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Record W3176902750 · doi:10.1088/1361-665x/ac0f44

Numerical ultrasonic full waveform inversion (FWI) for complex structures in coupled 2D solid/fluid media

2021· article· en· W3176902750 on OpenAlex
Jiaze He, Jing Rao, Jacob D. Fleming, Hom Nath Gharti, Luan Nguyen, Gaines Morrison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials and Structures · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsQueen's University
FundersUniversity of Alabama
KeywordsInversion (geology)Ultrasonic sensorWaveformGeologyAcousticsMaterials scienceComputer sciencePhysicsSeismologyTelecommunications

Abstract

fetched live from OpenAlex

Abstract Undetected inclusions in engineering components cause tremendous industrial expenses in maintenance and repairs each year, with additional risks of catastrophic failures. This paper introduces a powerful method for inclusion imaging and reconstruction in irregularly-shaped components, based on a cutting-edge imaging technique—full waveform inversion (FWI). We propose an ultrasonic scanning setup for nondestructive evaluation (NDE) that fits a variety of components with different shapes and sizes. The FWI theoretical expressions are summarized, aiming for creating clear explanations for the NDE and material characterization communities. Systematic analysis of the FWI performance using different setups has been conducted, and a variety of case studies show different aspects of complexity that the FWI technique can address. Multiple inclusions have been successfully reconstructed in gears, exhibiting the potential of applying the proposed technique in overcoming various NDE challenges related to the rapidly growing structural and material complexity nowadays.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.997

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)0.0040.000

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 head0.018
GPT teacher head0.236
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