Surface engineered Nanofluids in geothermal systems: Experimental evaluation of enhancement in chemical and thermal stability of Nanofluids and numerical investigation of the effect of nanoparticles in heat transfer and pressure drop characteristics
Bibliographic record
Abstract
Enhancing heat transfer is essential for the efficient operation of geothermal energy systems. Nanofluids are used to improve thermal performance, but their stability is crucial to ensure consistent efficiency and to prevent operational issues. This study examines the stability and performance of aluminum-based water nanofluids stabilized using three surface modifiers, including sodium dodecyl sulfate (SDS), (3-aminopropyl) triethoxysilane (APTES), and Span 80, over four months, targeting long-term geothermal applications. Advanced methods, including scanning electron microscopy (SEM), UV–Vis spectrometry, thermal gravimetric analysis (TGA), dynamic light scattering (DLS), FTIR (Fourier-transform infrared spectroscopy), turbidity, viscosity and density measurements, computed tomography (CT) and computational fluid dynamics (CFD) with the finite element method (FEM), are used to comprehensively assess the long-term stability, thermal and rheological behavior of nanofluids at low to high temperatures and pressures. Results at ambient and geothermal conditions show that surfactant-coated aluminum nanoparticles significantly improved stability over uncoated formulations. Stabilized nanofluids coated with SDS, Span 80, and APTES reduced friction factors by 22 %, 15 %, and 17.5 %, respectively, compared to water. Optimal volume fractions of coated aluminum nanofluids were seen at 0.0033, 0.025, and 0.012 for SDS, Span 80, and APTES, respectively. Additionally, the Prandtl number decreased by 10 %, 23 %, and 17 % with SDS, Span 80, and APTES, respectively, compared to water, showing enhanced heat transfer performance primarily due to the presence of thermally conductive nanoparticles. The observed reductions in friction factor values are likely due to the influence of surfactants on fluid rheology and interfacial interactions, compared to nanofluids without surface coatings. SDS demonstrated superior overall performance, showing the highest stability, greatest friction factor reduction, and best pressure drop characteristics. Therefore, it is recommended as the most effective surfactant for aluminum-based nanofluids in geothermal applications. The use of surface modifiers leads to a 30 % improvement in energy recovery over that of water, and about a 12 % increase compared to unmodified nanofluids. A novel stability index model is developed in this study to effectively screen and quantify the stability of nanofluids.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".