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Record W2140411936 · doi:10.1115/icnmm2011-58031

The Study of Microfilter Performance in Different Environments Using DSMC

2011· article· en· W2140411936 on OpenAlexaff
Masoud Darbandi, A. Karchani, Rasoul Khaledialidusti, G. E. Schneider

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsKnudsen numberHeliumMach numberMechanicsMaterials scienceNuclear engineeringPhysicsAtomic physicsEngineering

Abstract

fetched live from OpenAlex

Microfilters are commonly used to block undesirable particles in the fluid flows and to control the flow patterns in MEMS. The main purpose of this study is to understand the effect of gas type on density, pressure, Mach number, and velocity distributions of fluid flows through a microfilter. The Knudsen number is the slip flow regime passing through the microfilter. We use direct simulation Monte Carlo (DSMC) method to simulate the flow of nitrogen, helium, oxygen, air and methane passing through a specific microfilter. The geometry of microfilter is unique in all cases. Our results confirm that every gas performs a different performance passing through a specific microfilter, and that the efficiency of a microfilter varies in different gas type environments. For example, among different gases, nitrogen and air have the lowest the pressure drops and that helium has the maximum pressure loss passing through a microfilter.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.155

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 head0.067
GPT teacher head0.265
Teacher spread0.198 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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