Numerical and experimental investigation of heat transfer of ZnO/Water nanofluid in the concentric tube and plate heat exchangers
Bibliographic record
Abstract
The plate and concentric tube heat exchangers are tested by using the water-water and nanofluid-water streams. The ZnO/Water (0.5%v/v) nanofluid has been used as the hot stream. The heat transfer rate omitted of hot stream and overall heat transfer coefficients in both heat exchangers are measured as a function of hot and cold streams mass flow rates. The experimental results show that the heat transfer rate and heat transfer coefficients of the nanofluid in both of the heat exchangers is higher than that of the base liquid (i.e., water) and the efficiency of plate heat exchange is higher than concentric tube heat exchanger. In the plate heat exchanger the heat transfer coefficient of nanofluid at mcold = mhot = 10 gr/sec is about 20% higher than base fluid and under the same conditions in the concentric heat exchanger is 14% higher than base fluid. The heat transfer rate and heat transfer coefficients increases with increase in mass flow rates of hot and cold streams. Also the CFD1 code is used to simulate the performance of the mentioned heat exchangers. The CFD results are compared to the experimental data and showed good agreement. It is shown that the CFD is a reliable tool for investigation of heat transfer of nanofluids in the various heat exchangers.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".