Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates two predictive modeling approaches for estimating the thermal and tribological performance of graphene-enhanced greases, aiming to reduce reliance on protracted endurance tests. Seven grease formulations with varying graphene concentrations (0–4 wt%) were prepared and tested under a uniform load to capture temperature evolution, wear scar area and coefficient of friction. A classical piecewise regression model, augmented by a Linear Quadratic Regulator (LQR), leverages feedback control to correct temperature predictions and subsequently estimate wear using a polynomial fit. This framework demonstrated high accuracy in tracking transient thermal behaviour, maintaining temperature deviations within ±1 °C of measured data. In parallel, a quantum-classical hybrid model employs a fidelity-based quantum kernel with support vector regression. By encoding partial early-cycle temperature measurements (e.g., from 30 to 120s) into a higher-dimensional Hilbert space, the quantum approach captures subtle nonlinearities and yields strong correlations for both final temperature and wear scar area. Moreover, consistent performance on IBM Quantum models with realistically simulated noise underscores the model’s potential for practical industrial implementation. Collectively, these results confirm the viability of advanced computational tools, both classical and quantum, for rapid, data-driven lubricant assessments. They highlight opportunities to optimize graphene content while minimizing costly trial and error testing.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it