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Record W4319603149 · doi:10.37934/aram.101.1.118

Numerical Simulation of Surface Pressure of a Wedge at Supersonic Mach Numbers and Application of Design of Experiments

2023· article· en· W4319603149 on OpenAlexaff
Shamitha, Asha Crasta, Khizar Ahmed Pathan, Sher Afghan Khan

Bibliographic record

VenueJournal of Advanced Research in Applied Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsTrinity College
Fundersnot available
KeywordsMach numberWedge (geometry)Supersonic speedMach waveMechanicsMach reflectionAngle of attackPhysicsAerodynamicsMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper aims to numerically simulate the flow field for wedge, cone, and ogives. Usually, wedge shapes are used as a stabilizing surface for supersonic projectiles like rockets, missiles, and planes in defense applications. Wedge-shaped or delta wings are often the principal wing used for the stability of supersonic aircraft/missiles. The main goal of the current study is to estimate the pressure at the wedge-shaped plane's/missiles nose at different Mach numbers and incidence angles. Analytical pressure distribution is determined using the strip and piston theory. Later the outcomes from the numerical simulations are compared with the results obtained analytically. The analytical and CFD findings matching is very good. The findings demonstrate that the Mach number and wedge angle are the variables that influence the deviation of static pressure. The static pressure on the surface of the wedge grows with the rise in the semi-vertex angle of the wedge angle and the Mach number. This increase in the surface pressure ratio is linear for the increase in Mach number as well as the semi-vertex angle of the wedge. However, the magnitude of increase with the Mach numbers is not the same as what it was for low Mach numbers. The enhancement in the pressure decreases with the Mach numbers.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
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
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.336
Teacher spread0.303 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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