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Record W2060065958 · doi:10.5539/mas.v3n2p117

Aerodynamics Simulation of Hypersonic Waverider Vehicle

2009· article· en· W2060065958 on OpenAlexvenueno aff
Dingyi Wu, Hong Xiao

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNorthwestern Polytechnical UniversityNorthwestern University
KeywordsHypersonic speedAerodynamicsAerospace engineeringRamjetLift coefficientHypersonic flightScramjetLift-to-drag ratioLift (data mining)Computational fluid dynamicsAerodynamic heatingPressure coefficientComputer scienceMechanicsPhysicsEngineeringTurbulenceCombustor

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the aerodynamic characteristics of three hypersonic configurations. Three hypersonic forebodies were designed. For the purpose to integrate with ramjet or scramjet, all forebodies were designed integrated with hypersonic inlet.To better understand the forebody performance, three dimensional flow field calculations of these hypersonic forebodies integrated with hypersonic inlet were conducted in the design and off design conditions. Computational result show that waverider offer an aerodynamic performance advantage in the terms of high lift-drag ratios over the other two configurations. Liftbody offer good aerodynamic performance in subsonic region. The aerodynamic performance of the liftbody integrated with waverider configuration is not comparable to that of pure waverider in the terms of lift-drag ratios and is not comparable to that of pure liftbody in subsonic. But the liftbody integrated with waverider configuration exhibit good lateral-directional and longitudinal-directional stability characteristics. Both pure waverider and liftbody integrated with waverider configuration can provide relatively uniform flow for the inlet and offer good aerodynamic characteristics in the terms of recovery coefficient of total pressure and uniformity coefficient.

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)0.000
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 score0.418
Threshold uncertainty score0.426

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 head0.006
GPT teacher head0.214
Teacher spread0.207 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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