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Record W2770602270 · doi:10.1108/aeat-05-2016-0072

Computational modeling of the flow in a wind tunnel

2017· article· en· W2770602270 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueAircraft Engineering and Aerospace Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsWind tunnelComputational fluid dynamicsDiscretizationFlow (mathematics)Boundary layerMechanicsAerodynamicsSuctionBoundary value problemFinite volume methodEngineeringWater tunnelStructural engineeringMechanical engineeringAerospace engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to use a computational technique to simulate the flow in a two-dimensional (2D) wind tunnel where the effect of the solid walls facing the model has been addressed using a porous geometry so that interference arriving at the solid walls are duly damped and a flow suction procedure has been adopted at the side wall to minimize the span-wise effect of the growing side wall boundary layer. Design/methodology/approach A CFD procedure based on discretization of the Navier–Stokes equations has been used to model the flow in a rectangular volume with appropriate treatment for solid walls of the confined volume in which the model is placed. The rectangular volume was configured by stacking O-Grid sections in a span-wise direction using geometric growth from the wall. A porous wall condition has been adapted to counter the wall interference signatures and a separate suction procedure has been implemented for reducing the side wall boundary layer effects. Findings It has been shown that through such corrective measures, the flow in a wind tunnel can be adequately simulated using computational modeling. Computed results were compared against experimental measurements obtained from IAR (Institute for Aerospace, Canada) and NAL (National Aeronautical Laboratory, Japan) to show that indeed appropriate corrective means may be adapted to reduce the interference effects. Research limitations/implications The solutions seemed to converge a lot better using relatively coarser grids which placed the shock locations closer to the experimental values. The finer grids were more stiff to converge and resulted in reversed flow with the two equation k-w model in the region where the intention was to draw out the fluid to thin down the boundary layer. The one equation Spalart–Allmaras model gave better result when porosity and wall suction routines were implemented. Practical implications This method could be used by industry to point check the results against certain demanding flow conditions and then used for more routine parametric studies at other conditions. The method would prove to be efficient and economical during early design stages of a configuration. Originality/value The method makes use of an O-grid to represent the confined test section and its dual treatment of wall interference and blockage effects through simultaneous application of porosity and boundary layer suction is believed to be quite original.

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 score0.023
Threshold uncertainty score0.399

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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