Temperature-Dependent Thermal Conductivity Measurement System for Various Heat Transfer Fluids
Bibliographic record
Abstract
Accurate measurement of the thermal conductivity of a heat transfer fluid (HTF) is important for optimizing the performance of a thermal energy storage system. Herein, we develop a system to measure the thermal conductivity of an HTF during temperature variation, and the system was checked to measure several samples comprising water, lauric acid, stearic acid, oleic acid, and coconut oil. The thermal conductivity was measured using a KS-1 sensor of a KD2 Pro analyzer. In the study, a static heat conducting medium was used to control the temperature of the fluid, instead of the commonly used flowing water bath. The measured thermal conductivities of water (298 to 318 K) and lauric acid (323 to 373 K), stearic acid (358 to 372 K), oleic acid (334 to 372 K), and coconut oil (298 to 363 K) were compared to data from previous studies and fitted to available models. The accuracy of the data is further analyzed by relating the number of C and H atoms in the fatty acid, and the fatty acid content in coconut oil.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".