHeat exchangerPhysicsNanotechnology

Abstract

fetched live from OpenAlex

This paper reports the design and testing of a 3D-printed jet pump integrated with a self-oscillating fluidic heat engine (SOFHE) as an energy harvester for microfluidic pumping. This research marks the first instance of combining a thermally induced oscillatory flow with a valveless pumping mechanism. The valveless approach used can reduce losses while being well adapted to the operating pressure of the SOFHE. The jet pump characteristic curves exhibit a linear trend at various SOFHE operating temperatures with a pumping capacity of tens of milliliters per minute and a pumping head of tens of mmH2O. This heat-driven jet pump presents a promising solution for microfluidic applications, particularly for actively cooling microprocessors, where waste heat is available.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.237
Teacher spread0.215 · 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
Published2023
Admission routes2
Has abstractyes

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