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Record W4409567065 · doi:10.1063/5.0264218

Research advances in moving particle semi-implicit method and applications in ocean engineering

2025· article· en· W4409567065 on OpenAlex
Jinxin Wu, Biye Yang, Zhe Sun, Guiyong Zhang, Ahmad Shakibaeinia

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsHydro-QuébecPolytechnique Montréal
FundersNational Natural Science Foundation of China
KeywordsPhysicsParticle (ecology)Classical mechanicsMechanicsStatistical physicsAerospace engineeringOceanography

Abstract

fetched live from OpenAlex

As a Lagrangian particle method, the moving particle semi-implicit (MPS) method has demonstrated distinct advantages in addressing problems involving large deformations of free surfaces and interfaces. This paper comprehensively reviews the MPS method, including its development, advancements, and applications within ocean engineering. The article focuses on the crucial aspects—stability, accuracy, and efficiency—that affect the application of numerical methods. Additionally, it summarizes an overview of the latest developments and technological frameworks for multiphase flow and fluid–structure interaction models. Regarding applications, this paper highlights the achievements and challenges of the MPS method in ocean engineering. Finally, the paper discusses the MPS method's current challenges and future research directions, offering valuable insights for advancing its development and application.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.299

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 head0.013
GPT teacher head0.329
Teacher spread0.317 · 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