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Record W2892623037 · doi:10.1121/2.0000851

A numerical model for the nonlinear interaction of elastic waves with cracks

2018· article· en· W2892623037 on OpenAlexaff
Herurisa Rusmanugroho, Alison Malcolm, Meghdad Darijani

Bibliographic record

VenueProceedings of meetings on acoustics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNonlinear systemIsotropyNonlinear acousticsPhysicsMechanicsTensor (intrinsic definition)Mathematical analysisAnisotropyCauchy stress tensorClassical mechanicsMathematicsGeometryOptics

Abstract

fetched live from OpenAlex

It is reasonably well accepted that cracks play a significant role in the nonlinear interactions of elastic waves, but the precise mechanism of why and how this works is less clear. Here, we simulate wave propagation to understand these mechanisms. Following existing techniques, we formulate the stress in terms of its linear and nonlinear contributions. The linear stress is the generalized Hooke’s law involving only the fourth-rank elastic stiffness tensor. The nonlinear stress comes from the product of the fourth- and sixth-rank tensors, and the spatial derivatives of the displacement vector. In a nonlinear isotropic medium, we show that the speeds of P- and S-waves generated by a time-harmonic source-function are not constant over time. In an anisotropic medium, P-wave speed is commonly estimated using effective medium theory. In the linear slip theory, we represent a crack by a displacement discontinuity embedded in an isotropic background. In a cracked medium, the estimated wave speeds show nonlinear behaviors similar to the ones estimated using the direct nonlinear approach. When the particle motion is parallel to the normal of the crack, the variation in P-wave speed is large, indicating that the crack is clapping (opening and closing).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.262

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 head0.016
GPT teacher head0.239
Teacher spread0.223 · 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
Published2018
Admission routes1
Has abstractyes

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