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Record W2044699818 · doi:10.1121/1.1993130

A scattering-chamber approach for solving finite rough surface scattering problems

2005· article· en· W2044699818 on OpenAlexaff
John A. Fawcett

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSuperposition principleScatteringComputationMathematical analysisMathematicsSurface roughnessSurface (topology)Boundary value problemIntegral equationInterval (graph theory)Boundary (topology)Function (biology)Surface finishGeometryPhysicsOpticsAlgorithmMaterials science

Abstract

fetched live from OpenAlex

In this paper a new method is derived for the computation of scattering from a finite, rough free surface. The free surface is infinite in extent but only a portion of it is rough. In order to reduce the amount of numerical computation for such a problem, it is desirable to restrict the computations to the interval of roughness, even for remote sources and receivers. This can be easily done in the case that the rough portion of the surface is only directed into the surrounding fluid medium. In this case, the use of the appropriate half-space Green’s function will restrict the integral equation to the interval of roughness only. However, for general deformations this Green’s function cannot be used. The use of truncated integral equations utilizing the free space Green’s function is discussed. An alternate approach is then described. A system of boundary conditions is derived for a finite curve containing the interval of roughness and a surrounding contour in the fluid half-space. The resulting equations are solved using the method of wave-field superposition. The derived method is also easily generalized to the case that the rough surface under consideration is the upper boundary of a waveguide.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.257
Teacher spread0.226 · 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
GenreMethods

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

Citations13
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207