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Record W3036683220

On the pseudo-incident wave technique for interacting inhomogeneities in electromechanical problems

2015· article· en· W3036683220 on OpenAlexfundno aff
Xiaodong Wang, Huangchao Yu, S. Abdel-Gawad, Chen Wang

Bibliographic record

VenueUPCommons institutional repository (Universitat Politècnica de Catalunya) · 2015
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

The current paper presents the Pseudo-Incident Wave method for the theoretical treatment of the dynamic interaction between general inhomogeneities in advanced piezoelectric structures. Instead of simulating the response of such complicated systems using purely numerical or analytical methods, the current technique will take the advantages of the accuracy and reliability of analytical solutions and the flexibility of numerical methods. Using this method the original interaction problem is reduced to the solution of coupled single inhomogeneity problems, for which analytical solutions or simpler numerical solutions could be derived. By considering the consistency condition between different inhomogeneities, the steady state dynamic solution of multiple interaction problems can be formulated in terms of coupled single inhomogeneity solutions. The current method is very general and can provide reliable simulation of complicated interaction problems. Numerical examples are presented to illustrate the effectiveness of the Pseudo-Incident Wave method in simulating dynamic interaction between general inhomegeneities under complicated geometries.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.683

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 head0.026
GPT teacher head0.218
Teacher spread0.192 · 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 designSimulation or modeling
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

Citations1
Published2015
Admission routes1
Has abstractyes

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