Design of a nanocoated heat exchanger working with organic nanofluids using a hybrid technique
Bibliographic record
Abstract
In this paper, we developed an adaptive neuro-fuzzy inference system (ANFIS) to predict the thermal and hydrodynamic properties of two types of Newtonian nanomodules in the outer layer of shell and tube heat exchanger (STHE). The input data for the ANFIS model were the apparent density of the nanoparticles, the Reynolds number, the thermal conductivity of the nanoparticles, and the brand number. According to a particle swarm optimization (PSO) algorithm, multi-component optimization was performed to reduce the overall pressure, increase the heat transfer coefficient, and increase the number of nanofluid cores in the STHE. During the optimization, the pressure of the nanofluids decreased and the number of noses (tube side) was calculated using the ANFIS model. The best ANFIS was a combination of spatial neural network and phase organization. Despite the stability of the nanofluids, the heat transfer during cooking was significantly reduced owing to its resistance to minerals. The formation and laceration of the nanoparticles was experimentally studied. The comparison of experimental thermal conductivity coefficients between the results of the relationship with the ANFIS shows high efficiency and accuracy of the synthetic neural network provided in thermal conductivity data.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".