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Record W2089297419 · doi:10.1145/2503210.2503298

Petascale direct numerical simulation of turbulent channel flow on up to 786K cores

2013· article· en· W2089297419 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersOffice of Advanced CyberinfrastructureUniversity of Illinois at Urbana-ChampaignPetroleum Research Newfoundland and LabradorNational Centre for Supercomputing ApplicationsArgonne National LaboratoryUniversity of Texas at AustinNational Science Foundation
KeywordsTurbulenceDirect numerical simulationPetascale computingReynolds numberComputer scienceFlow (mathematics)MechanicsComputational fluid dynamicsOpen-channel flowFluid dynamicsChannel (broadcasting)Computer simulationStatistical physicsComputational sciencePhysicsSupercomputerTelecommunicationsParallel computing

Abstract

fetched live from OpenAlex

We present results of performance optimization for direct numerical simulation (DNS) of wall bounded turbulent flow (channel flow). DNS is a technique in which the fluid flow equations are solved without subgrid modeling. Of particular interest are high Reynolds number (Re) turbulent flows over walls, because of their importance in technological applications. Simulating high Re turbulence is a challenging computational problem, due to the high spatial and temporal resolution requirements.

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 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 score0.095
Threshold uncertainty score0.552

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)0.0010.000

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Quick stats

Citations101
Published2013
Admission routes1
Has abstractyes

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