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Record W4408613160 · doi:10.1016/j.jcp.2025.113928

An accurate immersed boundary method using radial-basis functions for incompressible flows

2025· article· en· W4408613160 on OpenAlexafffund
Hamayun Farooq, Imran Akhtar, Arman Hemmati, Muhammad Saif Ullah Khalid

Bibliographic record

VenueJournal of Computational Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsLakehead UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaHigher Education Commision, PakistanHigher Education Commission, Pakistan
KeywordsRadial basis functionImmersed boundary methodCompressibilityIncompressible flowBasis (linear algebra)Boundary (topology)Mathematical analysisPhysicsMathematicsMechanicsClassical mechanicsGeometryComputer science

Abstract

fetched live from OpenAlex

In this work, we introduce a parallel computational solver based on the sharp-interface immersed boundary method for simulating three-dimensional incompressible flows over stationary and moving boundaries. Despite the robustness and ease of implementation of conventional body-conformal grid methods, they are limited to relatively simple immersed geometries, leading to challenges in grid generation and quality. Our approach employs a multi-dimensional ghost-cell methodology and radial basis functions interpolation/splines to achieve accurate boundary condition and superior efficiency. We utilize unstructured triangular elements for geometric surface discretization and non-uniform Cartesian grids for constructing flow domains around the immersed boundaries. Furthermore, full parallelization using domain decomposition ensures scalability on distributed memory platforms, facilitated through message-passing interface libraries. Additionally, we introduce a flow smoothing strategy to mitigate spurious pressure oscillations near immersed bodies. Through simulations of two- and three-dimensional fluid-structure interaction problems, we demonstrate the effectiveness, accuracy, and efficiency of our computational solver. • An immersed-boundary method is coupled with radial basis functions interpolation. • A smoothing strategy is introduced to mitigate spurious oscillations in pressure. • The full parallelization of the solver through domain decomposition is explained.

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: none
Teacher disagreement score0.706
Threshold uncertainty score0.515

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 communication0.0000.001
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 head0.037
GPT teacher head0.354
Teacher spread0.316 · 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
Published2025
Admission routes2
Has abstractyes

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