Robust lightweight multifunctional thermally tailored lattices
Bibliographic record
Abstract
Abstract This paper presents conceptual designs for lightweight lattices that are able to connect two materials with differing coefficients of thermal expansion without generating thermal expansion mismatch stresses during temperature excursions. The lattices operate passively at ambient conditions to accommodate large variations of temperature and to provide constant separation, independent of temperature, between the substrate materials. When pin-connected, the lattices are free from thermal mismatch stresses both internally and at their connections with the substrates. However, previous configurations of these lattices are highly sensitive to small perturbations in geometry, as would be associated with thermal expansion or manufacturing imperfections, and hence must be designed giving consideration to such phenomena. Hence, sensitivity analysis must be a factor in the design process. To show that such sensitivity analyses can be carried out using theoretical approaches, experimental results are presented that are in accord with the theoretical calculations. Several examples are given to demonstrate strategies for reducing lattice sensitivity to geometric imperfections for different combinations of material and geometry. More importantly, novel alternative designs for lattices using simpler configurations, which are less sensitive to small perturbations, are explained. These alternative lattices employ less complicated geometry and, in some cases, require only one material, but offer less adaptibility in the situations to which they are applicable.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".