Conformally Perforated Shellular Metamaterials with Tunable Thermomechanical and Acoustic Properties
Bibliographic record
Abstract
Abstract The tunability of multifunctional properties of Triply Periodic Minimal Surfaces (TPMSs) is mainly constrained to variations in their shell thickness. To address this limitation, a novel approach inspired by 2D cellular solids is introduced, utilizing discrete conformal mapping of perforated 2D cellular layouts onto TPMS surfaces to minimize distortion during 3D mapping. This study focuses on primitive (P)‐type Conformally Perforated Shellular Metamaterials (CPSMs), evaluating their thermomechanical and acoustic properties through computational analyses and experimentation on 3D‐printed samples. Effective thermomechanical properties are determined via asymptotic homogenization, which demonstrate that the thermal conductivity of CPSMs can be calculated by multiplying the effective thermal conductivities of the 3D shellular and 2D thermally‐isotropic cellular architectures. Thermal conductivity and elastic stiffness are systematically enhanced by engineering the shell's in‐plane architecture while preserving the 3D topology. The optimized design demonstrates a significant increase in elastic stiffness compared to an intact P‐shellular of the same density. Numerical and experimental results reveal that the 2D‐mapped architecture effectively tunes the acoustic bandwidth and bandgap frequency range of CPSMs, achieving a 79% increase in bandwidth and a 34% reduction in material density compared to an intact P‐shellular. These findings highlight CPSMs' potential for structural, thermal, and acoustic applications, advancing the development of additively manufactured TPMS‐based multifunctional metamaterials.
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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".