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Record W2239595556 · doi:10.1115/smasis2015-8855

Thermal Actuation Through Bimaterial Lattices

2015· article· en· W2239595556 on OpenAlexaff
Marina M. Toropova, Craig A. Steeves

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActuatorLattice (music)Thermal expansionSkewNonlinear systemPlanarAnisotropyThermalMaterials scienceComputer sciencePhysicsComposite materialOptics

Abstract

fetched live from OpenAlex

The goal of this study is to examine the theoretical capability of bimaterial lattices as thermally driven actuators. The lattices are composed of planar non-identical cells. Each cell consists of a skewed hexagon surrounding an irregular triangle; the skew angles of the hexagon and the ratio of the coefficients of thermal expansion (CTEs) of the two component materials determine the overall performance of the actuator. Such a cell has three tailorable CTEs along the lines connecting the points where adjoining cells are connected. Each individual cell and a lattice consisting of such cells can be strongly anisotropic in terms of thermal expansion. While these lattice cells have been used as stress-free connectors for components with differing CTEs, they have not been explored for their actuation capacity. This paper develops models for bimaterial lattices that can be used as mechanical actuators for valves, switches and differential motion. A general procedure for lattice design includes drawing of its skeleton, which identifies the points at which a lattice cell is connected to other cells or substrates; calculation of three CTEs in each cell depending upon the functionality desired; choosing lattice materials; and finding of the skew angles for each cell as solutions of three nonlinear algebraic equations. By changing materials and geometry, we can determine the change of their configuration when the temperature changes. This paper illustrates the concepts with several examples: a two-cell lattice that is connected to a substrate that functions as a lever in a switch; a three-cell lattice that serves as a valve; and a lattice that controls the maximum total deflection of two adjoining parts of a structure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.252
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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