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Record W7081917428 · doi:10.11159/icmie25.167

Rheological Calibration and CFD Simulation of Grease Behaviour Using the Herschel-Bulkley Model

2025· article· en· W7081917428 on OpenAlexvenueno aff

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersEuropean Commission
KeywordsCalibrationGreaseRheologyComputational fluid dynamics

Abstract

fetched live from OpenAlex

This paper represents a first step within a broader research project aimed at developing Computational Fluid Dynamics (CFD) models capable of reliably predicting the flow behavior of lubricating greases in mechanical systems, with the ultimate objective of estimating load-independent power losses and analyzing grease distribution.In this initial phase, the authors focus on the rheological characterization of a bearing grease through experimental measurements performed on a cone-on-plate rheometer at two temperatures: 25°C and 80°C.The Herschel-Bulkley (HB) model was selected due to its widespread adoption in modeling non-Newtonian fluids like grease.A curve-fitting procedure was employed to calibrate the HB parameters (yield stress, consistency index, and flow index) using various regression strategies: Mean Squared Error, Percentage Error, Absolute Error, and Logarithmic Error.Among them, the logarithmic error minimization approach provided the best agreement with the experimental data.The sensitivity of model accuracy to the chosen fitting criterion is discussed.The optimized HB parameters were then used to simulate the experimental setup in the opensource environment OpenFOAM®, modeling a rotational sector of the rheometer by exploiting its cyclic symmetry.A structured hexahedral mesh was generated, and a mesh sensitivity analysis was carried out to ensure solution robustness.Although the physical system is steady and single-phase, a transient two-phase solver was adopted to align with the long-term goal of simulating grease-air interactions in real-world applications such as rolling-element bearings.The simulation results confirmed the theoretical assumption of nearly uniform shear rate across the cone-plate gap and demonstrated excellent agreement between the predicted and measured torques.Additionally, a clear influence of temperature on HB parameters was observed, emphasizing the need for temperature-specific calibration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractno

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