An Experimental Evaluation of Thermal Conductivity of Colloidal Suspension of Carbon-Rich Fly Ash Microparticles and Diamond-Nano Powder (DNP) in Jet-A Fuel
Bibliographic record
Abstract
This study investigates the enhancement of thermal conductivity in Jet-A fuel by dispersing carbon-based micro/nano materials, specifically Carbon Fly Ash (CFA) and Diamond Nano Powder (DNP).CFA, derived from heavy fuel oil combustion and rich in unburned carbon and inorganic oxides, possesses a porous structure, while DNP is renowned for its high thermal conductivity.Both materials were introduced into Jet-A fuel to assess their impact on heat transfer properties.Colloidal suspensions were stabilized using a two-step process involving surfactant addition and sonication, with stability lasting between 20 to 60 minutes, depending on particle concentration.Thermal conductivity measurements under controlled heat flux conditions revealed that a 2% DNP concentration increased thermal conductivity by 2%, whereas a 3% CFA concentration resulted in an 8% improvement comparable to activated carbon nanoparticles.The significant enhancement by CFA is attributed to its porous structure and trace iron content, making it a promising additive for fuel performance improvements.This study highlights the potential of CFA and DNP to enhance thermal properties in Jet-A fuel, while also identifying the challenges in colloidal stability, particularly for DNP.
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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 | 0.000 | 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 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".