Bibliographic record
Abstract
Abstract Effective heat transfer is crucial for energy conservation, efficiency of industrial processes, and the development of advanced heating and cooling systems. Heat transfer enhancement techniques aim to facilitate faster and more efficient thermal energy transport. The first part of the lecture focuses on the emerging field of nanofluids, which, as a novel class of heat transfer fluids, has gained significant attention in recent years due to their unique properties and potential for improving heat transfer effectiveness. It is presented a brief overview of the experimental methods involved in the preparation of the nanofluids along with their combination with other heat transfer enhancers such as turbulators and vortex generators. Their application to small-scale solar thermal systems is also reported. Current challenges, such as nanoparticle agglomeration and long-term stability, are addressed, while exploring new nanoparticle materials for multi-purpose use. The second part of the lecture stresses the urgent need for further heat transfer enhancement by device development in two specific and very distinct areas: thermal management of battery assemblies in electric vehicles and geothermal energy extraction using super-long gravity heat pipes. The thermal management of battery assemblies in electric vehicles is vital to maintain safe operating temperatures under different weather conditions and prevent thermal runaway. Effective heat transfer within the battery pack is critical for optimizing battery performance, increasing its operational lifespan, and ensuring safety. A particular CFD study is reported along its main conclusions. In what concerns geothermal energy extraction, the super-long gravity heat pipe, with lengths over 3,000 meters, offer unique promise. Data from field tests for a prototype are reported along with preliminary conclusions. They are also noted several challenges, which include, among others, design of the heat pipe internals, reduction of the thermal resistance between pipe and surrounding ground, insulation of the adiabatic region, and reservoir recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".