Accurate prediction of the rheological behavior of MWCNT-Al2O3/water-ethylene glycol nanofluid with metaheuristic-optimized machine learning models
Bibliographic record
Abstract
The accurate prediction of the rheological properties of nanofluids is critical for optimizing their application in various industrial systems. This study focuses on the dynamic viscosity prediction of MWCNT-Al 2 O 3 /water (80 %) and ethylene glycol (20 %) hybrid nanofluid using machine learning approaches. A multilayer perceptron neural network (MLPNN) was employed for viscosity prediction, and its structural and training parameters, including the number of hidden layers and neurons, learning rate, training technique, and transfer functions, were optimized using three metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Marine Predators Algorithm (MPA). A dataset containing viscosity measurements influenced by nanoparticle volume fraction (VF), temperature (T), and shear rate (SR) was utilized. The optimization algorithms were evaluated over 10 and 20 runs for single-hidden-layer (1HL) and double-hidden-layer (2HL) MLPNNs, respectively. For the 1HL-MLPNN models, all three algorithms achieved nearly identical performance with high predictive accuracy (R = 0.99992, MSE = 0.00176). In contrast, for 2HL-MLPNN models, PSO outperformed MPA and GA with R = 0.99995 and MSE = 0.00105, followed by MPA (R = 0.99995, MSE = 0.00123) and GA (R = 0.99992, MSE = 0.00160). Also, sensitivity analysis revealed the VF as the most significant input parameter affecting viscosity predictions, followed by shear rate and temperature. These findings demonstrate the potential of metaheuristic-optimized MLPNNs for high-accuracy prediction of hybrid nanofluid rheological properties, facilitating improved design and application in thermal management systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".