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Record W2363990973

Immersed Boundary Method and Its Application

2007· article· en· W2363990973 on OpenAlexaff
Huaxiong Huang

Bibliographic record

VenueChinese Quarterly of Mechanics · 2007
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsYork University
Fundersnot available
KeywordsImmersed boundary methodForcing (mathematics)Boundary (topology)Boundary conditions in CFDBoundary knot methodMathematicsBoundary value problemSingular boundary methodMathematical analysisBoundary problemRobin boundary conditionFree boundary problemBoundary element methodPhysicsFinite element method
DOInot available

Abstract

fetched live from OpenAlex

The immersed boundary method is a general technique for modeling flow flied with complex geometries and fluid structure interaction and now it is applied in various aspects of fluid dynamics.This immersed boundary method is both a mathematical formulation and a numerical scheme.In immersed boundary method,the structure immersed in fluid is considered as a kind of momentum forcing in Navier-Stokes equations rather than a real body,which avoids the problem of generating a body conformal grid.The immersed boundary methods are split up in continuous forcing and discrete forcing approach.The continuous forcing approaches,which forcing source satisfy a certain kind of mechanical relation,are mainly used in elastic boundary problem;however,the discrete forcing approaches,which forcing source are derived from the boundary conditions,are primarily used in solid boundary problem.An overview and comments on the immersed boundary method were presented.First the basic theory and mathematical construction of immersed boundary were described in detail,then briefly several kinds of immersed methods were introduced,after that some applications on different areas were given,finally the future investigation on the immersed boundary were recommended.

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: none
Teacher disagreement score0.914
Threshold uncertainty score0.508

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

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