Optimizing bio-inspired macro-structures for enhanced thermal efficiency in multi-skin facade buildings
Bibliographic record
Abstract
Bio-mimicry design applies natural structures and efficiencies to engineering solutions, enhancing sustainability in modern building construction. In this research, Infinity Kagome lattice is investigated and compared to the Kagome lattice due to its similarity to silkworm cocoons. Nusselt number together with pressure drop and vortex formation were studied through numerical simulations and experimental tests for the lattice core structures. The results illustrate that Infinity Kagome reaches 27% better average Nusselt numbers than Kagome because its vortex production capability is improved although it maintains twice the pressure loss, which means the geometric transformation reduces access to direct solar energy thus creating superior thermal insulation properties. The heat transfer coefficient reached 22% higher levels as Reynolds numbers increased thus validating superior thermal capabilities. However, the higher flow resistance necessitates optimization for practical implementation. The integration of bio-mimetic principles with periodic cellular materials constitutes the main novelty of this work for thermal regulation enhancement. The validated model incorporated experimental data in addition to simulated results obtained from Computational Fluid Dynamics (CFD) which verified both mesh independence and the accurate performance of the turbulence model. These results lead to energy-efficient facade developments since bio-inspired lattice structures work effectively in sustainable architectural applications.
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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 | 0.000 | 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".