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Record W2114741360 · doi:10.1155/2014/209575

A Meshless Solution Method for Unsteady Flow with Moving Boundary

2014· article· en· W2114741360 on OpenAlexfundno aff
Jun Zhang, Dengfeng Ren, Xinjian Ma, Junjie Tan, Xiaowei Cai

Bibliographic record

VenueAdvances in Mechanical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaOrganisation Canadienne des Physiciens Médicaux
KeywordsFlow (mathematics)Boundary (topology)AirfoilField (mathematics)Regularized meshless methodComputer scienceMathematicsSingular boundary methodMathematical analysisGeometryMechanicsPhysics

Abstract

fetched live from OpenAlex

Using the concept of overlapping mesh method for reference, a new method called as Overlapping Clouds of Points Method (OCPM) is firstly proposed to simulate unsteady flow with moving boundary problems based on meshless method. Firstly, a set of static background discrete points is generated in the whole calculation zone. Secondly, moving discrete points are created around moving body. According to the initial position of moving object in the flow field, the two sets of discrete points can be overlapped. With the motion of moving objects in the calculation field, moving discrete points around the moving body will inherently move. The exchange of flow field information between static points and moving points is realized by the solution of the clouds of points made up of static and moving discrete points using weighted meshless method nearby overlapping boundary. Four cases including piston problem, NACA0012 airfoil vibration flow around a moving sphere in supersonic and multibody separation are given to verify accuracy and practicability of OCPM. The numerical results agree well with exact solution and experimental results, which shows that the proposed OCPM can be applied to the simulation of unsteady flow problem.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.490
Threshold uncertainty score0.625

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 head0.005
GPT teacher head0.250
Teacher spread0.245 · 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
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

Citations5
Published2014
Admission routes1
Has abstractyes

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