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Record W2484286701 · doi:10.1017/s0001924000005194

Experimental and numerical aerodynamic analysis of a satellite launch vehicle with strap-on boosters

2004· article· en· W2484286701 on OpenAlexfundno aff
Mahmoud Mani, Arash Naghib-Lahouti, Mehdi Nazarinia

Bibliographic record

VenueThe Aeronautical Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsAerodynamicsComputational fluid dynamicsInviscid flowMach numberAerospace engineeringAerodynamic forceFlow (mathematics)SIMPLE algorithmAerodynamic dragDragMechanicsComputer sciencePhysicsEngineeringComputational physics

Abstract

fetched live from OpenAlex

Abstract Results of numerical simulation of inviscid compressible flow around a generic satellite launch vehicle (SLV) with strap-on boosters using a commercial computational fluid dynamics (CFD) code named Star-CD are experimentally evaluated. Governing equations of flow around the SLV with two and two strap-on boosters were solved in three dimensions using the SIMPLE algorithm in an unstructured tetrahedral mesh, to determine longitudinal aerodynamic coefficients and surface pressure distribution at Mach numbers from 0·6 to 2·0, and angles-of-attack from 0° to 16°. To evaluate the numerical results, 1:100th scale models of the SLV were tested in a trisonic wind tunnel in the same configurations and flow conditions as those analysed numerically. Comparison of results shows reasonable agreement between numerical and experimental values, however, drag coefficients had to be corrected to compensate the effects of base flow and the struts connecting the boosters to the core rocket. This evaluation shows that a relatively simple simulation of flow using a commercial CFD code can be considered an efficient tool for prediction of aerodynamic characteristics of a multi body satellite launch vehicle with a level of accuracy acceptable in the process of preliminary aerodynamic design.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.295

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 head0.215
Teacher spread0.208 · 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 designBench or experimental
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

Citations3
Published2004
Admission routes1
Has abstractyes

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