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Record W2615885550 · doi:10.18260/p.27193

What Flies Like a Dragonfly and Swims Like an Eel? Bio-inspired Design Cornerstone Projects

2016· article· en· W2615885550 on OpenAlexaff
Marjan Eggermont, Denis Onen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Calgary
FundersAmerican Society for Engineering Education
KeywordsBiomimeticsCornerstoneBionicsEngineering design processPropulsionVariety (cybernetics)Presentation (obstetrics)EngineeringProcess (computing)Computer science3D printingSystems engineeringArtificial intelligenceMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Biologically inspired design is a challenging topic to teach, especially to beginning first year engineering students with no background in engineering skills and analysis. We have incorporated bio-inspired design as a project and successfully run a design challenge with first year engineering students, to present an alternative design process than a traditional Problem Definition > Alternative Solutions > Prototyping > Testing methodology. Students were given a biomimicry presentation by company that researches bio-inspired design solutions and given the opportunity to study technical details of biomimetic aircraft (dragonfly and albatross), to see how technology could be mapped to create biomimetic motion. Students were given small, elastic-band powered “flyers,” which they built and tested, to understand how simple mechanisms could be used to create biomimetic motion. Students were then instructed to study biological means of propulsion through water, and to create a water craft that could travel through water. This successful project resulted in many different designs, illustrating a variety of biological solutions. This paper will discuss a bio-inspired design methodology illustrated with student designs and will discuss lessons learned.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.506

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 communication0.0000.002
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 head0.043
GPT teacher head0.262
Teacher spread0.219 · 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 designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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