Numerical simulation of airflow within a duct with sickle‐shaped barriers in tube heat exchangers
Bibliographic record
Abstract
Abstract This study introduces a novel thermal enhancement strategy for solar air channels through the integration of sickle‐shaped baffles, a geometry not previously explored in this context. These uniquely curved baffles are designed to optimize heat transfer by enhancing flow disruption and promoting forced convection. Unlike traditional rectangular or triangular obstacles, the sickle‐shaped design aims to strike a balance between thermal performance and pressure loss. To analyze the flow and heat transfer behaviour, the Navier–Stokes equations coupled with the k‐ε turbulence model are solved using the finite volume method in ANSYS‐FLUENT 2023 R1. The numerical results demonstrate that increasing the baffle height significantly enhances heat transfer. At a Reynolds number of 10,000, raising the baffle height from 40 to 80 mm resulted in a 41.65% increase in Nusselt number compared to a smooth channel. At Re = 87,300, enhancements reached 49.34% and 134%, respectively. The maximum thermo‐hydraulic performance factor of 1.74 was achieved for σ1 at Re = 10,000, highlighting the efficiency of the design. This work differs from previous studies by focusing on the impact of curved baffle geometry on both thermal and hydrodynamic characteristics. While the baffles effectively enhanced heat transfer, they also introduced a moderate pressure drop, a common trade‐off in passive enhancement techniques. Furthermore, the turbulence analysis revealed a substantial rise in turbulent kinetic energy with increasing Reynolds number, confirming the robustness of the proposed configuration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".