Rheological Calibration and CFD Simulation of Grease Behaviour Using the Herschel-Bulkley Model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".