Heat transfer enhancement analysis using TiO2-water nanofluid in shell and tube heat exchanger with doughnut and flower segmental baffles
Bibliographic record
Abstract
Owing to the vital role of heat exchangers in process industries, several researchers have shown interest in enhancing its performance.In recent decades, Nano fluids are emerging to expand the thermal efficiency of heat exchangers on top of base fluids.This research focuses on enhancement of the heat transfer of the shell and tube heat exchanger equipped with flower and doughnut baffles using TiO 2 -water nanofluid.The properties of Heat Transfer of TiO 2 -Water Nanofluid was studied with flower and doughnut baffles equipped in shell and tube heat exchanger for various flow conditions and the comparison was made with base fluid.In the shell and tube heat exchanger, water based TiO 2 nanofluid with 0.05%, 0.1%, 0.15% and 0.2% volume fractions were used as working fluids for different flowrates of nanofluids.The temperature of the hot fluid was also varied as 60°C, 70°C and 80°C to study its influence on heat transfer rate.The result shows, the overall heat transfer coefficient increased by enlarging the percentage volume concentration of TiO 2 -water nanofluid and the hot fluid temperature.It is observed that TiO 2 -water nanofluid contributes more than water to the thermal performance of shell and tube heat exchangers using flower and doughnut baffles.
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 | 0.000 | 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 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".